Abstract
In this paper a multi-objective vehicle routing problem (MOVRP) with the criteria being the total distance and the utilization of the vehicle space is considered. Two methods were developed to decrease the execution time of the main part of the algorithm – the neighborhood search procedure. First method, an accelerator, is defined in order to reduce the computational complexity of the algorithm from O(n 3) to O(n 2). The second method utilizes multiple threads of execution to speedup the neighborhood search. Both methods were applied to tabu search metaheuristic and tested against the basic version of the algorithm. In result, we concluded that the enhanced version allows for significant reduction of execution time (2500 times for 5000 clients) that scales well with the number of clients. Moreover, this allows the enhanced algorithm to find significantly better approximations of the Pareto front in the same time as the original algorithm.
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Baños, R., Ortega, J., Gil, C., Márquez, A.L., de Toro, F.: A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows. Computers & Industrial Engineering (65), 286–296 (2013)
Wodecki, M., Bożejko, W., Karpiński, M., Pacut, M.: Multi-GPU parallel memetic algorithm for capacitated Vehicle Routing Problem. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) PPAM 2013, Part II. LNCS, vol. 8385, pp. 207–214. Springer, Heidelberg (2014)
Bożejko, W., Pempera, J., Smutnicki, C.: Parallel tabu search algorithm for the hybrid flow shop problem. Computers & Industrial Engineering (65), 466–474 (2013)
Crainic, T.G.: Parallel Solution Methods for Vehicle Routing Problems, The Vehicle Routing Problem: Latest Advances and New Challenges. Operations Research/Computer Science Interfaces (43), 171–198 (2008)
Diego, F.J., Gómez, E.M., Ortega-Mier, M., García-Sánchez, A.: Parallel CUDA Architecture for Solving de VRP with ACO. Industrial Engineering: Innovative Networks, 385–393 (2012)
Garcia-Najera, A., Bullinaria, J.A.: An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows. Computers & Operations Research (38), 287–300 (2011)
Ghoseiri, K., Ghannadpour, S.F.: Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm. Applied Soft Computing (10), 1096–1107 (2010)
Grandinetti, L., Guerriero, F., Laganá, D., Pisacane, O.: An optimization-based heuristic for the multi-objective undirected capacitated arc routing problem. Computers & Operations Research (39), 2300–2309 (2012)
Guerriero, F., Surace, R., Loscrí, V., Natalizio, E.: A multi-objective approach for unmanned aerial vehicle routing problem with soft time windows constraints. Applied Mathematical Modelling (38), 839–852 (2014)
Jagiełło, S., Żelazny, D.: Solving Multi-criteria Vehicle Routing Problem by Parallel Tabu Search on GPU. Procedia Computer Science (18), 2529–2532 (2013)
Moura, A.: A Multi-Objective Genetic Algorithm for the Vehicle Routing with Time Windows and Loading Problem. Intelligent Decision Support, 187–201 (2008)
Wang, C.-H., Li, C.-H.: Optimization of an established multi-objective delivering problem by an improved hybrid algorithm. Expert Systems with Applications (38), 4361–4367 (2011)
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Jagiełło, S., Rudy, J., Żelazny, D. (2015). Acceleration of Neighborhood Evaluation for a Multi-objective Vehicle Routing. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9120. Springer, Cham. https://doi.org/10.1007/978-3-319-19369-4_19
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DOI: https://doi.org/10.1007/978-3-319-19369-4_19
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