Business Analytics for Intermodal Capacity Management

Long Gao (), Jim (Junmin) Shi (), Michael F. Gorman () and Ting Luo ()
Additional contact information
Long Gao: School of Business, University of California, Riverside, Riverside, California 92521
Jim (Junmin) Shi: New Jersey Institute of Technology, Newark, New Jersey 07102; Rutgers Business School, State University of New Jersey, Newark, New Jersey 07102;
Michael F. Gorman: School of Business Administration, University of Dayton, Dayton, Ohio 45469
Ting Luo: Mihaylo College of Business and Economics, California State University, Fullerton, California 92831

Manufacturing & Service Operations Management, 2020, vol. 22, issue 2, 310-329

Abstract: Network operations often suffer from chronic asset imbalance over time and across locations. This paper addresses the issue in the intermodal industry. The problem is mainly driven by myopic policies, environmental uncertainty, and network interdependence. To address the problem, we develop a unified framework that integrates two core operations: container repositioning and load acceptance. The central piece is the scarcity pricing scheme, which internalizes the externalities each acceptance imposes over time and across locations. The scheme plays two crucial roles: to transmit dynamic scarcity information and to incentivize container repositioning. It is most effective when network imbalance and supply risk are high. Exploiting random capacity and heterogeneous lead time, we further refine the load acceptance policy and develop efficient algorithms. We demonstrate that our approach can dynamically reduce network imbalance and improve efficiency. As such, our work provides analytical tools and insights on how to manage network capacity, when the information is dispersed and evolving over time.

Keywords: network operations; spatial pricing; capacity management; dynamic programming; simulation; stochastic comparison (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:22:y:2020:i:2:p:310-329

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