Dynamic Inventory Repositioning in On-Demand Rental Networks

Saif Benjaafar (), Daniel Jiang (), Xiang Li () and Xiaobo Li ()
Additional contact information
Saif Benjaafar: Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota 55455
Daniel Jiang: Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261
Xiang Li: Research and Development (R&D), Amazon, Seattle, Washington, 98109
Xiaobo Li: Department of Industrial Systems Engineering and Management, National University of Singapore, 117576, Singapore

Management Science, 2022, vol. 68, issue 11, 7861-7878

Abstract: We consider a rental service with a fixed number of rental units distributed across multiple locations. The units are accessed by customers without prior reservation and on an on-demand basis. Customers can decide on how long to keep a unit and where to return it. Because of the randomness in demand and in returns, there is a need to periodically reposition inventory away from some locations and into others. In deciding on how much inventory to reposition and where, the system manager balances potential lost sales with repositioning costs. Although the problem is increasingly common in applications involving on-demand rental services, not much is known about the nature of the optimal policy for systems with a general network structure or about effective approaches to solving the problem. In this paper, first, we show that the optimal policy in each period can be described in terms of a well-specified region over the state space. Within this region, it is optimal not to reposition any inventory, whereas, outside the region, it is optimal to reposition but only such that the system moves to a new state that is on the boundary of the no-repositioning region. We also provide a simple check for when a state is in the no-repositioning region. Second, we leverage the features of the optimal policy, along with properties of the optimal cost function, to propose a provably convergent approximate dynamic programming algorithm to tackle problems with a large number of dimensions.

Keywords: rental networks; inventory repositioning; optimal policies; approximate dynamic programming algorithms; stochastic dual dynamic programming (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2021.4286 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:68:y:2022:i:11:p:7861-7878

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().