Discrete Optimization
On carriers collaboration in hub location problems

https://doi.org/10.1016/j.ejor.2019.11.038Get rights and content
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Highlights

  • Hub location models are introduced for alternative carriers collaboration policies.

  • A theoretical analysis is developed quantifying potential savings for each policy.

  • Mixed-integer programming formulations are presented for each collaboration policy.

  • Extensive computational tests on large testbed adapted from well-known instances.

  • Numerical results confirm that large savings can also be obtained empirically.

Abstract

This paper considers a hub location problem where several carriers operate on a shared network to satisfy a given demand represented by a set of commodities. Possible cooperative strategies are studied where carriers can share resources or swap their respective commodities to produce tangible cost savings while fully satisfying the existing demand. Three different collaborative policies are introduced and discussed, and mixed integer programming formulations are provided for each of them. Theoretical analyses are developed in order to assess the potential savings of each model with respect to traditional non-collaborative approaches. An empirical performance comparison on state-of-art sets of instances offers a complementary viewpoint. The influence of several diverse problem parameters on the performance is analyzed to identify those operational settings enabling the highest possible savings for the considered collaborative hub location models. The number of carriers and the number of open hubs have shown to play a key role; depending on the collaborative strategy, savings of up to 50% can be obtained as the number of carriers increases or the number of open hubs decreases.

Keywords

Location
Hub location
Mixed integer programming
Carriers collaboration

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