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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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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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.

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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.

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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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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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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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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