Dynamic Pricing of Relocating Resources in Large Networks

Santiago R. Balseiro (), David B. Brown () and Chen Chen ()
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Santiago R. Balseiro: Graduate School of Business, Columbia University, New York, New York 10027
David B. Brown: Fuqua School of Business, Duke University, Durham, North Carolina 27708
Chen Chen: Booth School of Business, University of Chicago, Chicago, Illinois 60637

Management Science, 2021, vol. 67, issue 7, 4075-4094

Abstract: Motivated by applications in shared vehicle systems, we study dynamic pricing of resources that relocate over a network of locations. Customers with private willingness to pay sequentially request to relocate a resource from one location to another, and a revenue-maximizing service provider sets a price for each request. This problem can be formulated as an infinite-horizon stochastic dynamic program, but it is difficult to solve, as optimal pricing policies may depend on the locations of all resources in the network. We first focus on networks with a hub-and-spoke structure, and we develop a dynamic pricing policy and a performance bound based on a Lagrangian relaxation. This relaxation decomposes the problem over spokes and is thus far easier to solve than the original problem. We analyze the performance of the Lagrangian-based policy and focus on a supply-constrained large network regime in which the number of spokes ( n ) and the number of resources grow at the same rate. We show that the Lagrangian policy loses no more than O (ln n / n ) in performance compared with an optimal policy, thus implying asymptotic optimality as n grows large. We also show that no static policy is asymptotically optimal in the large network regime. Finally, we extend the Lagrangian relaxation to provide upper bounds and policies to general networks with multiple interconnected hubs and spoke-to-spoke connections and to incorporate relocation times. We also examine the performance of the Lagrangian policy and the Lagrangian relaxation bound on some numerical examples, including examples based on data from RideAustin.

Keywords: dynamic programming; applications; probability: stochastic model applications; industries; transportation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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