基于量子粒子群算法的物流配送中心选址
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TP301.6

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Research on Location of Logistics Distribution CenterBased on Quantum Particle Swarm Optimization
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武汉船舶职业技术学院计算机信息技术学院

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    摘要:

    在物流系统网络中,物流配送中心地址的优化选择不但能够高效及时地完成物资的配送,而且能使得配送成本和仓储成本等运营成本最小化,显著提高物流管理的效率和能力。针对物流配送中心选址最优解的问题,通常采用经典粒子群算法解决,但其有易早熟收敛和仅能得到局部最优解的缺陷。为了克服此缺点,本文将量子进化算法融入经典粒子群算法中,采用量子理论中独有的叠加态和概率幅特性,粒子最优位置的搜寻采用量子自旋门完成,粒子位置的多样性变异采用量子非门完成,以免出现局部最优解和早熟收敛缺陷。实验结果表明,与经典粒子群算法相比,量子粒子群算法在最优解的搜寻能力和优化效率方面更具有优势,能够优化配送中心的地址选取,从而减少物流运营的总成本,提高物流配送的效率,优化物流管理系统。

    Abstract:

    In the logistics system network, the optimization of the address of the logistics distribution center can not only efficiently and timely complete the distribution of materials, but also minimize the distribution cost, storage cost and other operating costs, significantly improving the efficiency and capacity of logistics management. Classical particle swarm optimization is usually used to solve the problem of optimal location of logistics distribution center, but it has the defects of premature convergence and local optimal solution. Quantum evolutionary algorithm in order to overcome this drawback, this paper will be integrated into the classic particle swarm algorithm, adopt unique superposition of quantum theory and probability amplitude characteristic, the optimal position of particle search using spin quantum gate is complete, the diversity of the particle position variation using quantum gate, avoid a local optimal solution and premature convergence defects. Experimental results show that compared with the classical particle swarm optimization algorithm, the quantum particle swarm optimization algorithm has more advantages in the search ability and optimization efficiency of the optimal solution, and can optimize the address selection of distribution center, thus reducing the total cost of logistics operation, improving the efficiency of logistics distribution, and optimizing the logistics management system.

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引用本文

生力军. 基于量子粒子群算法的物流配送中心选址[J]. 科学技术与工程, 2019, 19(11): .
SHENG Li-jun. Research on Location of Logistics Distribution CenterBased on Quantum Particle Swarm Optimization[J]. Science Technology and Engineering,2019,19(11).

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  • 收稿日期:2019-01-14
  • 最后修改日期:2019-02-14
  • 录用日期:2019-02-18
  • 在线发布日期: 2019-04-25
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