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Hub Network Design Problem with Capacity, Congestion and Stochastic Demand Considerations

Bayram, Vedat, Yildiz, Baris, Farham, Saleh M. (2023) Hub Network Design Problem with Capacity, Congestion and Stochastic Demand Considerations. Transportation Science, 57 (5). pp. 1276-1295. ISSN 0041-1655. E-ISSN 1526-5447. (doi:10.1287/trsc.2022.0112) (KAR id:101333)

Abstract

Our study introduces the hub network design problem with congestion, capacity, and stochastic demand considerations (HNDC), which generalizes the classical hub location problem in several directions. In particular, we extend state-of-the-art by integrating capacity acquisition decisions and congestion cost effect into the problem and allowing dynamic routing for origin-destination pairs. Connecting strategic and operational level decisions, HNDC jointly decides hub locations and capacity acquisitions by considering the expected routing and congestion costs. A path-based mixed-integer second-order cone programming (SOCP) formulation of the HNDC is proposed. We exploit SOCP duality results and propose an exact algorithm based on Benders decomposition and column generation to solve this challenging problem. We use a specific characterization of the capacity-feasible solutions to speed up the solution procedure and develop an efficient branch-and-cut algorithm to solve the master problem. We conduct extensive computational experiments to test the proposed approach’s performance and derive managerial insights based on realistic problem instances adapted from the literature. In particular, we found that including hub congestion costs, accounting for the uncertainty in demand, and whether the underlying network is complete or incomplete have a significant impact on hub network design and the resulting performance of the system.

Item Type: Article
DOI/Identification number: 10.1287/trsc.2022.0112
Uncontrolled keywords: hub location problem; hub congestion; capacity building; multiple allocation; second order cone programming; Benders decomposition; column generation; branch-and-cut
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
Q Science > Operations Research - Theory
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Vedat Bayram
Date Deposited: 25 May 2023 09:11 UTC
Last Modified: 01 Nov 2023 15:47 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/101333 (The current URI for this page, for reference purposes)

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