A two-stage robust hub location problem with accelerated Benders decomposition algorithm

Reza Rahmati, Mahdi Bashiri, Erfaneh Nikzad, Ali Siadat

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
34 Downloads (Pure)


In this paper, a two-stage robust optimisation is presented for an uncapacitated hub location problem in which demand is uncertain and the level of conservatism is controlled by an uncertainty budget. In the first stage, locations for establishing hub facilities were determined, and allocation decisions were made in the second stage. An accelerated Benders decomposition algorithm was used to solve the problem. Computational experiments showed better results in terms of number of iterations and computation time for Benders decomposition with Pareto-optimal cuts in comparison with the classical Benders decomposition algorithm. According to numerical analysis, it was concluded that increasing the uncertainty budget also increased total costs for more established hubs. To determine the uncertainty budget in an appropriate manner, a new expected aggregate function was introduced. The numerical studies demonstrated the usefulness of the proposed method in defining the appropriate uncertainty budget in the presence of uncertainty.
Original languageEnglish
Pages (from-to)5235-5257
Number of pages23
JournalInternational Journal of Production Research
Issue number17
Early online date6 Sept 2021
Publication statusPublished - 2 Sept 2022

Bibliographical note

© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


  • Two-stage robust
  • hub location problem
  • demand uncertainty
  • Benders decomposition algorithm
  • Pareto-optimal cut

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Strategy and Management
  • Management Science and Operations Research


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