Tabu search with simulated annealing for solving a location–protection–disruption in hub network

Abstract

Nowadays, designing a reliable hub network has become a critical issue in the process of transporting goods from an origin to a destination. Due to intentional disasters, both location and protection of hub play a key role in satisfying the demands and ensuring network reliability. This study tries to model the impact of the number of hubs opened, allocation of defensive resources, and the risk of disruptions on the configuration of the hub network. This model aims is to minimize the total transportation cost via the primary and backup hubs subject to the installed hubs, allocation of defensive resources, and the risk of disruptions. The formulation with the location of hubs, allocation of protection budget, flow routing between two nodes in origin–destination via the primary and backup hubs, and hub failure probabilities have not been remarked in the literature.As the problem is an NP-hard, the performance of some metaheuristic algorithms called tabu search (TS), simulated annealing (SA), variable neighborhood search (VNS), imperialist competitive algorithm (ICA) and genetic algorithm (GA) is investigated to solve instances of problem with 50 nodes and 5, 10, and 15 hubs. Computational results show that the SA and TS algorithms are superior to existing metaheuristic methods to the novel problem based on solution accuracy and computational time, respectively. Additionally, this study presents a fast and robust hybrid approach that combines the advantages of TS and SA. The proposed algorithm has been successfully used to solve a large number of instances of this problem via sensitivity analysis and instances from the Turkish Postal data set. Several experimental results indicate the applicability of the new model and the advantage of the new hybrid method compared to other metaheuristic algorithms concerning decision and execution time.