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

Ghaffarinasab N., Cavus O., Kara B. Y.

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, cilt.167, ss.32-53, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 167
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.trb.2022.11.008
  • Dergi Adı: TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, International Bibliography of Social Sciences, PASCAL, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, EconLit, Environment Index, INSPEC, Metadex, Pollution Abstracts, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.32-53
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

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.