Risk-averse hub location: Formulation and solution approach

• Published in: Computers & operations research Vol. 143; p. 105760
• Main Authors: Kargar, Kamyar, Mahmutoğulları, Ali İrfan
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.07.2022; Pergamon Press Inc
• Subjects: Algorithms; Hub location; Operations research; Risk; Risk-averse optimization; Scenario decomposition; Stochastic models; Stochastic programming; Scenario decomposition; Risk-averse optimization; Hub location; Stochastic programming
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2022.105760

In this study, we present risk-neutral and risk-averse two-stage stochastic formulations for the uncapacitated multiple allocation p-hub median problem and discuss the impact of risk-aversion on the optimal solution. Although stochastic models are useful to tackle the uncertainty in problem parameters, the solution of these models requires higher computational effort than their deterministic counterparts. Therefore, we present a scenario decomposition algorithm for the stochastic formulations. To evaluate the performance of the proposed solution algorithm, a set of computational experiments is conducted on real data sets. The results show that the proposed algorithm is very effective in finding optimal or near-optimal solutions in significantly shorter computation time than that of deterministic equivalent problems. •We present two-stage stochastic formulations for the hub location problem.•We propose a scenario decomposition algorithm to solve the stochastic models.•The proposed algorithm is very effective in finding optimal/near-optimal solutions.