A conditional β-mean approach to risk-averse stochastic multiple allocation hub location problems. (February 2022) - Ghaffarinasab, Nader; Kara, Bahar Y.
Abstract: This paper addresses risk-averse stochastic hub location problems where the risk is measured using the conditional β -mean criterion. Three variants of the classical multiple allocation hub location problem, namely the p -hub median, the p -hub maximal covering, and the weighted p -hub center problems are studied under demand data uncertainty represented by a finite set of scenarios. Novel mixed-integer linear programming formulations are proposed for the problems and exact algorithms based on Benders decomposition are developed for solving large instances of the problems. A large set of computational tests are conducted so that the efficiency of the proposed algorithms is proved and the effect of various input parameters on the optimal solutions is analyzed. Highlights: New risk-averse hub location problems are introduced to the literature. Novel MILP formulations are proposed for the problems. Exact algorithms based on Benders decomposition are proposed to solve the problems. Extensive computational study is carried out using the proposed algorithms.