Abstract
Hubs are critical facilities in the power projection network. Due to the uncertainty factors such as terrorism threats, severe weather, and natural disasters, hub facilities may be disrupted randomly, which could lead to excessive cost or loss in practice. One of the most effective ways to withstand and reduce the impact of disruptions is designing more resilient networks. In this paper, a stochastic programming model is employed for the hub location problem in the presence of random hub failures. A heuristic algorithm based on Monte Carlo method and tabu search is put forward to solve the model. The proposed approach is more general if there are numbers of hubs that would fail even with different failure probability. Compared with the benchmark model, the model which takes the factor of stochastic failure of hubs into account can give a more resilient power projection network.
Highlights
Power projection is a term used in military to refer to the capacity of a state to apply national transportation network to rapidly and effectively deploy and sustain forces in and from multiple dispersed locations to respond to crises, to contribute to deterrence, and to enhance regional stability [1]
A heuristic algorithm based on Monte Carlo method and tabu search is put forward to solve the model
In order to illustrate the importance of considering the possible failure of the hub in the design of the network, the multiple assignment hub location model in the normal state is provided as the benchmark model, and a stochastic programming model considering the probability of hub failure is proposed
Summary
Power projection is a term used in military to refer to the capacity of a state to apply national transportation network to rapidly and effectively deploy and sustain forces in and from multiple dispersed locations to respond to crises, to contribute to deterrence, and to enhance regional stability [1]. Bi et al [2] assumed that the enemy would select the hubs for attack which result in the greatest damage to the network They gave a resilient hub location strategy considering the failure of specific hubs. Azizi et al [21] incorporated hub unavailability into the classical single-allocation p-hub median problem They assumed that once a hub stopped normal operations, the entire demand initially served by this hub was handled by a backup facility. They proposed a mixed integer quadratic programming model and a metaheuristic algorithm. Compared with traditional hub location problems, it is more complex for resilient hub location in power projection network considering random hub failures. The superiority of the proposed approach is illustrated by a computational example compared with the benchmark model
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