A mean-CVaR approach to the risk-averse single allocation hub location problem with flow-dependent economies of scale. (January 2023)

• Record Type: Journal Article
• Authors: Ghaffarinasab, Nader; Çavuş, Özlem; Kara, Bahar Y.
• Is Part Of: Transportation research. Volume 167(2023)
• Page Start: 32
• Page End: 53
• Publication Date: 2023-01
• Subjects: Hub location -- Risk-aversion -- Economies of scale -- Benders decomposition -- Scenario grouping; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2022.11.008
• Languages: English
• ISSNs: 0191-2615

Abstract

Abstract: The hub location problem (HLP) is a fundamental facility planning problem with various applications in transportation, logistics, and telecommunication systems. Due to strategic nature of the HLP, considering uncertainty and the associated risks is of high practical importance in designing hub networks. This paper addresses a risk-averse single allocation HLP, where the traffic volume between the origin–destination (OD) pairs is considered to be uncertain. The uncertainty in demands is captured by a finite set of scenarios, and a flow-dependent economies of scale scheme is used for transportation costs, modeled as a piece-wise concave function of flow on all network arcs. The problem is cast as a risk-averse two-stage stochastic problem using mean-CVaR as the risk measure, and a novel solution approach combining Benders decomposition and scenario grouping is proposed. An extensive set of computational experiments is conducted to study the effect of different input parameters on the optimal solution, and to evaluate the performance of the proposed solution algorithm. Managerial insights are derived and presented based on the obtained results. Highlights: The risk-averse single allocation hub location problem with flow-dependent economies of scale is introduced. Mixed-Integer Programming formulations are proposed for the problem using mean-CVaR as the measure of risk. An efficient solution algorithm based on Benders decomposition and scenario grouping is proposed to solve the problem. Extensive computational study is carried out on the AP data set.