Omar Besbes

Surge Pricing and Its Spatial Supply Response

Management Science · 2020 · 161 citations

We consider the pricing problem faced by a revenue-maximizing platform matching price-sensitive customers to flexible supply units within a geographic area. This can be interpreted as the problem faced in the short term by a ride-hailing platform. We propose a two-dimensional framework in which a platform selects prices for different locations and drivers respond by choosing where to relocate, in equilibrium, based on prices, travel costs, and driver congestion levels. The platform’s problem is…

Spatial Capacity Planning

Operations Research · 2021 · 67 citations

We study the relationship between capacity and performance for a service firm with spatial operations, in the sense that requests arrive with origin-destination pairs. An example of such a system is a ride-hailing platform in which each customer arrives in the system with the need to travel from an origin to a destination. We propose a parsimonious representation of a spatial multiserver system through a state-dependent queueing model that captures spatial frictions as well as spatial economies…

How Big Should Your Data Really Be? Data-Driven Newsvendor: Learning One Sample at a Time

Management Science · 2023 · 62 citations

We study the classical newsvendor problem in which the decision maker must trade off underage and overage costs. In contrast to the typical setting, we assume that the decision maker does not know the underlying distribution driving uncertainty but has only access to historical data. In turn, the key questions are how to map existing data to a decision and what type of performance to expect as a function of the data size. We analyze the classical setting with access to past samples drawn from th…

Workforce Scheduling with Heterogeneous Time Preferences: Effective Wages and Workers’ Supply

Manufacturing & Service Operations Management · 2024-08-01 · 12 citations

Problem definition: Motivated by the debate around workers’ welfare in the gig economy, we propose a framework to evaluate current practices and possible alternatives. We study a setting in which customers seek service from workers and a platform facilitates such matches over the course of the day. The platform allocates time slots to workers using an allocation policy, and the workers are strategic agents (with respect to “when to work”) who maximize their expected utility that depends on their…

Contextual Inverse Optimization: Offline and Online Learning

Operations Research · 2023-08-02 · 11 citations

Learning from data are critical across applications. However, in many applications, past data only gives partial information about the future. In “Contextual Inverse Optimization: Offline and Online Learning,” Besbes, Fonseca, and Lobel study a general setting in which historical data are associated with observations of past optimal actions from experts in specific contexts but without the underlying rewards associated with these actions. To what extent can one “reverse engineer” the underlying…