A new stochastic approach for a reliable p-hub covering location problem. (December 2015)

• Record Type: Journal Article
• Authors: Sadeghi, Marjan; Jolai, Fariborz; Tavakkoli-Moghaddam, Reza; Rahimi, Yaser
• Is Part Of: Computers & industrial engineering. Volume 90(2015)
• Page Start: 371
• Page End: 380
• Publication Date: 2015-12
• Subjects: Hub location problem -- Stochastic programming -- Degradable network -- Capacity reliability -- Meta-heuristics; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2015.10.003
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Considering the road capacity as a TErl random variable. Using the designed capacity as an upper bound of the TErl distribution function. Defining a new parameter for the TErl distribution function that relates with disruption intensity. Considering a specific exceedance probability for each link. Abstract: A hub location problem (HLP) is a fertile research field, in the aspect of interdisciplinary studies, such as transportation, operation research, network design, telecommunication and economics. The location of hub facilities and allocation of non-hub nodes to hubs configure the backbone of HLPs. This study presents a new mathematical model for a reliable HLP by a new stochastic approach to minimize the total transportation cost and obtain maximum flows that the network can carry, when its link capacities are subject to stochastic degradations, as in a form of daily traffic, earthquake, flood, etc. We consider the road capacity reliability as a probability that ensures the maximum network capacity is greater than or equal to the total incoming flow to the network by considering the road capacity as random variable. As a result, this paper assumes that link capacities satisfy in a Truncated Erlang ( TErl ) distribution function. Due to complexity of the HLP, a meta-heuristic algorithm, namely differential evolution (DE) algorithm, is applied on the problem in order to achieve near-optimal solutions. Furthermore, the performance of the proposed algorithm (i.e., DE) is evaluated by the performance of the genetic algorithm (GA) applied on the given problem. Some computational experiments are presented to illustrate the effectiveness of the presented model and proposed algorithm. Finally the conclusion is provided.