Business analytics for intermodal capacity management

Long Gao, Jim Junmin Shi, Michael F. Gorman, Ting Luo

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)310-329
Number of pages20
JournalManufacturing and Service Operations Management
Volume22
Issue number2
DOIs
StatePublished - Mar 2020

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research

Keywords

  • Capacity management
  • Dynamic programming
  • Network operations
  • Simulation
  • Spatial pricing
  • Stochastic comparison

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