物流配送中心选址问题研究  

Research on the Location of Logistics Distribution Center

作　　者：张平东 马军[1] ZHANG Pingdong;MA Jun(School of Management,Shenyang University of Technology,Shenyang 110870)

机构地区：[1]沈阳工业大学管理学院,沈阳110870

出　　处：《计算机与数字工程》2023年第9期2177-2183,共7页Computer & Digital Engineering

基　　金：教育部人文社会科学基金项目(编号:16YJC630085)资助。

摘　　要：针对于传统算法在处理物流中心选址问题收敛速度慢、寻优精度低等缺陷,提出了粒子群算法和模拟退火算法来解决该问题。粒子群算法通过粒子间集体合作,在解空间中不断搜索、迭代来寻找最优解;模拟退火算法是通过固体退火的过程来求解优化问题。两种算法的引入有效了节约了运输成本,解决了配送最优路径的选址问题。最后,通过仿真实验结果表明,两种算法都可以很好地求解物流中心选址问题,其中在需求点和配送中心规模较小时,应采用粒子群算法求解该问题;在需求点和配送中心规模较大时,应采用模拟退火算法求解该问题,两种算法都具有一定的普遍性和可靠性。Aiming at the shortcomings of traditional algorithms in dealing with logistics center location problem,such as slow convergence speed and low optimization accuracy,particle swarm optimization algorithm and simulated annealing algorithm are proposed to solve this problem.Particle swarm optimization(PSO)searches and iterates continuously in the solution space to find the optimal solution through the collective cooperation between particles.Simulated annealing algorithm solves the optimization problem through the process of solid annealing.The introduction of the two algorithms effectively saves the transportation cost and solves the location problem of the optimal distribution path.Finally,the simulation results show that the two algorithms can solve the logistics center location problem well.When the scale of demand point and distribution center is small,particle swarm optimization algorithm should be used to solve the problem.When the scale of demand point and distribution center is large,simulated annealing algorithm should be used to solve the problem.Both algorithms have certain universality and reliability.

关 键 词：物流 选址问题 粒子群算法 模拟退火算法 最优解 最优路径 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]