Affiliations: [a] School of Industrial Engineering and Center of Excellence for Intelligence Based Experimental Mechanics, College of Engineering, University of Tehran, Tehran, Iran | [b] LCFC Laboratory, Arts et Métier Paris Tech, Metz, France | [c] DISP Laboratory, INSA-Lyon, Villeurbanne Cedex, France
Correspondence:
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Corresponding author. Reza Tavakkoli-Moghaddam, Professor, School of Industrial Engineering and Center of Excellence for Intelligence Based Experimental Mechanics, College of Engineering, University of Tehran, Tehran, Iran. Tel.: +98 2182084183; Fax: +98 2188013102; E-mail: tavakoli@ut.ac.ir.
Abstract: A tree hub location problem (THLP) is a recently introduced extension of the classical hub location problem with an incomplete graph. The aim of this problem is to design a network more economic and practical. This paper presents a new multi-objective model to design a multi-modal tree hub location network under uncertainty. For this purpose, a fuzzy approach is applied to cope with the inherent uncertainty of input data in the THLP. One important issue, which is recently introduced in transportation network, is the amount of fuel consumption effected on economic and environmental problems, In this model, the amount of fuel consumption used in a transportation sector are accounted and the effect of road and vehicle types on consumed fuel in the THLP are studied. This model allows having different transportation modes between hubs and a set of capacity levels for each potential hub so that only one of them can be chosen. The objectives of this model include the minimization of energy consumption and the minimization of transportation costs and fixed costs of locating hubs and hub links. To solve the model, a multi-objective imperialist competitive algorithm (MOICA) is proposed to obtain the Pareto-optimal solutions of the problem. Furthermore, the performance of this algorithm is compared with non-dominated sorting genetic algorithm (NSGA-II).
Keywords: Multi-modal tree hub location problem, energy consumption, fuzzy approach, meta-heuristics
