Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Optimization Method of Knapsack Problem Based on BPSO-SA in Logistics Distribution

  • Zhang, Yan (Shool of Economics and Management, Chongqing, University of Posts and Telecommunications) ;
  • Wu, Tengyu (Shool of Economics and Management, Chongqing, University of Posts and Telecommunications) ;
  • Ding, Xiaoyue (Shool of Economics and Management, Chongqing, University of Posts and Telecommunications)
  • Received : 2022.02.15
  • Accepted : 2022.05.23
  • Published : 2022.10.31
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Abstract

In modern logistics, the effective use of the vehicle volume and loading capacity will reduce the logistic cost. Many heuristic algorithms can solve this knapsack problem, but lots of these algorithms have a drawback, that is, they often fall into locally optimal solutions. A fusion optimization method based on simulated annealing algorithm (SA) and binary particle swarm optimization algorithm (BPSO) is proposed in the paper. We establish a logistics knapsack model of the fusion optimization algorithm. Then, a new model of express logistics simulation system is used for comparing three algorithms. The experiment verifies the effectiveness of the algorithm proposed in this paper. The experimental results show that the use of BPSO-SA algorithm can improve the utilization rate and the load rate of logistics distribution vehicles. So, the number of vehicles used for distribution and the average driving distance will be reduced. The purposes of the logistics knapsack problem optimization are achieved.

Keywords

Acknowledgement

This paper is supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (No. KJQN201900625 and KJQN202000638).

References

  1. J. H. Drake, M. Hyde, K. Ibrahim, and E. Ozcan, "A genetic programming hyper-heuristic for the multidimensional knapsack problem," Kybernetes, vol. 43, no. 9/10, pp. 1500-1511, 2014. https://doi.org/10.1108/K-09-2013-0201
  2. Z. Beheshti, S. M. Shamsuddin, and S. Hasan, "Memetic binary particle swarm optimization for discrete optimization problems," Information Sciences, vol. 299, pp. 58-84, 2015. https://doi.org/10.1016/j.ins.2014.12.016
  3. F. Glover, "Advanced greedy algorithms and surrogate constraint methods for linear and quadratic knapsack and covering problems," European Journal of Operational Research, vol. 230, no. 2, pp. 212-225, 2013. https://doi.org/10.1016/j.ejor.2013.04.010
  4. I. B. Mansour and I. Alaya, "Indicator based ant colony optimization for multi-objective knapsack problem," Procedia Computer Science, vol. 60, pp. 448-457, 2015. https://doi.org/10.1016/j.procs.2015.08.165
  5. S. C. Leung, D. Zhang, C. Zhou, and T. Wu, "A hybrid simulated annealing metaheuristic algorithm for the two-dimensional knapsack packing problem," Computers & Operations Research, vol. 39, no. 1, pp. 64-73, 2012. https://doi.org/10.1016/j.cor.2010.10.022
  6. B. Haddar, M. Khemakhem, H. Rhimi, and H. Chabchoub, "A quantum particle swarm optimization for the 0-1 generalized knapsack sharing problem," Natural Computing, vol. 15, no. 1, pp. 153-164, 2016. https://doi.org/10.1007/s11047-014-9470-5
  7. C. Sachdeva and S. Goel, "An improved approach for solving 0/1 knapsack problem in polynomial time using genetic algorithms," in Proceedings of International Conference on Recent Advances and Innovations in Engineering (ICRAIE), Jaipur, India, 2014, pp. 1-4.
  8. R. Merkle and M. Hellman, "Hiding information and signatures in trapdoor knapsacks," IEEE Transactions on Information Theory, vol. 24, no. 5, pp. 525-530, 1978. https://doi.org/10.1109/TIT.1978.1055927
  9. Y. Li, Y. He, H. Li, X. Guo, and Z. Li, "A binary particle swarm optimization for solving the bounded knapsack problem," in Computational Intelligence and Intelligent Systems. Singapore: Springer, 2018, pp. 50-60.
  10. Y. He, H. Xie, T. L. Wong, and X. Wang, "A novel binary artificial bee colony algorithm for the set-union knapsack problem," Future Generation Computer Systems, vol. 78, pp. 77-86, 2018. https://doi.org/10.1016/j.future.2017.05.044
  11. Y. Zhang, X. Y. Wu, and O. K. Kwon, "Research on Kruskal crossover genetic algorithm for multi-objective logistics distribution path optimization," International Journal of Multimedia and Ubiquitous Engineering, vol. 10, no. 8, pp. 367-378, 2015. https://doi.org/10.14257/ijmue.2015.10.8.36