Multi-period hub location problems in transportation

• Published in: Transportation research. Part E, Logistics and transportation review Vol. 75; pp. 67 - 94
• Main Authors: Gelareh, Shahin, Neamatian Monemi, Rahimeh, Nickel, Stefan
• Format: Journal Article
• Language: English
• Published: Exeter Elsevier India Pvt Ltd 01.03.2015; Elsevier Sequoia S.A; Elsevier
• Subjects: Algorithms; Benders decomposition; Budgeting; Computer Science; Contracts; Decision making models; Heuristic; Horizon; Hub location problems; Location analysis; Mathematical models; Mathematics; Meta-heuristic; Multi-period planning; Ports; Route optimization; Studies; Transport; Transportation; Transportation problem (Operations research); Meta-heuristic; Benders decomposition; Multi-period planning; Hub location problems
• ISSN: 1366-5545; 1878-5794
• DOI: 10.1016/j.tre.2014.12.016

•A novel mathematical model for the multi-period hub location with budget constraints is proposed.•A very efficient decomposition scheme is proposed.•A very efficient metaheuristic is developed. Many transport service providers operate on hub-and-spoke network structures. Major operators may have several dedicated hub facilities that are leased for a time horizon rather than being owned or constructed. For a given discrete planning horizon, service providers must decide on the location of the hub ports (i.e. terminals), the period when the lease contract starts, the period when the existing contracts must be terminated and the flow routing over the entire planning horizon so as to minimize the total operational cost. Thus, we propose a mathematical model for a Multi-period Uncapacitated Multiple Allocation Hub Location Problem with Budget Constraint. The proposed model incorporates several features of practice, particularly from maritime and land transport practices. We also propose a meta-heuristic solution algorithm that produces high-quality solutions in a reasonable amount of time. By exploiting the decomposable structure of the model, we extended a Benders decomposition approach by proposing several improvements. Extensive computational experiments confirm the efficiency of the proposed methods and also show its limitations.