摘要
针对供应链合作伙伴选择的准确性和效率问题,提出一种基于粒子群和蚁群优化的合作伙伴选择算法。建立基于供应链链节体和连接弧的有向图路径模型,构造多目标规划模型。利用改进的离散型粒子群算法,求取伙伴选择问题的初始解集,构建初始信息素矩阵,通过改进蚁群算法的寻径规则,求取供应链合作伙伴选择问题的最优解。实验结果表明,所提算法有效提高了供应链合作伙伴选择的精度和效率,具有较好的性能。
To improve the accuracy and efficiency of partner selection in the supply chain, we propose a hybrid algorithm of particle swarm and ant colony optimization （PSACO）, establish a directed graph path model based on supply chain nodes and directed arcs, and construct a multi-objective optimization model. A discrete particle swarm optimization （DPSO） is modified so as to obtain initial solutions to the partner selection problem. Then the initial solutions are used to form pheromone-initializing matrix for ant colony optimization （ACO）, which is further modified by redefining its searching strategy to find the optimal solution. Experimental results demonstrate that the proposed PSACO algorithm can achieve more accurate solutions with greater efficiency, and is of better performance.
出处
《计算机工程与科学》
CSCD
北大核心
2016年第5期946-953,共8页
Computer Engineering & Science
基金
上海海事大学校基金(20130464)
上海市基础研究重点项目(15590501800)
关键词
供应链
伙伴选择
粒子群算法
蚁群算法
supply chain
partner selection
particle swarm optimization
ant colony algorithm
