Optimization Method of Knapsack Problem Based on BPSO-SA in Logistics Distribution
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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, thatis, they often fall into locally optimal solutions. A fusion optimization method based on simulated annealingalgorithm (SA) and binary particle swarm optimization algorithm (BPSO) is proposed in the paper. Weestablish a logistics knapsack model of the fusion optimization algorithm. Then, a new model of expresslogistics simulation system is used for comparing three algorithms. The experiment verifies the effectivenessof the algorithm proposed in this paper. The experimental results show that the use of BPSO-SA algorithm canimprove the utilization rate and the load rate of logistics distribution vehicles. So, the number of vehicles usedfor distribution and the average driving distance will be reduced. The purposes of the logistics knapsackproblem optimization are achieved.
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, thatis, they often fall into locally optimal solutions. A fusion optimization method based on simulated annealingalgorithm (SA) and binary particle swarm optimization algorithm (BPSO) is proposed in the paper. Weestablish a logistics knapsack model of the fusion optimization algorithm. Then, a new model of expresslogistics simulation system is used for comparing three algorithms. The experiment verifies the effectivenessof the algorithm proposed in this paper. The experimental results show that the use of BPSO-SA algorithm canimprove the utilization rate and the load rate of logistics distribution vehicles. So, the number of vehicles usedfor distribution and the average driving distance will be reduced. The purposes of the logistics knapsackproblem optimization are achieved.
참고문헌 (11)
- J. H. Drake, "A genetic programming hyper-heuristic for the multidimensional knapsack problem", Kybernetes, 43(9, pp.10) (2014), 1500~1511
- Z. Beheshti, "Memetic binary particle swarm optimization for discrete optimization problems", Information Sciences, 299, pp.58~84 (2015)
- F. Glover, "Advanced greedy algorithms and surrogate constraint methods for linear and quadratic knapsack and covering problems", European Journal of Operational Research, 230(2), pp.212~225 (2013)
- I. B. Mansour, "Indicator based ant colony optimization for multi-objective knapsack problem", Procedia Computer Science, 60, pp.448~457 (2015)
- S. C. Leung, "A hybrid simulated annealing metaheuristic algorithm for the two-dimensional knapsack packing problem", Computers & Operations Research, 39(1), pp.64~73 (2012)
- B. Haddar, "A quantum particle swarm optimization for the 0–1 generalized knapsack sharing problem", Natural Computing, 15(1), pp.153~164 (2016)
- C. Sachdeva, "An improved approach for solving 0", 1 knapsack problem in polynomial time using genetic algorithms, 2014, 1~4
- R. Merkle, "Hiding information and signatures in trapdoor knapsacks", IEEE Transactions on Information Theory, 24(5), pp.525~530 (1978)
- Y. Li, "Computational Intelligence and Intelligent Systems", pp.50~60 (2018), Springer
- Y. He, "A novel binary artificial bee colony algorithm for the set-union knapsack problem", Future Generation Computer Systems, 78, pp.77~86 (2018)
- Y. Zhang, "Research on Kruskal crossover genetic algorithm for multi-objective logistics distribution path optimization", International Journal of Multimedia and Ubiquitous Engineering, 10(8), pp.367~378 (2015)