عنوان مقاله [English]
The hub location problem (HLP) is one of the most important and widely used issues in telecommunication and transportation (freight and passenger) network design. Hub location problem deals with locating the hub facilities in the network and determine the pattern based on the non-hub nodes assignment to each hub so that a specific objective function is optimized. Hubs are intermediate facilities that perform a set of tasks such as consolidation, break-bulk, sorting, etc. In other words, the traffic flows (cargo, passengers, or data) in the network rather than being sent directly from their origins to their destinations, are routed via these intermediate facilities. Established hubs in these networks can be disrupted because of events and natural disasters or deliberate disturbances during their use and in such a case an enormous cost is imposed on the operating companies. Therefore, it is crucial to have a suitable plan for reducing destructive effects of disrupted hubs in the network. In this study, an uncapacitated single allocation hub location problem under hub disruption is considered. It is assumed that every open hub in the network can fail and become unavailable after installation, in which case, the customers originally assigned to that hub, are either reassigned to other operational hubs or they do not receive service for which a penalty must be paid. The problem has been modeled as a two-stage stochastic program in which the
decisions on hub locations are made in the first phase. In second phase when disruption scenario has occurred, the allocation of non-hub nodes to hubs takes place in second phase with regard to the operational hubs. A hybrid metaheuristic algorithm based on the adaptive large neighborhood search (ALNS) and simulated annealing (SA) is proposed for solving it. Extensive computational experiments based on the CAB and TR data sets are conducted. Results show the high efficiency of the proposed solution method.