Online discussion platforms facilitate remote discussions between users. This paper explores the impact of consensus on engagement and proposes algorithms to optimize rankings. Consensus is identified as a crucial engagement driver, and our proposed algorithm outperformed current approaches in an experiment. Promoting debate over echo chambers, consensus is essential for user engagement and platform design.

Retailers use our framework to leverage category sales data for individual product demand prediction. Integrating category-product information, we exploit risk pooling through transfer learning. Our approach combines data from different levels, treating top-level sales as regularization. It outperforms JD.com benchmarks by over 9%, highlighting the value of transfer learning in demand prediction for cost savings in low-margin e-retail.

Human donor milk is vital for preterm infants, but its macronutrient content varies, necessitating pooling. To address resource limitations in milk banks, we propose a data-driven framework using machine learning and optimization. Collaborating with a milk bank, we collect data, fine-tune models, and simulate operational scenarios. Our approach improves macronutrient target achievement by 31-76% and reduces recipe creation time by 67% compared to baselines.

In repeated games, strategies are often evaluated by their ability to guarantee the performance of the single best action that is selected in hindsight. Yet, the efficacy of the single best action as a benchmark is limited, as static actions may perform poorly in common dynamic settings. We propose the notion of dynamic benchmark (DB) consistency and we characterize the possible empirical joint distributions of play that may emerge when all players are relying on DB consistent strategies.

TBD

Split liver transplantation (SLT) can potentially save two lives using one liver. To facilitate increased SLT usage, we formulate a multi-queue fluid model, incorporating size matching specifics, dynamic health conditions, transplant type, and fairness. We find the optimal organ allocation policy, and evaluate its performance versus other common allocations.

We study the deceased-donor liver allocation policies in the United States. In the transplant community, broader organ sharing is believed to mitigate geographic inequity in organ access, and recent policies are moving in that direction in principle. The key message to policymakers is that they should move away from the `one-size-fits-all' approach and focus on matching supply and demand to develop organ allocation policies that score well in terms of efficiency and geographic equity.

Problem: We enhance a hospital's abdominal surgery efficiency using patient-specific information. Our framework addresses heterogeneous surgical volume effects and generates patient-specific data. Regression models, causal forest, and optimization reveal significant effects and reduce surgery duration by 3-18%. This improves efficiency by matching patients to surgeons based on specific volume effects.

The capacity market is a marketplace for trading generation capacity, and is viewed by its proponents as a mechanism to ensure power system reliability. Based on practice at NYISO, we propose optimization models for capacity markets and analyze outcomes. We find that with capacity markets, more generators are profitable, especially the ones with a lower net cost of new entry. Also, it is possible for a generator to earn more by exercising market power. We conduct case studies on NYISO dataset.

The seller wants to sell an item to n i.i.d. buyers and only the support [a,b] are known; a/b quantifies relative support information (RSI). The seller either minimizes worst-case regret or maximizes worst-case approximation ratio. We show that i) with low RSI, second-price auctions (SPA) is optimal; ii) with high RSI, SPA is not optimal, and we introduce a new mechanism, the "pooling auction" (POOL), which is optimal; iii) with moderate RSI, a combination of SPA and POOL is optimal.

We study resale platforms, a growing type of online marketplaces in developing countries. These platforms allow their users to earn profits by selling products to their contacts, who do not typically shop online. We analyze data from a major resale platform in India and leverage our empirical findings to develop a model of the platform. We provide insights into key design aspects of the platform, such as the structure that determines resellers’ margins and the product ranking algorithm.

The sharing economy business model has recently received much attention to determine whether it is economically viable and environmentally friendly. The trade-off is between a decrease in production volume and an increase in product usage. We investigate both economic and environmental impacts of the sharing economy business model for comparison with the traditional models of pure sales and servicizing. Sharing economy can yield a win-win outcome as to profitability and environmental impact.

Our paper investigates the effectiveness of IoT-based nudging on energy savings. The results of our study highlight the value of using IoT technology to influence energy consumption behavior and reduce energy usage.

Drug-drug interactions (DDI) are common medical errors due to the negative interaction of medicines taken by a patient. up to 96% instances, doctors ignore the even the mandatory DDI alerts, leading to adverse reactions and poor health outcomes. To solve this issue, we collaborated with a large health-tech firm in India, which conducted a field experiment to identify the impact of real-time information nudges on DDIs. We find that nudges not only reduce DDIs by 8.7% but also promotes learning.

Due to social distancing requirements during the Covid-19 pandemic, the capacity of elevators in high-rise buildings has been reduced by 50-70%. This reduction in capacity has led to queue build-ups, making it difficult to maintain social distancing in the lobby and increasing the risk of disease transmission.

The objective of this work is to minimize elevator wait times in a multi-clinic facility by optimizing the clinic scheduling.

Should firms differentiate quality to soften competition, or equalize it by sourcing? We study sourcing by inefficient firm 2 from efficient firm 1. We show firm 2 prefers sourcing, if it well improves quality. Otherwise no sourcing even for free. Firm 1 averts sourcing if it has large quality leadership under no-sourcing and low horizontal differentiation. While sourcing pools resource to firm 1, it may yield lower quality than no-sourcing for consumers if firm 1 has small efficiency advantage.

The growing complexity of consulting, new product development, and information technology projects has led firms to increasingly adopt the strategy of collaborative value cocreation with their vendors. Research in this field overlooks value cocreation that often involves a client firm engaging with more than one vendor. We bridge this gap by studying a cocreation environment involving a client, a primary vendor, and a potential secondary vendor.

Firms rely on suppliers for innovation, often through procurement or innovation contests. Supplier ownership and control impact contest outcomes. We analyze the effects on buyer's profits and product innovation, identifying when they align or diverge. Our findings inform optimal supplier base structure and explain recent industry developments. Highly profitable structures may hinder innovation, prompting firms to prioritize long-term innovativeness over short-term profits.

Optimal stopping is the problem of determining when to stop a stochastic system in order to maximize reward. We propose a methodology for optimal stopping based on randomized linear policies, which choose to stop with a probability that is determined by a weighted sum of basis functions. We develop a practical heuristic for solving our randomized policy problem, and numerically show that our approach can substantially outperform state-of-the-art methods.

​​​​A multinational risk averse newsvendor produces goods at home (domestically) and sells both overseas and at home, over multiple periods. ​​We consider ​risks due to uncertain exchange rate as well as uncertain demand and investigate the effectiveness of ​(a) general financial hedging contracts​ and (b) operational hedging, which is to allow production both domestically and overseas. ​W​e evaluate both types of hedging and describe the situations that favor each type of hedging.

Firms can adjust operations based on financial asset price impact on product demand. We develop a model integrating financial risk hedging into pricing decisions. Hedging generally lowers optimal price and service level. The impact of asset price on demand determines the effect on service level. Our model reduces risk without significantly reducing operational profit. Including operational payoff functions reveals when hedging reduces optimal operational levels.

Consider an omni-channel retail system that consists of an online sales website and a physical store. This study examines the assortment planning problem in the physical store under decentralized and centralized decision structures, while also allowing for product returns. We use combinatorial optimization and game theory techniques to find the optimal assortments. Moreover, we propose economies/diseconomies of scope (EOS/DEOS) contracts that are capable of fully coordinating the retail system.

Problem: Separate product ranking and inventory ordering decisions in e-commerce lead to inefficiencies. We propose a joint approach to optimize them. Methodology/Results: Our algorithms improve profitability by coordinating inventory and ranking decisions. They provide practical solutions for a large number of products. Managerial Implications: Coordinated decisions boost profits in e-commerce.

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We present a collaboration with the OCP Group, one of the world's largest producers of phosphate, in support of a green initiative to reduce OCP's carbon emissions significantly. We study the problem of decarbonizing OCP’s electricity supply by installing a mixture of solar panels and batteries to minimize its investment cost plus the cost of satisfying its remaining demand via the Moroccan national grid. This forms the basis for a one billion USD investment in renewable energy generation.

Problem: Evaluating an ED delay announcement system in Hong Kong's healthcare. Methodology: Studying 1.3M patient visits, we estimate patient sensitivity to announced waiting times (WT) and factors influencing it. Results show potential improvements in reducing WT and patients leaving without being seen. Implications: Increase patient awareness, reduce WT update window, focus on older population and Kowloon district for promotion.

Online agri-platforms, like Karnataka's United Market Platform (UMP), aim to improve smallholder farmers' welfare. This study designs a two-stage auction on the UMP, increasing farmers' revenue with over $6 million in commodity trades. Results show a 3.6% price increase (55%-94% profit gain), benefiting 10,000+ farmers. Lessons highlight innovative price discovery in resource-constrained environments, emphasizing operational and behavioral factors for success.

We present a framework for optimizing the selection of which content to promote on digital distribution channels, consisting of: (1) A model of the decision-problem, (2) An off-policy identification and evaluation method, (3) the design of ranking policies, and (4) a causal machine learning procedure. We partner with an international newspaper and show that our optimal policy improves the newspapers outcome by 18 percent. Our work contributes by supporting the curation of digital channels.

Tree-based models have interpretability but are not able to capture complex relationships while other ML models often lack interpretability. We propose neural-informed decision trees (NIDTs) to combine predictive power with interpretability. We evaluate NIDTs on over 20 UCI datasets and show they outperform multiple ML benchmarks. We demonstrate interpretability by extracting an explainable warfarin prescription policy and show how they can be used on a pricing problem.

Platforms run many A/B tests, but testing all combinations is impractical. DeDL, our debiased deep learning framework, estimates causal effects and identifies optimal treatments. It combines deep learning with double machine learning, yielding consistent estimators. Results on a video-sharing platform show accurate estimation and efficient iteration. DeDL enables platforms to iterate operations effectively.

Physicians spend more than 5 hours a day on EHR and 1 hour after work, causing burnout, attrition, and appointment delays. This paper examines the impact of workflow decisions on EHR time. Using 150,000 appointments from 74 family medicine physicians, we find pre-appointment EHR reduces workload and after-work hours, while post-appointment EHR decreases after-work hours but increases total EHR time. Our findings help healthcare administrators design EHR workflows to reduce physician burnout.

We study how task switching in emergency departments (ED) impacts physician efficiency, quality of care, and patient routing. We find task switching hurts physician productivity, while its influence on quality is insignificant. ED physicians are switch-averse when routing patients. Leveraging the heterogeneity among task switches, we propose an implementable data-driven queue management method to partition patients into two queues. The simulation shows our method effectively improves efficiency.

This study investigates the impact of telehealth expansion on rural-urban healthcare access disparities during COVID-19. The findings reveal an increased gap between rural and urban areas, with telehealth contributing to a 3.9% rise in the disparity. Urban patients adopt telehealth more, while rural patients rely on in-person visits. The research highlights the need to address barriers and promote equitable access to remote care, informing policymakers, healthcare providers, and researchers.

In traditional assortment optimization, profits may be improved by making some products unavailable as part of the lost demand flows to higher margin products. The refined assortment optimization problem takes a subtler approach to improving profits without necessarily making some unavailable. This is done by judiciously making some products less appealing thereby steering demand to more profitable products. We develop a theoretical framework and provide tight revenue bounds.

We study efficient real-time data collection approaches for an online retailer that dynamically personalizes the assortment offerings based on customers’ attributes to learn their preferences and maximize revenue. We study the structure of efficient exploration in this setting, prove a performance lower bound, and propose efficient learning policies. We illustrate the superior performance of our policies over existing benchmark using a dataset from a large Chilean retailer.

We investigate assortment problem under distributionally robust and robust satisficing formulations. We generalize the optimality of revenue-ordered set for both distributionally robust assortment and robust assortment satisficing problems. We further discuss the choice model based on worst-case distribution and derive managerial insights by theoretical analysis. Moreover, we discuss the two robust formulations of assortment problem with cardinality constraint and develop efficient algorithms.

Crowdsourced delivery platforms offer workers complete flexibility in scheduling their own hours. However, since workers are treated as independent contractors, they do not receive minimum wage protection. Here we examine the integration of driver compensation guarantees in a platform's matching decisions. We design dynamic matching policies that guarantee a particular level of utilization or wage for active workers, while maintaining the inherent work hour flexibility of the sharing economy.

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In team settings, it is critical to mitigate moral hazard, and induce reporting of any shirking. However, agents can free-ride, which exacerbates moral hazard; and deep-rooted loyalty considerations prevent peer reporting. We study peer reporting in a team of one principal and two agents. One of the agents may be loyal: doesn’t shirk and doesn’t report shirking. We show that even a small probability of such a loyal agent unravels reporting.

The flow of humanitarian aid as a response to the Russian Federation’s full-scale invasion of Ukraine was unprecedented. In this paper, we analyze the pressing issues of how to regulate the delivery of humanitarian aid to the final beneficiaries, preventing its loss and misuse due to corrupt behavior in the delivery process.

Girl Scouts' Cookie Program aims to develop girls' skills, but retaining their engagement is crucial. We analyze troop-based factors influencing engagement retention.

Small troop size, balanced sales performance, high adult-to-girl ratio, scout level heterogeneity, and troop-based booth sales positively impact retention.

NPOs can enhance engagement by focusing on team composition, relationship-building, and understanding the factors driving retention in volunteer and fundraising activities.

Research suggests that the regulatory inspection outcomes are influenced by factors unrelated to non-conformance. One of the reasons is shift in the U.S. political geography every two years which exposes firms to differential policy risks. We use political alignment to empirically uncover the impact of time-varying policy risks on quality performance (recalls) of food operations via regulatory inspection outcomes. We identify firm-level strategies to mitigate it and make policy recommendations.

In a contest, participants exert effort to win the a prize. The cost of their effort is irreversible. Most of the research on contests has treated a contest as an isolated event. However, in many real-life situations, we choose among several contests that are going on in parallel. We combine theoretical and empirical question to address the following questions: How does an individual choose among contests and how does the existence of multiple alternative contests affect her behavior?

New apparel brands have emerged that offer sustainable fashion at affordable prices. Designing an effective CSR disclosure strategy for affordable apparel presents new challenges, as the literature has shown that consumers often equate low prices with low quality. We use an experiment in an online purchase context to examine the mechanisms behind consumers' valuations of such CSR disclosures and how price-related factors (retail price, historical price paid) moderate these indirect effects.

We study a new business practice of “crowdsourcing buying” wherein a retailer first seeks input from customers on the desirability of a product and then bases their purchasing decision on their votes. We collaborate with an apparel rental platform and analyze a dataset comprised of the platform’s products and users. We find that the initiation of the service improves both conversion rate and the number of turns. We also investigate several potential mechanisms that drive this improvement.

We show that WCs can improve their resilience to digital competition by making strategic retail location decisions. We find that ecommerce intensity in a geographic market negatively impacts Costco foot traffic in the same market. However, the location matters. Specifically, locating WCs in a “low agglomeration” area, distant from competing WCs and grocery stores, not only contributes to increased foot traffic but also has the potential to offset the negative impact from ecommerce competition.

Loot boxes are a primary source of revenue in video game industry. Loot boxes randomly drop items of differing value. To design a loot box, sellers choose the purchase price and drop rates. We show that, in general, the loot box design problem is NP-hard. By restricting the form of player utilities, we can solve the problem exactly in polynomial time when the number of items is fixed. Under different restrictions, we solve the problem approximately in polynomial time with fixed precision.

The degree of modularity embodied by a system overall architecture has clearly received quite some attention in the product/service literature but there has been much less theory established to specifically guide the incentive design for the development of a modular system within an organization. We seek to understand how the product architecture and the sources of uncertainty that affect the value interdependences of such a system, influence the optimal design of individual and team incentives.

In innovation contests, organizers seek solutions from solvers individually or as teams. High-novelty, nondecomposable problems benefit organizers with team submissions despite lower solver efforts. For low-novelty, nondecomposable problems, teams may not benefit organizers. Decomposable problems have specific conditions for organizer benefits. Contests favor team collaboration over in-house innovation. Solvers benefit from teams when novelty is moderate.

We present ways to improve weather-based agricultural insurance for smallholder farmers in the developing world, where post-season inspection of crop outcomes is not feasible. We seek policies that better align insurance payments with adverse weather-related yield reductions to help stabilize the farmer’s income over time. To illustrate the ideas and results, we utilize parameters for crops that are commonly grown in India.

Health on Loan: The Effect of Local Credit Availability on Hospital (Re)Admissions

Some life-improving technologies for the poor are unaffordable to them, as their limited liquidity puts the purchase costs out of reach. Thus, business models have emerged where the consumers pay a fraction of the price upfront to acquire the technology and make a series of payments for continued access. Using the optimal contracting approach, we investigate payment mechanisms that balance flexibility, discipline, and ownership incentives. Several implementable features emerge from our analysis.

The current business practice and existing academic literature estimate customers’ lifetime value (CLV) for the company, considering customers’ frequency, recency, and monetary value (RFM). In this paper, we uncover the limitations of this approach and propose the Operational Customer Lifetime Value (OCLV) as a more comprehensive way to think of the long-term value of a customer for the company.

In this paper, we study a retailer's returnless refund (RR) policy, under which the retailer may issue a refund without asking the buyer to return the product. We develop parsimonious models to formulate the problem. We show that an early commitment to RR improves the retailer's profit, but only when the net salvage value is small or moderate. We also consider customers who may abuse this policy and show that surprisingly, their existence may increase profit.

Solar power integration into the grid is important for combating climate change, but variability in solar irradiance poses a challenge. We introduce CrossViViT, a deep learning model that utilizes satellite data to accurately forecast Global Horizontal Irradiance (GHI) for the next day. Our model also provides prediction intervals for each time-step, indicating the level of forecasting uncertainty. CrossViViT performs well in solar irradiance forecasting, even in unobserved solar stations.

We present a collaboration with the OCP Group, one of the world's largest producers of phosphate, in support of a green initiative to reduce OCP's carbon emissions significantly. We study the problem of decarbonizing OCP’s electricity supply by installing a mixture of solar panels and batteries to minimize its investment cost plus the cost of satisfying its remaining demand via the Moroccan national grid. This forms the basis for a one billion USD investment in renewable energy generation.

In rural areas of Sub-Saharan Africa, people’s main source of clean drinking water comes from handpumps. However, across SSA, one in four handpumps are broken at any given moment. In this project, we study the maintenance program of three NGOs. We develop a MDP model to study the optimal schedule of mechanics visiting water points under different information availability and logistics structure. The goal is to minimize water point downtime.

Deep learning systems use huge training data sets to meet desired performances, but over/under-collecting training data can incur unnecessary costs and workflow delays. We propose and then solve an optimal data collection problem incorporating performance targets, collection costs, a time horizon, and penalties. Experiments on six deep learning tasks show that we reduce the risks of failing to meet performance targets by over 2x compared to existing estimation-based heuristics.

Learning optimal policies from historical data enables personalization in many applications. Adaptive data collection is becoming more common for allowing to improve inferential efficiency and optimize operational performance, but adaptivity complicates policy learning ex post. Our work complements the literature by learning policies with adaptively collected data. We propose an algorithm with proven finite-sample regret bound, which is minimax optimal and meets our established lower bound.

We study the performance implications of quality and quantity of data in contextual decision-making. We focus on the Newsvendor loss and consider a data-driven model in which outcomes observed in similar contexts have similar distributions. We characterize exactly the worst-case regret of a classical class of kernel policies. Our exact analysis unveils new structural insights on the learning behavior of these policies that cannot be observed through state-of-the-art general purpose bounds.

Current trends suggest telemedicine will continue to play a key role in post-pandemic care delivery. Some empirical studies, however, observed that adopting telemedicine can trigger more demand for in-person visits and overcrowd the clinic. We develop a queueing game model to assess the impact of telemedicine in equilibrium. Analyzing this model allows us to characterize the optimal resource allocation for outpatient clinics and the conditions under which introducing telemedicine is beneficial.

We describe a novel approach to automatically generating data-driven simulation models from event log data by combining process mining, queue mining, and machine learning techniques. The resulting model can be used for mapping and improving the process. We describe a healthcare application of this technique where the focus is on estimating direct and indirect effects of congestion. We also discuss how to overcome a challenge posed by very scarce event log.

Problem: Impact of telehealth on patient behaviors (no-shows, unpunctuality) unclear. Analysis of 280,067 appointments shows telehealth reduces no-shows by 4.0% and late arrivals by 10.2%. Adherence improves for follow-up patients (convenience) and new patients (timely access). Improved adherence enhances throughput, efficiency, and access to care. Telehealth implementation justified for increased revenue.

We consider an assortment optimization problem where the customer may purchase multiple products and possibly more than one unit of each product. We adopt the customer consumption model based on the multiple-discrete-choice (MDC) model. We present an algorithmic framework that delivers near-optimal algorithms for different variations of the assortment problem.

Essentially every choice involves an information collection or search phase prior to a decision-making phase. To study how these phases interact, we embed the Exponomial Choice model (Alptekinoglu and Semple 2016) in a classical model of sequential search with perfect recall (Weitzman 1979). We derive the search path and final choice probabilities in closed form and develop all the analytical tools to enable joint optimization of assortment and prices efficiently.

Online discussion platforms facilitate remote discussions between users. This paper explores the impact of consensus on engagement and proposes algorithms to optimize rankings. Consensus is identified as a crucial engagement driver, and our proposed algorithm outperformed current approaches in an experiment. Promoting debate over echo chambers, consensus is essential for user engagement and platform design.

We consider the firm maximizes the total expected revenue over a finite time horizon by optimizing the assortment/pricing of each time period.

Customers make choices under MNL with popularity effect, which also considers the historical sales.

The optimal prices can be solved by concave programming.

The heuristic algorithms we propose for assortment optimization have a 1/T performance ratio in the general case, and the ratio improves to 1/ln(T) when the product's utility is constant over time.

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Managed charging (MH) programs allow utilities to centrally manage electric vehicle (EV) drivers' home charging to reduce cost, avoid new and aggravated peaks and blackouts, and ensure grid stability. In this paper, we present a program design model and a load management model for jointly designing and executing various MH programs.

Do energy price surcharges encourage energy efficiency investments without harming domestic manufacturing?

We analyze a supply chain model and find that energy price surcharges can have varying impacts on domestic sourcing. Supplemental policies like surcharge reduction and EE investment subsidies can help mitigate potential harm.

Policymakers should consider these findings when designing energy policies to promote energy efficiency while minimizing adverse effects on domestic manufacturing.

This study explores multi-agent data-driven decision-making in a supply chain with vertical competition. We find that the sample average approximation and kernel optimization approaches can tacitly collude, reduce double marginalization, improve profits for all participants while protecting consumer welfare. Besides, non-coordinating contracts can even reduce more double marginalization than coordinating ones in the data-driven setting, especially when the feature distribution exhibits skewness.

We propose a novel bimodal inventory optimization (BIO) model and algorithm to position inventory across a retail chain to meet time-varying omnichannel demand. While prior Robust optimization (RO) models emphasize the downside, i.e., worst-case adversarial demand, BIO also considers the upside to remain resilient like RO while also reaping the rewards of potential positive outcomes. Experiments project a profitability gain of 15% for BIO over RO on real-life data from a major retail chain.

The automotive sales has focused on selling an experience or a lifestyle via features. Features are optional add-ons that increase product attractiveness (from stereo audio to cruise control) and basis for purchasing decision. While previous studies conducted car-level analysis on sales, the effect of features has remained unexplored. We examine features' collective impact on sales time along new and used-car segments, propose a two-stage estimation framework, and suggest policy recommendations.

We analyze food purchasing patterns among low-income individuals in India using scanner data. Heavy consumption of packaged junk food is observed. A subsidy program is implemented, resulting in decreased snack purchases and increased purchases of complementary foods. Working parents show the strongest effects. No negative impact on nutrient purchases is found. Customized subsidy programs show a tradeoff between nutrient richness and customer appeal.

We study how payment delays affect project delays. We develop theories linking the two, and empirically test our hypotheses using the QuickPay reform, which expedited payments to certain projects, as an exogenous shock. Surprisingly, faster payments led to greater project delays. We identify contractor liquidity constraints and aggressive bidding as causes for this increase. We also reveal a spillover effect: some projects are accelerated although their payment timing is unaffected by QuickPay.

Valuation of target firms in mergers and acquisitions (M&As) has far-reaching implications for shareholder wealth. This study investigates the effect of a target’s operations capability on the valuation premiums above its market value. We find that target firms’ operations capability has a positive effect on their M&A valuation premiums. However, this effect becomes weaker with increasing market overlap between the target and acquiring firms.

We study dual sourcing under non-stationary demand. The actual demand distribution is not observable, yet demand observations reveal partial information about it. We propose a novel policy that combines a base order on the slow source with flexible orders from the fast and slow source. We formulate the problem as a partially observable Markov decision process and prove the optimal policy structure. Our results show how partial demand information and flexible slow source orders may reduce costs.

We partner with a major online retailer to optimize congestion-aware task assignment in robotic warehousing. We develop an original integer optimization formulation using a time-space network representation of fulfillment center operations. To solve it, we develop a machine-learning-guided large-scale neighborhood search algorithm to iteratively construct and re-optimize small subproblems. We demonstrate the benefits of our model and algorithm as compared to baseline algorithms and heuristics.

We study the structural properties of the optimal ordering policy for perishable products by considering demand forecasts in the inventory model. The optimal ordering policy is a state-dependent base-stock policy that depends on the inventory level, current and previous forecasts, and previous demands. We also propose a linear heuristic that integrates demand forecasts in the ordering policy. Despite the simplicity of the heuristic, its performance is comparable to that of the optimal policy.

We study tactical decision-making for a last-mile e-retail system in which same-day delivery (SDD) and next-day delivery (NDD) orders are fulfilled by the same fleet. We build a continuous approximation model of the system. Unlike prior work using similar methods, we explicitly consider customers' price sensitivity and the relationship between consecutive days in the system. Using this model, we analyze a customer satisfaction objective, then maximize revenue via a differentiated pricing scheme.

Ultrafast grocery deliveries are struggling to become economically sustainable. One way to do so is to optimize the committed delivery time. We show that the optimal delivery radius might be different for different locations. Our results highlight while retailers commit to an identical delivery time, this policy harms their economic sustainability. Our study provides an extensive understanding of the influential factors on profitability and identifies the bottlenecks to improve operations.

E-commerce focuses on improving shipping experience. Pick-up stations boost sales by 3.9%, driven by logistic flexibility, not shipping speed. Consumer choice model shows value in time (76.2%) and choice (23.8%) flexibility. Fewer pick-up stations achieve sales lift. A new strategy without pick-up stations improves sales by 8.4%. Counterfactual logistic strategies increase consumer welfare by 2.0%-10.0%.

Counter-trafficking efforts are often conducted with a local scope. However, sex traffickers commonly move individuals geographically. Thus, there is a need for organizations in different geographical regions to collaborate in counter-trafficking efforts. This research leverages a large dataset of commercial sex ads and techniques from the areas of machine learning and network science to identify prominent geographical circuits and individuals operating on these circuits.

Community corrections provide alternatives for incarcerations, which can reduce jail overcrowding and recidivism rate, particularly for individuals with substance use disorder. We study the placement decisions for community corrections and relevant capacity planning via an MDP model and prove structural properties for policy insights. To address the complex dependence between optimal placement and system congestion, we leverage a two-timescale approach to develop algorithmic solutions.

This paper presents a data-driven approach to mitigate industrial plant air pollution on nearby cities by linking operational decisions with weather conditions. Our method combines predictive and prescriptive machine learning models to forecast short-term wind patterns and recommend production adjustments. Implemented at a chemical plant in Morocco, our algorithm improves weather forecasts by 40-50% and offers valuable trade-offs, reducing emissions by 33% and costs by 63%.

While the cost-saving benefit of generic drugs is obvious, the treatment effectiveness remains unclear. We examine the effect of generic drug usage on health outcomes and address the potential endogeneity concern using instrumental variables and a difference-in-differences framework. We find that generic drug usage leads to higher healthcare service utilization and worse clinical outcomes. Moreover, Our findings highlight the effectiveness heterogeneity of generics from different manufacturers.

Problem: We examine telemedicine's impact on antibiotic prescription errors using patient-provider encounter data. Two-stage least squares regressions show lower overall errors with telemedicine. Effects vary by provider's patient volume and relationship. Reduction in errors mainly stems from type I errors, without compromising patient health outcomes. Telemedicine reduces drug waste and antibiotic resistance, benefiting decision-makers and patients.

The use of telemedicine has grown rapidly. To understand patient behaviors in telemedicine and in-person visits, we studied service incompletion. Physician availability affects in-person visits but not no-shows. Using a multivariate probit model, we found that intra-day delay increases telemedicine service incompletion by 7.40% but has no significant effect on in-person visits. Differentiating incompletions from no-shows is crucial for optimal patient sequencing decisions.

In this work, we study the product replacement problem. This problem arises in the fast fashion industry, where the company's headquarters frequently send new items to stores, expecting store managers to remove current items to make room for the new items and display them. Our study addresses a general case, including misaligned incentives among decision-makers and cross-item effects. We present optimal strategies for the headquarters, but also investigate different store managers' behaviors.

We study the impact of wait time information distortion on customer join decision and the service provider’s revenue in virtual queues. We show that the optimal revenue is first increasing and then decreasing in the distortion level. A zero distortion is optimal only when the service capacity is sufficient and the service value is high. Customer joining rate could be either increasing or decreasing in the distortion level.

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Limited resources in the judicial system can lead to costly delays and even failure to deliver justice. Using the Supreme Court of India as an exemplar for such resource-constrained settings, we apply ideas from service operations to study delay. Court dynamics constitute a case-management queue which is known to be intractable. Hence, we employ data-driven simulations and find that even small interventions can improve the system performance dramatically.

We develop asymptotically optimal policies to track queue lengths under different cost structures in a setting with inaccurate arrival and departure sensor data. We propose an idleness detection policy and explore the value of queue inspections. Our model is motivated by queue tracking implemented at a large airport.

TBD

Efficiently managing inventories requires an accurate estimation of stockout costs. This estimation is complicated by challenges in determining how to compensate consumers monetarily to ensure they will maintain the same level of utility they would have obtained had stockouts not occurred. This paper presents an analysis of these compensation costs, as applied to the design of optimal stockout rates by an online retailer marketing to consumers aesthetically substandard fruits and vegetables.

In this study, we evaluate the effectiveness of AI-based systems in reducing the food waste generated in commercial kitchens. Using a multi-year field experiment in 500+ commercial kitchens, we estimate the waste reductions due to the use of (1) an automated transaction-level scaling system, and (2) an automated weighing and computer vision based waste-classification system. We also explore methods that use the waste data to identify the root causes behind the sub-optimal levels of food waste.

Relay logistics optimize long-haul shipments through a pit-stop network and separate drivers. This model benefits drivers and the environment, while reducing costs and lead times. An efficient algorithm solves the Relay Pickup-and-Delivery Problem, generating multiple arcs for optimal routing. Real-world experiments confirm its superiority over benchmarks, resulting in fewer miles traveled and win-win outcomes for sustainability.

Freight-sharing platforms, where shippers can offer excess transportation capacity at a discounted cost, enable the effective sharing of shipping capacity. This occasional offer of excess transportation capacity introduces new opportunities in inventory management. We study inventory decisions for a company that interacts with a freight-sharing platform. We derive optimal ordering policy characteristics and propose replenishment heuristics.

In this paper, we formulate an optimal information gathering strategy (IGS) to identify which uncertainties in the supply chain network drive our decisions. Existing approaches consider uncertainties, but do not consider the benefit of resolving them. Based on stylized, numerical, and case study results, we show a significant value of optimal IGS. As a non-monotone non-submodular minimization problem, we solve the problem with an algorithm which achieves a constant approximation guarantee.

Leading thinkers in supply chain management have proposed the long-term effects of supply chain disruptions on market share as customers shift their purchases to competitors. Motivated by this insight, we empirically analyze the lasting effects of supply chain disruptions on firms' market shares. Focusing on pharmaceutical supply chain disruptions, we find that products do not fully recover from the market share loss even after they recover from supply chain disruptions.

This paper investigates the impact of advance commitment on supply chain network design under demand uncertainty. The study develops a robust cooperative model and solves it using a column-and-constraint generation algorithm, finding that this approach significantly reduces conservatism and improves performance compared to a non-cooperative model. The research highlights the potential benefits of contractual commitment for companies seeking to enhance their supply chain operations.

This study examines the relationship between the digital divide and online retail sales, encompassing internet infrastructure and socioeconomic inequalities. We demonstrate how retailers can better bridge the divide by leveraging omnichannel and online fulfillment options. Our research emphasizes the significance of incorporating the digital divide into ecommerce fulfillment strategies. We make a contribution to the literature on diversity, equity, and inclusion in operations management.

We propose a data-driven platform that provides traceability to the first mile of agricultural supply chains by coordinating the transactions of farmers and intermediaries. We model unique aspects of the supply chain, including pre-existing informal relationships between farmers and intermediaries, and we develop algorithms to solve real-world instances. We test the results on data from the palm oil supply chain and show the platform’s potential to reduce costs and increase farmers’ welfare.

Interference poses challenges in panel experiments. Aggregating units into clusters is common, but optimal aggregation level is unclear. We propose a randomized design for grid-based units. Our design features randomized spatial clustering and balanced temporal randomization. Theoretical performance, inferential techniques, and simulations validate its superiority.

Challenge: Estimating long-term treatment effects in early-stage experiments is costly. Methodology: We propose a surrogate model using short-term data and historical observations. Results: Verified on WeChat, our method effectively estimates long-term treatment effects. Implications: Our approach reduces experiment duration and provides efficient empirical estimation of long-term effects.

Problem: Content platforms lack effective promotion policies utilizing the diffusion effect. Methodology: We propose a diffusion model, formulate the optimization problem, and introduce D-OLS estimators. Results: We prove submodularity and achieve a 1-1/e-approximation solution. D-OLS estimators are consistent and efficient. Our model improves adoption by 22.48% compared to existing policies. Implications: Our diffusion model enhances content promotion for online platforms.

Innovation projects typically integrate several distinct components which can be completed either iteratively or sequentially. In our experimental study we examine the behavioral underpinnings of iterative vs. sequential workflow in two innovation tasks. Our results show that the iterative workflow significantly outperforms the sequential workflow in a design task, and vice versa in a search task. This cautions against the uniform adoption of iterative workflow methods such as Lean and Agile.

The high patient volumes and acuity levels placed extraordinary stress on the nursing workforce. Increased nursing workload is linked to nurse burnout and patient safety concerns. In this work, we take an empirical approach to understanding the effect of historical workload on nurses’ perceived workload. Quantifying this temporal effect of nursing workload allows us to design patient-to-nurse assignment policies that achieve a more balanced workload and create a fairer and safer working environment.

Can past helping behaviors improve shift scheduling? We study organizational citizenship behavior (OCB) and its impact on shift performance. Past helping predicts patient length of stay better than team familiarity. Small shift composition changes reduce length of stay significantly. Scheduling based on past helping holds promise beyond ICU nursing.

Workers in developing economies may be forced to work excessive overtime, which not only causes severe mental and physical issues to the workers but also results in significant brand damage to the buyers if exposed in public. In this paper, we develop a game-theoretic model of a dyadic supply chain and analyze the buyer’s strategies to combat such excessive overtime issues of the supplier, including auditing the supplier’s practice and conducting supplier development activities.

Problem: Studying disruption impact on centralized and decentralized supply chains. Methodology/Results: Analyzing three-tier supply chain with disruption and network incompleteness. Centralized supply chain maximizes profit. Managerial Implications: Complete network favors centralized supply chain for higher profit and reliability. Incomplete network amplifies disruption's negative impact, reducing profit by up to 20%/30%.

In this paper, we examine the value of vertical integration as a disruption risk mitigation strategy. Vertical integration increases firms' control over their supply chain and could reduce the risk of production being disrupted due to material or component shortages. Using a stylized model of a manufacturer sourcing a critical component from a multi-tier supply chain, we show that vertical integration is most valuable for moderate probability, severe disruptions that are positively correlated.

Companies operating under the gig-worker model are offering full-time jobs to ensure better performance resulting from increased work availability. Using panel data from an online grocery platform offering flexible workhours, we explore if voluntary availability affects performance. We find that voluntary work positively affects productivity and service quality, controlling for worker and task-specific characteristics, and order batching, task complexity and discretion moderate the main effect.

Communication and customer interaction design have been used as elements to improve customer satisfaction and purchasing behavior, but little is known regarding their use as levers to improve operational efficiency. We show the relevance of communication channels as levers of operational performance and provide evidence that these channels can be used to manage customers’ expectations and improve operational performance.

The impact of formal incentives on team productivity is not fully understood and only a handful of field experiments have documented either a small or a null impact. In this study, we empirically explore their effect on individuals and teams. We calibrate a theoretical model to show that the null effect observed in teams is not due to formal incentives losing their effectiveness but because weak formal incentives display larger social incentives than teams with strong formal incentives.

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We consider the dynamic scheduling problem of a classical single-server multi-class queueing system where the system manager does not have full knowledge about the cost/reward of the customers. One of the key results is that the Whittle index policy is optimal for a two-class queue if the system manager knows the distribution of the reward of one class and dynamically learns the distribution parameters of the other class.

Sepsis is a life-threatening response to infection that leads to organ failure, tissue damage, and oftentimes, death. Our work leverages historical health data in order to learn treatment strategies for sepsis that result in improved patient outcomes under limited resources. We learn an interpretable, Markov Decision Process (MDP) model of the system, formulate a robust value iteration algorithm, and solve the problem of limited resource allocation for optimal sepsis treatment.

Problem: ED overcrowding disrupts public safety net. Impact of increasing ED entry fee is unknown.

Methodology/Results: In Hong Kong, increasing the ED fee reduced traffic by 6.3%, targeting less-urgent visits. Frequent visitors decreased their visits, reducing patient abandonment.

Managerial implications: Financial access hurdles alleviate healthcare congestion without discouraging urgent visits. Managing external demand complements internal process improvements in ED management.

In many service systems, service times of customers can be correlated with waiting times. Scheduling under such dependency is challenging as a Markovian state description requires keeping track of all customers' waiting history. We propose an approximate dynamic programming algorithm for multi-class scheduling with wait-dependent service times. Our algorithm can generate policies with simple structures and achieve strong performance which we illustrate in a healthcare setting using real data.

We study stochastic online resource allocation: a decision maker needs to allocate limited resources to i.i.d. sequential requests generated from an unknown distribution in order to maximize reward. Online resource allocation has been studied extensively in the past, but prior results crucially rely on the assumption that the total number of requests (the horizon) is known to the decision maker in advance. In this work, we develop online algorithms that are robust to horizon uncertainty.

We consider an online advance reservation problem under customer heterogeneity. The decision maker decides on admitting a customer based on the customer's class and the current system information. Our problem model captures applications in medical appointment and hotel room booking, where a resources are to be scheduled for customers' uses in advance. We derive a novel rejection price based algorithm, and we quantify the theoretical guarantee of our algorithm in terms of its competitive ratio.

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We introduce the problem of finding a student-optimal stable matching under adaptive priorities, i.e., when priorities depend on the assignment of other agents. We show that the problem is NP-hard, provide math-programming formulations for the problem, and introduce several heuristics to preprocess the instances and solve them. Finally, using both synthetic and real data from Chile, we show that clearinghouses can significantly improve students' welfare when considering dynamic priorities.

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This research optimizes driver shifts and itineraries for paratransit operators, considering uncertainties like cancellations and no-shows. The SIPPAR model, using a shareability network representation and a two-stage stochastic optimization approach, reduces operating costs and improves robustness. The algorithm outperforms benchmarks in real-world instances, providing faster computational times and higher-quality solutions.

By analyzing economic incentives, we show that reducing the number of stop signs at crossroads (e.g., from four to three) leads to a self-enforcing equilibrium with significant benefits. This simpler mechanism saves fuel gas, reduces time and infrastructure costs, lowers carbon emissions, and decreases police expenses. The new approach generates substantial economic gains compared to the traditional practice of erecting one stop sign in each direction.

Competition and market composition impact corporate adoption of green technologies. Cooperative markets with both green and non-green goods see increased green investment with reduced production costs. In competitive markets without green products, cost reductions facilitate green product introduction. Policy-making can leverage these findings for effective green production cost reductions.

We consider market and technological equilibria in Cournot competition with linear and isoelastic demand between firms heterogeneous in operational and environmental efficiency. We examine possibilities and limitations of incentivizing “green” technology choice with environmental/”carbon” taxes. The resultant equilibria and the impact of taxation qualitatively differ with demand function.

We examine whether and how renewable energy (RE) adoption can increase or decrease energy efficiency (EE) improvement. Using site-level data, we estimate the impact of changes in RE usage and in the acquisition approach on the EE. We find that using RE to meet 10% more of a site's energy demand led to an additional 2.0% improvement in EE. While purchasing RE credits or entering into power purchase agreements led to gains in EE, installing on-site RE generators had no effect.

Clean energy technologies require large amounts of critical metals and minerals, the demand for which is skyrocketing as the global energy sector rapidly shifts towards renewable energy. The scarcity of critical materials may prevent governments from reaching their ambitious clean energy targets and jeopardize the profitability of the renewable energy sector. Inspired by this challenge, we investigate mechanisms that can address the impact of scarcity on the renewable energy transition momentum.

The ability to produce electricity when renewables allow and store it for later demand is crucial for emissions reduction. Electric Vehicles (EVs) could provide distributed energy storage to the electric grid through optimal charging and discharging, however, highly complex, non-linear driver behavior must be accounted for. In this work we collaborate with an American EV manufacturer to combine machine learning with optimization to model driver behavior to size the capacity of EV energy storage.

Quantifying the operating cost of a battery has been considered a key challenge for economic profitability in the battery literature/industry. One key modeling difficulty is that the cost is realized through different types of performance degradation. This work takes an analytical approach to compare the optimal dynamic policy under different degradation models (capacity-degradation-only, efficiency-degradation-only, and correlated degradation).

Organizational support improves worker retention. In K-12 education, teacher crowdfunding platforms bridge resource gaps. We analyze teachers' actions on DonorsChoose and employment data. Crowdfunding reduces attrition rates by 1.7 pp, benefiting projects that personalize classrooms and teaching pedagogy. Identity-relevant resources decrease school and system exit by 1.8-2.4 pp. Findings highlight the role of accessible resources in enhancing worker retention.

AVs revolutionize transportation, but cost barriers persist. Open platforms with human drivers aid deployment. Incentive alignment in the supply chain is vital. We analyze the game involving platforms, AV suppliers, and contractors. Underutilization risk affects profit. Contracting solutions align the chain, ensuring half of optimal profit. Misalignment hampers AV adoption. Contracts mitigate efficiency loss.

Algorithms control pricing and match customers and workers in the gig economy. However, algorithms face several critiques: they lack transparency, can be biased, and can be inefficient. We empirically analyze these issues and show that algorithms lose efficiency from two sources: competition between platforms and misaligned worker incentives. We model workers' strategic responses to variation in pricing and estimate counterfactuals on the effects of minimum wage and transparent pricing policies.

Typically, fairness properties of algorithmic decisions are analyzed as if the machine predictions were implemented directly. However, many machine predictions are instead deployed to assist a human decision-maker who retains the ultimate decision authority. In this article, we therefore consider how properties of machine predictions affect the resulting human decisions through both a formal model and lab experiment.

The use of algorithms may be preferred over human judgment; however, algorithms are prone to bias. In this study, we designed a behavioral lab experiment environment to imitate the processes instated by the companies when asking their programmers to develop an ML algorithm for any given task. We investigate the causes of programmers developing biased ML algorithms and study how these causes can be mitigated through policies to encourage the development of fair and less-biased ML algorithms.

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We use a cluster randomized field experiment to study the effect of performance monitoring on the efficiency of public procurement in Chile. In collaboration with the Chilean Public Procurement Office, we provided monthly reports on the purchasing performance of individual procurement officers and services. After 5 months of treatment exposure, we find that the reports generated significant reductions in overspending in interventions that point towards extrinsic motivation of buyers.

Problem: This paper examines the impact of auction timing on bidder behavior and welfare. Methodology/Results: A structural auction model is developed and applied to real data. Complementarities in bids are found to affect contract combinations. An optimization algorithm for auction schedules is proposed. Managerial Implications: Optimizing auction schedules can reduce procurement costs by over 8%.

Presenting online retail customers with individualized substitution suggestions (ISS) when an item is forecasted to be out-of-stock (OOS) is a challenging problem. We investigate how to provide more relevant ISS by employing state-of-the-art choice models. In collaboration with a partner online retail platform, we assess the value of our model by conducting a field experiment. Online retailers can obtain benefits both in revenue and in costs from presenting customers with improved ISS.

Problem: Does selling ugly produce in grocery stores reduce food waste?

Methodology/results: Modeling the supply chain, we find that selling ugly produce reduces waste but lowers retailer profit. Dedicated ugly produce retailers achieve the same waste reduction, explaining the rise of startups. A food landfill tax can significantly reduce waste.

Managerial implications: Our findings support the value of ugly produce startups, informing efforts to combat food waste and hunger.

Problem: How should retailers handle imperfect produce to reduce food waste?

Methodology/results: We analyze discarding, bunching, and differentiating strategies. Increasing acceptance of imperfect produce may not reduce waste. Full-shelf ordering may not increase waste, especially with higher prices. Discarding can decrease waste.

Implications: Retailers should choose strategies wisely, consider full-shelf ordering, and educate consumers on imperfect produce.

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Our work provides new results on the arm-sampling behavior of the celebrated UCB family of multi-armed bandit algorithms, leading to several important insights. Among these, it is shown that arm-sampling rates under UCB are asymptotically deterministic, regardless of the problem complexity. This discovery facilitates new sharp asymptotic characterizations revealing profound distinctions between UCB and Thompson Sampling such as an "incomplete learning" phenomenon characteristic of the latter.

Many pharmaceutical drugs have multiple indications, for which they offer a varying degree of benefit for patients. Yet, in the current US pricing system, the price of the drug is the same regardless of the indication for which it is prescribed. We use a modeling approach to analyze how indication-based pricing compares to uniform pricing for the manufacturer’s profit and investment incentives, the patients’ access to the drug and benefit, and the payer’s coverage incentives and objective.

We propose and analyze a budget-neutral incentive scheme that Organ Procurement Organizations could utilize to increase donor hospital's (DH's) proportion of timely referrals and thereby increase the number of potential donors. We find that, depending on the DH's cost of effort required to increase the proportion of timely referrals, the proposed contract could increase the percentage of family authorizations from a single DH by 1.3%.

Although available evidence suggests that home hemodialysis(HHD) may achieve similar clinical outcomes to in-center hemodialysis and are less resource intensive for patients with end-stage renal disease, they have been used less in the US than in other developed nations. We design two incentive plans in a bi-level game structure consisting of a payer and providers to obtain equilibrium. We show these incentive plans can improve the HHD rate and increase ESRD beneficiaries' quality of life.

Our paper studies how to determine the personalized incentive for each customer to learn her true preference in the assortment optimization and product design problem. We provide algorithms that sequentially decide the personalized incentives based on the evaluated customer's contribution to the revenue increase. We show that compared to the naive supervised learning algorithm that provides fixed incentives, our algorithm can reduce the total cost significantly while achieving high revenue.

We introduce the concept of randomization into the robust assortment optimization liter-ature. We show that the firm can improve its worst-case expected revenues by selecting an assortment randomly according to a prudently designed probability distribution. We demonstrate this potential benefit of randomization both theoretically in an abstract problem formulation and empirically across three choice models: the multinomial logit model, the Markov chain model, and the preference ranking model.

We study dynamic joint assortment and pricing over a finite time period. The goal is to maximize the expected cumulative revenue. We propose a new doubly high-dimensional contextual bandit model to formulate the problem. We developed a computationally tractable algorithm and provide a convergence rate for it. The numerical results of applying our method to a real-world online retail data set demonstrate the efficiency of our method, which is further supported by extensive simulations.

We study a service system where customers with different service demands arrive at a facility with a shared waiting room. We assume two types of customers and two servers. Types differ by their service demand: type 1 seeks service from server 1, and type 2 seeks service from server 2. Customers cannot distinguish between the types, and make their decision whether to join based on the total number of customers in the shared waiting room, without observing the state of the servers.

We study ghost kitchen platforms, which consist of delivery-only restaurants that serve limited number of dishes. We develop a new model of multi-dash queueing system at the platform’s position. In the multi-dash queueing system, an order splits into a random number of sub-orders and is assigned to different home chefs. Our study identifies conditions under which the ghost kitchen platform can be more profitable than traditional food delivery platforms.

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There are more than 1.1 billion people lacking electricity residing in off-grid communities, where extending national electricity grids is infeasible. These communities turn to stand-alone diesel generators for their energy needs, which creates significant economic, operational and environmental challenges. In this study, we identify the optimal capacity investment decisions from the perspective of a remote community and investigate how common policy mechanisms interact with these decisions.

Precision agriculture has been promoted by agricultural technology providers through the use of outcome-based pricing (OBP). In this paper, we examine the effects of OBP on the adoption rate of precision agriculture, and the benefits it offers to both farmers and service providers. We develop a two-period game model that incorporates providers' learning from experience. We also explore the implications of government intervention when the service provider switches to OBP from traditional pricing.

We investigate the effect of upstream supply risk correlation of substitutable items. Suppliers offer menu of price-quantity or wholesale price contracts. Using common agency methodology we characterize the equilibrium contracts. We find that the buyer's profits are increasing in yield correlation under wholesale price contracts but are increasing under price quantity contracts only when product substitutability is low and in fact decrease for high product substitutability.

We consider the problem faced by spare parts inventory that demand intensity for components is unclear at the beginning of a product life cycle. We present a robust optimization (RO) approach in spare parts inventory to against demand uncertainties and design two more time-efficient algorithms capable of finding solutions in case of a large number of items in the model. Our experiments show that the RO model exhibits remarkable efficacy in case of limited information on the demand distribution.

We examine the impact of business disruptions in explaining the gender gap in small firm performance in developing countries. We find that business disruptions significantly increase gender gap in firm performance – on average, business disruptions decrease sales and sales growth of a small women-led firm by 11.6% and 15.2 percentage points, respectively, compared to their male counterpart. Importantly, building resilience helps small women-led firms to close this performance gap.

Strategic inventory refers to the inventory held by firms purely out of strategic considerations other than operational reasons (e.g., economies of scale). In this paper, we investigate the roles of strategic inventory in a system with two parallel supply chains under both full bargaining and partial bargaining, which differ in whether inventory is included in the bargaining terms.

This study proposes a markdown strategy to optimize joint replenishment and pricing decisions in a dynamically changing fresh/non-fresh inventory assortment with stochastic lifetimes, lead times, and demands. We model the problem as a generally modified (r, Q) policy and reformulate that as a MIP model to solve the model exactly. We propose an EOQ approximation and provide some bounds on the optimality gap with respect to market demand, maximum WTP, and lifetime that vanishes asymptotically.

Platforms are able to gather large quantities of data, which can result in high precision predictions regarding consumers purchasing patterns. A fundamental question is whether a platform has the incentives and ability to share such non-verifiable information with its sellers. We demonstrate that cheap-talk information can be exchanged when the platform shares with its sellers region-forecast. In this equilibrium, the platform shares only a region that contains its private information.

Firms rely on expert teams for decision making but face privacy concerns. We propose a mechanism to protect experts' privacy and analyze incentives. The firm garbles experts' reports to address privacy concerns, encouraging truthful reporting. Larger teams may perform worse, and more capable experts may be detrimental to team performance under privacy-conscious conditions.

Tele-driving lets drivers operate vehicles remotely, offering a viable alternative to fully automated ones. It reduces spatial mismatch in ride-hailing, enabling any driver for any customer. We quantify gains by comparing traditional systems to tele-driving. Findings: 1) For impatient customers, optimized driver capacity enhances service or reduces drivers; 2) For patient customers, tele-driving stabilizes systems or cuts drivers with similar service quality.

This paper investigates the undesirable customer behavior of opportunistic picking for products with longer expiration dates in grocery retail stores. We find that on average 20% of food waste at the retail stage is caused by consumer picking. Further, we reveal store and product-related determinants of picking waste and derive insights for store operations decisions. Our paper is the first to empirically quantify consumer picking as a food waste driver.

While fast delivery helps retailers to stay competitive, it often leads to worse environmental outcomes. We study how to nudge online retail customers to voluntarily choose a green but slow shipping option. We develop and test a theory to inform which strategies are more effective based on their ability to overcome psychological barriers. The results of our experiments provide guidelines on which information strategy to use in two logistical contexts: no-rush shipping and consolidated shipping.

"Eye level is buy level" is a common belief in the retail industry, but there is a lack of rigorous empirical evidence to support it. While some studies have used observational data and eye-tracking technology, more controlled field experiments are needed. This study uses a two-stage experimental design in collaboration with a convenience store chain to investigate the impact of eye-level placement on product sales.

Supplier copycatting occurs when the supplier (S) to a manufacturer (M) copies M’s product and sells a copycat product to the customers. M can change its suppliers; and S runs the risk of facing repercussions if it decides to copy. We propose a two-period game-theoretic approach to supplier copycatting. We investigate a setting with M, copycatting S, and non-copycatting S, and examine how the equilibrium is influenced by the presence of future opportunities and repercussions facing the firms.

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We study a manufacturer-supplier interaction during the rollover between a legacy part and its successor in a durable good supply chain. In practice, manufacturers try in vain to leverage the future business of the new part to delay a supplier’s ''last time buy'' and retirement of the old part. We propose a two-stage noncooperative game to guide managers and prove that there exist only six subgame perfect Nash equilibria which achieve this delay under a simple necessary and sufficient condition.

We study an off-policy contextual pricing problem where the seller has access to samples of prices that customers were previously offered, whether they purchased at that price, and auxiliary features. This is in contrast to the well-studied setting in which samples of the customer's valuation are observed. We focus on convex loss functions for pricing in this setting, prove expected revenue bounds when the valuation distribution is log-concave, and provide generalization bounds.

We study the design of stochastic optimization methods with a focus on guaranteed out-of-sample performance when data is corrupted. We design a novel robust approach that offers protection against corruption while ensuring strong generalization. Our approach is based on distributionally robust optimization with a combination of Kullback-Leibler and Levy-Prokhorov ambiguity sets. Our method is applied to training neural networks, resulting in robust networks with state-of-the-art performance.

The paper presents dynamic pricing with demand learning in presence of covariates and develops safe UCB pricing algorithms. The pricing algorithm minimizes total regret where the expected regret of the pricing policy should be at least as good as a fraction of that of the baseline policy. We extend it to the case that the cumulative reward of the algorithm is at least as high as a fraction of the cumulative reward of the baseline policy with known and unknown baseline expected reward.

Patient-level data from 72 MRI hospitals in Ontario, Canada from 2013 to 2017 shows that over 60% of patients exceeded their wait time targets. We conduct a data-driven analysis to quantify the reduction in the patient Fraction Exceeding Target (FET) for MRI. Our resource pooling model lowers the FET from 66% to 36% while constraining the average incremental travel time below three hours. In addition, our model and method show that only ten additional scanners are needed to achieve 10% FET.

Problem: We optimize personalized interventions for behavioral health using digital platforms, considering cost and capacity constraints. Existing approaches are data-intensive or overlook long-term dynamics. Our DecompPI algorithm approximates policy iteration, reducing intervention costs while maintaining efficacy. A case study shows potential for 50% cost reduction, enabling scalable implementation.

Many developing countries lack the health-emergency infrastructure of the developed world. In this context, our industry partner Flare (operating in Nairobi, Kenya) coordinates existing ambulance providers by operating a platform. We study the operational challenges for such platforms as they often lack knowledge about all ambulances' future availability and their location at a tactical level and typically do not fully control these ambulances.

We study privacy protection mechanisms inspired by recent regulatory regimes, limited data retention and customer self protection. Privacy protection affects the estimation of demand model and thus the charged prices. We find that the change of the resulting price and which customer groups benefit from the protection depend on the product type. A real dataset of online auto loans validates our theoretical findings. We also extend the framework to nonlinear demand functions and duopoly.

Personalized recommender systems face privacy concerns. We explore optimal design under local differential privacy constraints. Our findings suggest a coarse-grained threshold policy for recommendations. Pursuing privacy comes at an economic loss but may benefit consumers. Our study guides algorithm design and informs regulators on privacy policies.

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To increase equity on their platform, we re-designed the search algorithm on VolunteerMatch (VM), the largest online platform for connecting volunteers and nonprofits. The implementation of our algorithm in Dallas led to a 10.2% increase in the number of different volunteer opportunities that receive a sign-up each week without reducing the total number of sign-ups: a Pareto improvement for VM. A similar effect nationwide would lead to 800 more opportunities with at least one sign-up each week.

We build a game-theoretical model to analyze the impact of a merger between two platforms on consumer surplus and labor welfare. While a merger reduces competition, the merged platform can pool customers and agents together and improve matching between them; moreover, the merger can amplify the cross-side network effect and thus moderate the merged firm's pricing power. Under a sufficiently strong cross-side network effect, a merger can make merging firms, customers, and agents all better off.

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Recycled wastewater plays a pivotal role in water sustainability by closing the urban water cycle. This paper studies the wastewater recycling capacity investment decision of a water utility using a two-stage stochastic cost-minimization model under rainfall and recycling cost uncertainties. We characterize the optimal wastewater recycling capacity investment and conduct sensitivity analysis to investigate the impacts of uncertainties on the optimal expected cost and recycling capacity.

Disasters disproportionately affect low-income communities' access to groceries. We study price changes after hurricanes and find higher increases in low-income areas, along with stockouts and substitutions.

Low-income communities face greater price increases, reduced promotions, more stockouts, and increased substitution to higher-priced products.

Disaster response should address pricing, stock management, and equitable access to groceries based on community needs and substitution patterns.

Existing stormwater management fails to address climate-adaptive needs in cities experiencing intensified rainfall. This paper utilizes robust optimization to identify worst-case rainfall scenarios and their impact on flooding loss. It provides guidelines for risk mitigation, optimal infrastructure designs, and emphasizes the urgency of taking action as climate change exacerbates flooding risks.

In global retail marketplaces, returns may result in long shipping distances and loss of welfare through time-induced price deterioration. We propose to minimize these negative impacts by exchanging returns within the marketplace’s supplier network. We show that an auction-based system where suppliers bid on the returned items reduces returns’ shipping distance by up to 88% and increases resell value on average by 5% mitigating the drawbacks of benevolent return policies of retail marketplaces.

Motivated by our interaction with a labor market, we focus of matching jobs to workers with heterogenous preferences. In each period, jobs arrive sequentially. Each worker has three parameters: her work quality, capacity, and minimum number of jobs she desires. The platform wishes to maximize the matches quality via its online matching policy. We use our model to understand limitations of simple policies and propose an optimal index-based policy. We supplement our theory with extensive numerics.

Motivated by the rapid growth of the cloud computing industry, we study an infinite-horizon, stochastic optimization problem from the viewpoint of a firm that employs cloud resources to process incoming orders (or jobs) over time. Orders and resources are heterogeneous. The firm's goal is to minimize the long-run average expected cost per period, considering reserved-capacity costs, on-demand capacity costs, and order-delay costs. We show that our proposed policy is asymptotically optimal.

This paper uses a newsvendor setting to analyze the effect of payment methods and parking prices on payment amounts, occupancy, and wait times. Empirical results show that cash or mobile app payments are less than credit card payments, and lower prices increase payments for all methods. Simulation shows that lowering prices has a greater effect on occupancy and wait times than increasing them. The study also indicates surroundings of parking spaces significantly affect performance measures.

With the emergence of on-demand platforms, it has become viable for drivers to participate in package delivery and passenger rides operations at the same time. The integration and coordination of the two services, known as co-modality, is considered a promising solution for improving the efficiency of urban logistics and mobility systems. In this work, we propose a simple zoning-based coordination policy and analyze its performance against pure delivery and ride-hailing policies.

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Rent-to-own (RTO) firms rent products in exchange for a fee and offer the already-rented products for purchase at buyout prices to their renters. Prediction of demand requires a decision model that captures the renters' decision-making process and their ownership and rental utilities. We develop a modular framework that separates utility estimation from identifying the renter's decision process. We build renter decision models, reflecting different degrees of sophistication and alertness.

Digital goods firms routinely utilize renting models for product differentiation: one can try a free or cheap version first, then subscribe to unlock additional functionality. Recent technological advances enable physical goods firms to create products with identical hardware that are digitally controlled to allow for similar differentiation. We present a stylized model to explore when physical goods firms should adopt such differentiation instead of the traditional high- and low-end products.

Consider a platform facilitating matches in the presence of supply congestion. A key attribute of supply is how competitive it will be for demand to obtain the supply after the match. Should the platform reveal current competition levels? To answer this, we partnered with a marketplace and propose a structural model in which workers account for future competition. We conduct counterfactual analysis to study the impact of signaling competition on workers' lead purchasing decisions and revenue.

We theoretically and experimentally study the secretary problem under competition, focusing on the effect of two market design choices: i) transparency about agents’ priorities; and ii) the mechanism to collect decisions (simultaneous vs. sequential). Our results show that the latter affects the saliency of competition and induces frustration. Moreover, we theoretically show that transparency may lead to higher welfare, but the benefits do not translate to practice due to information friction.

We propose and analyze a stochastic model of service interactions that captures two (a)symmetries between the customer and agent: co-production vs self-production, synchrony vs asynchrony. This model reveals connection to the behavioral operations literature, such as non-monotonic system performance from monotonic agent-load slowdown, yielding insights for decision making and analysis.

In this paper, we investigate how line-sitting and express lines affect customers' satisfaction and fairness perceptions. We show that customers who encounter line-sitting report higher satisfaction with their overall experience than those who encounter an express-line customer, despite the actual wait time being the same. Moreover, we find that the effect of queueing schemes on customer satisfaction is mediated by differences in fairness perception.

Several prominent online platforms operate both reselling and agency channels. We explore when and why the platform and a supplier would choose to interact via the platform's agency channel versus its reselling channel.

Problem: Economic drivers of blockchain adoption for supply chain financing. Methodology/Results: Model of firms in a linear supply chain facing information leakage and entry threats. Adoption depends on short-term gains vs. long-term threats. Blockchain adoption is welfare enhancing. Proposed solutions: smart contracts and self-financing transfers.

Problem: Loyalty programs and consumer payment choices. Methodology/Results: Analyzing loyalty program data, we find factors influencing payment decisions, such as mental accounting and perceived value of points. Different consumer segments exhibit distinct attitudes toward points. Managerial implications: Firms can use this understanding to design effective pricing strategies, expand partnerships, and enhance consumer loyalty.

We study a single-item multi-warehouse multi-location order fulfillment problem faced by an online retailer with limited logistic capacity. Orders shall be fulfilled within the time window before being lost. We proposed a heuristic policy based on Lagrangian relaxations and showed that it is asymptotically optimal when the retailer serves a large number of locations. We also showed that a "two-day fulfillment" strategy, together with the policy, could mitigate the shortage of logistic resources.

We examine how an online grocer's fulfillment options affect customers' weekly and lifetime spending. Based on our results, we develop a structural model that estimates the preferences underlying customers’ choices while learning customer preferences. Based on a counterfactual analysis, we recommend whether the retailer should offer pick-up, delivery, or both services in each geographic market.

In this work, we propose an approach that leverages historical customer purchase conversion rates when designing a middle-mile consolidation network that aims to maximize the profit of large e-commerce retailers. We embed lead-time dependent sales volume predictions into a new mixed-integer program (MIP) that simultaneously determines shipment lead times and consolidation plans to maximize profit.

We consider a problem where customers repeatedly interact with a platform. The probability that a customer engages depends on past purchase history. The platform maximizes the total revenue obtained from each customer over the horizon. We study the dynamic program when consumer preferences are known and prove structural properties. We provide a formulation in a contextual episodic RL setting and prove a regret bound. We evaluate effectiveness on simulations, using real data from Expedia.

In a feedback collection process, a company sequentially and adaptively shows display sets to a population of customers and collects their choices. The objective is to identify the most preferred item at a high confidence level with the least number of samples. We propose an elimination-based algorithm, namely Nested Elimination (NE). NE is intuitive, simple in structure, easy to implement, and has a strong theoretical/numerical performance for sample complexity.

Problem: Inpatients wait for hospital diagnostic services, causing chaos. Methodology: We propose "advance notice" scheduling, providing patients with preparation and service time windows. Markov Decision Process optimizes decisions. Numerical study shows significant operational improvement. Managerial implications: Advance notice policy reduces waiting and improves appointment-based services.

Problem: Evaluating an ED delay announcement system in Hong Kong's healthcare. Methodology: Studying 1.3M patient visits, we estimate patient sensitivity to announced waiting times (WT) and factors influencing it. Results show potential improvements in reducing WT and patients leaving without being seen. Implications: Increase patient awareness, reduce WT update window, focus on older population and Kowloon district for promotion.

We study hospitals' operational characteristics contributing to the re-admission of psychiatry patients. We propose that length of stay (LOS) mediates the effects of hospital characteristics on the risk of readmission. We reveal how patient characteristics moderate these effects. Using a dataset of 15,000 psychiatry patients, we provide evidence on the negative volume-outcome and nonlinear LOS-outcome relationship. Our analysis provides helpful insights for managing the flow of psychiatric patients.

We study an extension of the cardinality-constrained knapsack problem where each item has a concave piecewise-linear utility structure. For the offline problem, we present a fully polynomial-time approximation scheme and show that it can be cast as the maximization of a submodular function with cardinality constraints; the latter result allows us to derive a greedy (1 − 1/e)-approximation algorithm. For the online problem in the random order model, we present a 6.027-competitive algorithm.

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We present a model for studying a ride-hailing platform that is faced with price and delay sensitive riders and drivers, and is considering offering two different service classes which are differentiated in prices and delays. We explore the “price of two sides”, show that the preferences of drivers impact the delays the riders experience and demonstrate that achieving the “full optimum" of price differentiation may not be feasible or optimal for a platform in all market conditions.

Ride-hailing platforms offering shared rides devote effort to reducing improving shared-ride efficiency: reducing the trip-lengthening detours that accommodate fellow customers' divergent transportation needs. Contrary to naive intuition, we show: greater customer sensitivity to shared-ride delay and greater labor cost can reduce the value of improving shared-ride efficiency; and an increase in shared-ride efficiency can prompt a platform to add individual-ride service.

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We study whether legalizing marijuana affects the operational efficiency of manufacturing facilities. We leverage a state-level quasi-experimental setting that arises with the staggered passage of marijuana laws. We find that medical marijuana legislation (MML) adversely affects operational efficiency of facilities, the average waste increased by 5.22 % after MML. Facilities start fewer managerial and technical modifications after MML, which clarifies the degrading mechanisms.

Retailers are increasingly sponsoring take-back initiatives to facilitate the recycling and reuse of secondhand clothing. To stimulate the return of goods, providers share information about circular economy strategies and offer consumers a small incentive in exchange for used clothing items. Through three experiments involving over 3,5000 subjects, we test how consumers’ willingness to return their used garments is affected as the degree of circular economy transparency and reward level is manipulated.

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This paper studies how service-age information influences customers’ strategic joining decisions in an observable M/G/1 queue. We ask how such information is reflected in customers' strategies at equilibrium, and what would be the corresponding system throughput and social welfare. The managerial insights indicate that a revenue-maximizing provider should disclose the service-age information when congestion is high, while a social planner should disclose the information when congestion is low.

We study a problem where a fixed number of servers must be split between a shared and a dedicated track. High-priority customers can be served on the dedicated track or the shared track with non-preemptive priority over low-priority customers. Low-priority customers are strategic and choose to either join the shared track or balk from the system based on wait time information. We use a queueing game to study the capacity allocation decision under different information revealing policies.

Strategic servers may discreetly disclose or hide queue length information to manage customers' joining behavior. For customer-intensive services, the choice of service rate further complicates this queue observability decision by affecting the service quality and consequently customers’ service rewards. In this work, we provide an integral analysis of managing speed-quality trade-off with the joint use of pricing and queue disclosure strategies.

Online labor markets are growing rapidly, where the numerical rating to workers provides a critical metric of service quality. In this study, we propose a two-stage machine learning framework to predict such quality. We apply our method on a unique from a leading online labor platform. We provide an understanding of how various features impact service quality. Then, we discuss the value of prediction in decision-making and provide actionable insights to improve service quality in OLMs.

Service providers often offer multiple variants of a service to their customers simultaneously through different servers. These servers could differ in their service value and expected service times, making one server preferable to customers over the other. However, even when the preferred server is busy and the less preferred server is free, customers may choose to wait for the preferred server. This behavior is called "stalling". Our goal is to price the servers in order to maximize revenue.

Ride-hailing platforms are highly popular so their decisions can affect social satisfaction levels significantly. The price determined by the platform is so critical decision factor in managing their demand and pool of available drivers. However, consumers are affected by pricing in different ways due to their heterogeneity of price or waiting time sensitivity.

We analyze a waiting time differentiation pricing strategy using a game-theoretic model in both monopoly and duopoly environments.

To address income inequality in "Superstar Markets", we propose income pools - a contract where individuals agree to share a portion of future earnings if they become successful. We develop the first math model and prove that no finite-sized stable pool exists. In response, we consider bounded stable pools and epsilon-stable pools, proving their existence and Pareto properties. Our case study on professional baseball shows a 20%-30% welfare increase, most acutely benefiting the weakest agents.

The gig economy relies on task outsourcing, but gig workers face challenges in finding locations and products. We conduct field experiments using AI-enabled guidance to help shoppers. Technology reduces refunds and benefits less experienced shoppers. However, complex routing algorithms increase consultation time and pickup times. AI improves efficiency and increases revenues, but technology adoption has limits and overuse can reduce productivity.

Motivated by collaboration with a clinic, we study open-shop service networks with two service level measures: macro-level average overall wait time and micro-level probabilities of excessive waits for individual services. In a stylized two-station open-shop network, we analytically show that an advanced customer priority and the buffer strategy can improve macro- and micro-level performance. We provide means to improve customers' experience in open shop service networks not applicable before.

We study the behavioral effects of process automation on human workers interacting with automated tasks. A stylized model with two workers completing their tasks sequentially predicts that workers do not delay their tasks if the early completion bonus is high enough. Our behavioral experiment shows that workers actually tend to delay their tasks. Process automation improves the project completion rate and time but reduces the productivity of the worker who collaborates with the robot.

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How can retailers promote healthier food choices? Price, convenience, and taste are key factors. Healthy nudges can incentivize customers. In a field experiment, add-on bundles were tested: unhealthy, healthy, and choice bundles. Healthy snacks increased healthy purchases, even when unhealthy snacks were promoted. However, the effect was not long-term. Retailers can benefit by offering add-on choice bundles, increasing revenue and profit.

A project involves several participants – clients, contractors, and subcontractors – who each manage multiple projects concurrently. This creates a network of otherwise unrelated projects. Accordingly, a disruption in one project forces all parties to reallocate resources from other concurrent projects, causing externalities across the wider project network. We use data from 2.6 million U.S. public projects – and their networks – to quantify the importance of these network externalities.

We consider the capacity planning problem to decide the initial capacity to maximize the expected total profit when general upgrading is allowed. Given the marginal mean and variance information of the demand distribution for each product, we formulate it as a two-stage distributionally robust optimization (DRO) model. We show how to reformulate the DRO model into a tractable SOCP formulation and conduct extensive numerical experiments to validate the out-of-sample performance of the DRO solution.

Problem: The effectiveness of flexibility in mitigating demand and supply mismatch with unequal profits is unclear. Methodology/Results: We evaluate the effectiveness of a long-chain structure compared to full flexibility. Performance ratio lower bound shows effectiveness of some flexibility. Performance ratio increases with profit differentials. Managerial implications: Cluster high-profit products for optimal flexible structures.

Price promotions are an important and widely established retailer's tool to uplift sales, foster cross-selling through increased store traffic and strengthen customer relationships. Retailers hereby face a self-induced dilemma of balancing high product availability and waste. By empirically showing that price promotions are a food waste driver in grocery retail for highly perishable goods, our study helps to address one pressing global social, ecological and economic sustainability issue.

Inefficient food system operations create huge waste for the earth, and cut profit of firms. In this research, we address the simultaneous determination of pricing and inventory control for perishable products to maximize profit. The optimal policy is computationally intractable due to the curse of dimensionality. Instead, we construct a stationary base-stock list-price policy. We show our approximate policy is asymptotically optimal under several parameter regimes.

We study joint replenishment and issuing policy for a perishable product with general lifetime using a periodic review model over a finite horizon. Customers' sensitivity to the remaining lifetime of the unit received is captured by a goodwill cost, which increases as the remaining lifetime decreases. We find that contrary to the case when the issuing policy is fixed (e.g., last-in, first-out policy), the value function in our model is always jointly concave in the on-hand inventory vector.

Our study focuses on a preference learning setting where customers select k most preferred items from a personalized set. Our new Mallows-type ranking model offers simple closed-form (ranked) choice probabilities that can be learned through MLE with theoretical guarantees. We demonstrate the model's excellent performance with real data sets. We use our model to study how feedback structure is related to the efficiency of the feedback collection and find that a little favor goes a long way.

This paper applies a machine learning lens to the problem of choice estimation. We (1) establish the first truly realistic-scale benchmark for practical choice estimation, (2) use this benchmark to run the largest evaluation of choice models to date, and (3) propose and prescribe the use of a simple, irrational choice model, which we dub the Sparse Halo-MNL.

When allocating products to brick-and-mortar stores, retailers face product availability constraints that force them to balance product offerings across stores. We model this problem under multinomial logit demand and show that the problem is NP-complete. We develop a tractable continuous relaxation of the problem which has a unique local maximum and allows us to build near-optimal solution algorithms. We use data from a large retailer and identify improvements of better product-store matching.

We investigate the crucial and complex task of selecting patients by physicians in emergency departments when multiple patient types are present. Conventional approaches in guiding physician decision-making are based on patient triage levels or waiting times. However, an important factor that is often overlooked is the time remaining in a physician's shift. We utilize a transient optimal control on the corresponding queueing system to derive an optimal time-dependent patient selection strategy.

Given heterogeneous treatment time distributions, no-shows, and patients that can arrive early or late, we show how a discrete time Markov chain model can be used to equitably space patients so that no patient has an expected wait longer than some maximum value. The calculation of an equitable schedule can be done extremely fast, in polynomial time, involving only simple linear algebra (matrix multiplication). The equitable schedule adds at most 2% to the clinic’s operation time.

Emergency Department (ED) boarding refers to the delay in transfer experienced by admitted patients from the ED to inpatient units. Using an instrumental variable design, we found that, on average, longer boarding time leads to a longer hospital stay and a higher chance of care escalation. Our findings also reveal that the impact of boarding differs across patients, suggesting that considering such heterogeneity when assigning inpatient beds could improve downstream efficiency and quality of care

We study an adversarial online assortment problem with reusable resources and customer-dependent usage durations. We propose a novel online algorithm which features rejection durations filtering out unprofitable products. We show that it achieves a competitive ratio within a constant factor from the best possible one with large capacities. This is the first work that derives a non-trivial performance guarantee for such problem. We further extend our algorithm framework to other reward functions.

We consider an assortment optimization problem for a class of online video games. Our paper is the first to study assortment optimization for the gaming industry under discrete choice models; it is also the first to devise solution approaches for the constrained mixture-of-nested-logit model with performance guarantees. Numerical experiments show that our approaches perform well across a variety of settings. Our work provides guidance to online video game stores for effective revenue maximization.

We study a new class of stock-dependent pricing policies for reusable resources that set prices based on the stock at hand. To find the optimal policy in this class, we introduce a reformulation that is convex. We provide a sharp characterization of the regret of this policy class via matching upper and lower bounds and show they can significantly improve upon static pricing. A simple two-price policy that changes prices when the stock is below a threshold can achieve the optimal rate of regret.

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Many multinational organizations have made dual commitments to halt deforestation and improve farmers’ livelihoods in agricultural supply chains. We propose group incentives conditional on forest protection requirements as a feasible mechanism for achieving this. We develop an analytical model, prove structural results concerning the equilibrium outcomes, and calibrate the model using data we collected from field research in Indonesia, demonstrating the effectiveness of group requirements.

We analyze competition between a sustainable firm and an unsustainable knockoff. We study open-source sharing and advertising strategies.

Sharing green tech can be profitable. Anti-knockoff ads may not be profitable, while self-promotion ads improve profits. Simultaneous use of open-source and ads can be beneficial but may harm the environment.

Firms can use open-source and ads to combat knockoffs. Governments can facilitate sustainable tech sharing through regulations.

In malaria-endemic countries, we explore optimal donor budget allocation for subsidizing antimalarial drugs in the presence of counterfeits. We develop a game-theoretic model and evaluate strategies to combat counterfeits. Our findings suggest the importance of understanding market characteristics to design effective subsidies and policies for improving access to legitimate drugs.

Prior discussions of predictive model development highlight advances in methods, but the value of tools that implement those methods has been understudied. In a field experiment, we study the importance of tools by restricting access to machine learning libraries in a prediction competition. We find that teams with unrestricted access perform 30% better, and teams with high general data-science skills are less affected by the intervention than teams with high tool-specific skills.

We investigate the remanufacturing strategy for the original equipment manufacturer (OEM) and independent remanufacturer (IR) in an innovative industry where the consumer valuation of the products increases with the innovation level and we consider the investments of an OEM to enhance innovation, in the face of a potential entry onto the market by an IR, together with two remanufacturing strategies: whether to remanufacture and whether to include innovative features in remanufactured products.

Optimization in sustainability is inherently multi-criteria and the underlying value function is usually unknown and difficult to elicit. Firms seeking to be more sustainable face difficult choices during material and supplier selection with no clear guidelines on how to make trade-offs between conflicting environmental impact categories. We propose an interactive optimization method to help decision makers, using an illustrative example of sustainable sourcing in the apparel industry.

This paper considers an industrial symbiosis system composed of a supplier and a manufacturer. The supplier produces product A with the output of by-product, which can be reused by the manufacturer as input to produce product B. Competition and cooperation are often juxtaposed in the same system. This paper examines the mode choice of the supplier, i.e., continue to cooperate to supply by-products to the manufacturer or generate direct competition by encroaching on the manufacturer's market.

Evidence shows national institutions driving innovation through openness. We focus on 'social globalization' and design innovation. Using the KOF Globalization Index, we find social globalization predicts design innovation. Robust analyses and U.S. design patents support our findings. Personal contact plays a key role. Social globalization fosters design innovation.

Problem: Despite advances, chatbot adoption faces hurdles. This paper explores customer choice between chatbots and live agents. Methodology: Experiments vary chatbot performance and features. Users respond positively to improvements but underutilize chatbots due to algorithm and gatekeeper aversion. Remedies: Highlight time savings for algorithm aversion. Managerial implications: Nudges and queue priority rules reduce costs by up to 22% in congested systems

Facing emergent business challenges, entrepreneurs often seek guidance from experienced advisors. When there are multiple alternatives that could potentially solve the entrepreneur’s problem, advisors can lead the entrepreneur’s exploration by choosing which alternative(s) to suggest and in what sequence. We develop a dynamic game-theoretic model that captures the sequential interaction between an advisor and an entrepreneur.

Even if algorithms make better predictions than humans on average, humans may sometimes have private information which an algorithm does not have access to, which can improve performance. When deciding how to use and adjust an algorithm’s recommendations we hypothesize people are biased towards a predictable heuristic leading to over-adhering to the algorithm’s predictions when their private information is valuable and under-adhering when it is not. We test these results in two lab experiments.

We develop a sequential decision-making task in the form of a virtual electric vehicle driving game in which the participant needs to make sequential charging decisions when facing uncertain traffic and receiving machine-generated recommendations on their strategy. Our experimental results offer key insights into how humans make decisions and respond to those recommendations, allowing us to design a better human-centric recommendation system.

We examine the effect of team familiarity on surgery duration. We develop a new approach, which we call the “MT forest” approach, to estimate heterogeneous effects of multiple treatments. We find (1) an increase in team familiarity score significantly reduces surgery duration, and (2) the effect of team familiarity is heterogeneous across patients with different features. Finally, we develop an optimization model to better match surgical teams with patients.

We study an inventory control problem called the One-Warehouse Multi-Store (OWMS) problem when the demand distribution is unknown. The OWMS system is ubiquitous in supply chain management, yet its optimal policy is notoriously difficult to calculate even under the complete demand distribution case. In this work, we consider the OWMS problem when the demand is censored, and its distribution is unknown a priori. Results show that our approach has great theoretical and empirical performances.

Using customer data for inventory management can improve profits and service but also increase privacy risk. We developed privacy-preserved adaptations for two data-driven newsvendor pipelines and analyzed the tradeoff between privacy loss, profits, and consumer surplus. We show that the joint approach outperforms the two-step approach. By accounting for downstream optimization problems, we can obfuscate customer data with more targeted noise injection, making it less costly in terms of profits.

Problem: Stochastic lost-sales inventory control with uncertain supply and demand is computationally challenging. We propose an efficient online learning algorithm for unknown distributions. Our algorithm achieves a regret of O(L+\sqrt{T}) when L≥log(T), outperforming existing literature. We address censored data using a coupling argument. Our method eliminates suboptimal solutions.

Staffing planning for last-mile delivery drivers is the process of planning the number of drivers that are required each week to deliver all the expected volume for a pre-determined time horizon, with the ability to adjust the decisions over time under guardrails restrictions. We formulate the problem as a multi-dimensional stochastic dynamic program with a newsvendor-based cost function and propose an approximation algorithm that can solve the problem to near optimality in tractable time.

The food delivery market migrates to platforms that allow optimizing courier routing by sharing couriers among many restaurants. We address the question: how does courier sharing contribute to the reduction of delivery costs? We consider a spatial queuing model in which couriers are servers. We show that in several scenarios dedicated courier policy achieves higher profit than a sharing policy. This result can be attributed to the imbalance in the courtier allocation that sharing creates.

The pandemic era experienced a significant amount of port congestion. Such congestion at one port spreads to another, leading to shipping delays and driving up costs for shippers. In this paper, we build an analytical fluid model to study how a disruption at a port would impact a disrupted port in one country and its counterpart port in another country.

We consider a decision-maker (DM) who can sample from multiple information sources to learn a state before making an earning decision. The DM optimizes his sampling and earning decisions to maximize his payoffs. The problem is motivated by financial and healthcare applications with multiple information sources. We develop a Bayesian control framework for this problem and solve it in the estimation and testing settings. We also develop an efficient algorithm for the general control setting.

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The US heart transplant system prioritizes candidates based on the severity of their pre-transplant therapy; the premise is that this severity reflects medical urgency. However, it is widely suggested that this rule opens room for gaming the system, by assigning high-severity therapies to low-urgency patients. We study a novel model of the gaming decisions of heart transplant centers. We identify the underlying trade-offs and shed light on the conditions that give rise to such gaming.

In this paper, our goal is to (i) characterize the equilibrium behaviour of the agents under targeted priority mechanisms, (ii) identify the impact of these mechanisms on several performance metrics, including the discard rates of organs, and overall social welfare, and (iii) establish the impact of the selection of design parameters on different outcomes in equilibrium to investigate the optimal design of such mechanisms.

We optimize blood donor incentivization to reduce shortages and wastage. Our model considers random demand, perishability, and donor variability. Using a coupled queueing network approach and the Pipeline Queue paradigm, we derive a tractable convex reformulation. Results show improved performance compared to the threshold policy. It provides decision support for dynamic donor incentivization in blood supply chain management.

We consider the problem of optimal assortment design for a platform by imposing fairness constraints that guarantees exposures of all products to consumers. We show that optimal solution can be found in polynomial time and the algorithm is easy to implement in practice. We investigate the welfare change of imposing the fairness constraints and find that it is possible to achieve a win-win-win solution where the platform, sellers, and consumers are all better off.

Problem: Allocating perishable resources online with non-linear utilities and complementarities. Methodology/Results: An algorithm achieves optimal envy-efficiency Pareto frontier by adapting to perishing order predictions and desired envy bounds. Numerical performance is demonstrated using data from a food bank. Managerial Implications: Accurate perishing predictions are crucial for fairness in practical non-profit settings.

We study the marketplace platform's problem that decides the assortment of sellers and how much fee to apply to each seller. In our model, each seller has its own outside option, which depends on customer traffic and product preference. The seller's compensation is determined based on the competition with the other joining sellers. We characterize the platform's optimal fee and assortment decisions and further study how the different fee contracts affect the optimal assortment.

This paper investigates the roles of selling format in a two-sided marketplace with many sellers selling substitutable products on a common retailing platform. We show that a contribution-based scheme (CBS), whereby the payment for each seller is based on her contribution, leads to a stable, efficient, and `win-win' outcome for all firms in the entire marketplace. Our findings could provide useful guidance on the design of strategic partnership between firms in a two-sided marketplace.

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We study how to optimally allocate a limited resource (community solar (CS) capacity) among consumers with heterogeneous income levels and private resource valuations. We identify the shortcomings of existing CS program designs, and study various alternatives. Our recommendation offers consumers income-dependent menus of subscription capacity and rate options; this significantly improves social welfare. We further illustrate its usefulness using numerical studies calibrated by real data.

US nonprofits disclose three expenses annually: program to meet their beneficiaries' needs; fundraising to raise donations; administration to build and maintain capacity. We characterize the optimal budget allocations to program, fundraising, and administration using a two-period model, which also includes the nonprofit’s capacity, return on program (the net value of program to beneficiaries) and uncertain future needs of beneficiaries. The optimal allocation depends on the nonprofit's capacity.

As consumers, policy makers and NGOs associate low environmental impact with “uniformly green” (UG) products or higher recycled/reused content, we explore two issues: (i) Should distinct green preferences be “targeted” with distinct product variants? (ii) Is profitable UG adoption environmentally superior? We show that optimal and eco-friendly UG adoption requires design/quality control and reduced production/material costs, and blindly demanding UG adoption may worsen environmental results.

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We propose a multi-stage choice model in which customers can choose multiple products and multiple units of each product in a single shopping trip. We characterize the optimal assortment under the cardinality, space, and basket size constraints, respectively. We prove the NP-hardness of the problem under the latter two constraints and develop approximation algorithms to find near optimal assortments. We solve the price optimization problem efficiently and provide a calibration method.

This paper studies assortment optimization and product line design problems under the mixed multinomial logit model and discrete pricing. Both literature streams are connected by improving exact, extending approximate, and novel heuristic methods. We show that an FPTAS algorithm exists even if prices are taken into account. To improve the state-of-the-art conic formulation, valid inequalities are introduced to a branch and cut method. Our results show an average time reduction of 35 % - 66 %.

Many product recalls are caused by quality-related product failures. This paper analyzes quality and pricing strategies for competing firms facing the risk of a severe quality-related recall making the product hazardous and leading to its removal from the market. We develop a two-stage Nash game where the probability of recall depends on the firms’ chosen quality investments, and either firm can experience a recall.

Customers are concerned about personalized pricing (PP) tactics. Using a Bayesian persuasion framework, we study whether and under what conditions price can signal such PP implementation to customers. We also investigate whether disclosing inventory availability information can alleviate customer concerns and benefit the firm and customers. We show that price alone may not signal PP, and firms can create transparency over the pricing strategies by disclosing inventory availability information.

Patients self-triage for acute care but lack medical knowledge, leading to inaccurate decisions. Virtual triage tools aim to improve self-triage. We develop a queueing game model to assess virtual triage's operational efficacy. Excessive emergency primary care recommendations reduce ED GP visits. Informative virtual triage can worsen system performance. Policy actions should consider decentralized behavior, incentive alignment, and accuracy decisions based on ROC curve.

Problem: Crowdfunding with insiders and outsiders faces reciprocity and information asymmetry. Methodology/Results: High reciprocity hampers information transmission, benefiting outsiders. Information asymmetry lets outsiders gain a larger share. Increased reciprocity reduces investor payoff. Managerial Implications: Limiting investors or investment improves information transmission. Leverage social networks for better crowdfunding.

We study the implications of assortment variety on customer choice. Specifically, we are interested in characterizing and quantifying the effects of assortment variety on customer choice by proposing and operationalizing a novel representation of an assortment that measures variety along multiple dimensions. Moreover, we investigate potential moderating effects of assortment variety on customer choice, namely customer segments and seasonality. We test our model on a large clickstream data set.

Retailers often provide store inventory information on their websites, and it can influence customers' decisions to visit the store. For instance, when a customer wants to buy three units of a product and the website indicates three units are available, concerns about inventory insufficiency or inaccuracy may still arise. This paper investigates the impact of website inventory information on customer behavior and how retailers can optimize the displayed inventory information to maximize profits.

Prior research on loyalty programs typically finds rewards increase loyalty, without considering the impact of redemption size and consumer habits. We demonstrate these factors are significant predictors in fostering long-term loyalty, with smaller redemptions often outperforming larger ones. Our results demonstrate redemption is a key lever in loyalty program optimization.

We address the reliable facility location problem in a data-driven setting by presenting a model aiming to balance solution conservatism with efficiency. In particular, our model approximates the total cost by a tractable data-driven estimator, which equals to a probabilistic upper bound on the intractable Kolmogorov DRO estimator. Our approach is proved to be asymptotically optimal, and offers a theoretical guarantee for its out-of-sample performance in situations with limited data.

Supply chain models often receive little testing due to a lack of data. We propose a random model of supply chain networks to overcome this problem and establish that it generates accurate representations of real networks. Generated networks' treewidth is logarithmic in the number of firms, which has important implications for tractability. We illustrate this with the NP-hard guaranteed service model, showing a pseudo-polynomial time algorithm for networks with logarithmic treewidth.

This paper proposes an empirical framework for analyzing the evolution patterns of supply chain networks over time. Using a semiparametric matrix variate factor model, we investigate the latent lower-dimensional structure of the network dynamics and the loading matrices that connect the underlying latent factors with the surface supply chain networks and characterize the latent nodes. Our findings shed light on the latent structure, centrality, trends, and patterns of supply chain networks.