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.

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.

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.

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%.

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.

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.

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.

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.

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.