Volume 23, Issue 2 (IJIEPM 2012)                   2012, 23(2): 161-173 | Back to browse issues page

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Eydi A, Mirakhorli A. Hybrid Heuristic Method Based on Genetic Algorithm for the Hub Covering Problem under Fuzzy Environment. Journal title 2012; 23 (2) :161-173
URL: http://ijiepm.iust.ac.ir/article-1-878-en.html
Assistant Professor in the Faculty of Engineering, University of Kurdistan, Sanandaj, Iran , eydi81@yahoo.com
Abstract:   (11350 Views)
Hub location problem is one of the new issues in location problems. This kind of location problem is widely used in many transportation  and telecommunication networks. Hubs are facilities that serve as transshipment and switching point to consolidate flows at certain locations for transportation,airline and postal systems so they are vital elements of such these networks. The location and number of hubs is an important issue in hub and spoke network design problems because the performance and efficiency of the network is highly depended on number of hubs and hub locations. Hub covering is one the four kind of hub location problems (P-hub median, hub location with fixed cost, P-hub center, hub covering) which has received the least attention in the literature. Objective of hub covering location problem is to find the minimum number of hubs and the assignment of non-hub nodes to selected hubs considering the predefined maximum allowable travel time or travel cost (cover radius). In order to take into account the real world uncertainties such as unpredictable events, this paper formulizes the hub covering location problem under fuzzy environment considering fuzzy travel time and cover radius. This paper employs the technique of fuzzy inequality constraint to convert the presented fuzzy model to its crisp equivalent using possibility measures. Genetic-based heuristic algorithm is applied to solve the problem for large instances. The computational results show that due to uncertainty assumption, number of hubs is increased to satisfy customer demand as soon as possible. CAB and AP data set are used to show the performance and validity of the algorithm.
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