Abstract
Cross docking play an indispensable role in streamlining the efficiency and effectiveness of any supply chain operations. Owing to the need to reduce transportation lead time and increase coordination between other supply chain activities such as just-in-time, make-to-order, or merge-in-transit strategies, shortening the total transfer time at cross docking is increasing important. Thus, in this paper we propose a new hybrid metaheuristic for vehicle routing scheduling in cross-docking systems. This new hybrid algorithm incorporates the elements from Particle Swam Optimization, Simulated Annealing and Variable Neighborhood Search to enhance its search capabilities. On view of the fact that the performance of metaheuristic algorithms are considerably influenced by the proper tuning of their parameters, we take advantage of Taguchi’s robust design method to come up with the best parameters of the before-mentioned algorithms. In order to measure the performance of our proposed algorithm, we compared it with the Tabu Search algorithm presented by Lee et al. (Comput Ind Eng 51:247–256, 2006). The computational evaluations clearly support the high performance of our proposed algorithm against other algorithm in the literature.
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Vahdani, B., Tavakkoli-Moghaddam, R., Zandieh, M. et al. Vehicle routing scheduling using an enhanced hybrid optimization approach. J Intell Manuf 23, 759–774 (2012). https://doi.org/10.1007/s10845-010-0427-y
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DOI: https://doi.org/10.1007/s10845-010-0427-y