Opposition-based learning for competitive hub location: A bi-objective biogeography-based optimization algorithm

doi:10.1016/j.knosys.2017.04.017

2017-07-01

PRE

Amir Hossein Niknamfar *

Seyed Taghi Akhavan Niaki

Seyed Armin Akhavan Niaki

DOI：10.1016/j.knosys.2017.04.017

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摘要

En 中文

This paper introduces a new hub-and-center transportation network problem for a new company competing against an operating company. The new company intends to locate p hubs and assign the center nodes to the located hubs in order to form origin-destination pairs. It desires not only to maximize the total captured flow in the market but also aims to minimize the total transportation cost. Three competition rules are established between the companies which must be abided. According to the competition rules, the new company can capture the full percentage of the traffic in each origin-destination pair if its transportation cost for each route is significantly less than of the competitor. If its transportation cost for each route is not significantly less than one of the competitors, only a certain percentage of the traffic can be captured. A bi-objective optimization model is proposed for the hub location, problem on hand under a competitive environment. As the problem is shown to be NP-hard, a novel meta-heuristic algorithm called multi-objective biogeography-based optimization is developed. As there is no benchmark in the literature, a popular non-dominated sorting algorithm is utilized to validate the results obtained. Moreover, to enhance the performance of the proposed Pareto-based algorithms, this paper intends to develop a binary opposition-based learning as a diversity mechanism for both algorithms. The algorithms are tuned to solve the problem, based on which their performances are compared, ranked, and analyzed statistically. Finally, the applicability of the proposed approach and the solution methodologies are demonstrated in three steps. (C) 2017 Elsevier B.V. All rights reserved.

Keyword:

Competitive hub location

Evolutionary computations

Binary opposition-based learning

Multi-objective biogeography-based optimization

Non-dominated sorting genetic algorithm