网络化制造模式下基于改进蚁群算法的供应链调度优化研究

为制定网络化制造（networked manufacturing，NM）模式下供应链合作成员间的动态调度策略，构建了由制造商、协同设计商以及客户组成的三层动态调度模型；在生产能力约束、多目标优化约束等制约因素下，采用时间函数、成本函数和延期惩罚函数三个目标函数对调度问题进行描述；使用改进蚁群算法（improved ant colony optimization algorithm，IM-ACO），对调度路径可行解节点添加不同的信息素，并将信息素浓度约束在τ_min和τ_max之间，使得供应链客户个性化需求服务、运作时间、成本等综合收益达到最优. 实例仿真表明本文提出的动态调度优化算法求解具有较快的搜索速度、收敛性好，算法具有较好的稳定性；同时，也表明本文构建调度模型合理，可以为实际生产调度提供优化的策略.

Supply chain scheduling optimization under networked manufacturing based on improved ant colony optimization algorithm

In order to get dynamic scheduling strategy of alliance members based on networked manufacturing (NM), we set a three-layer dynamic scheduling model composed of manufacturer, cooperative designer and customer. Under the constraints of product capability and multi objective optimization, we apply three objective functions: time function, cost function and delay punishment function to depict the scheduling model. In addition, an improved ant colony optimization algorithm (IM-ACO) is employed to solve our presented model. By adding different pheromone concentration to the feasible nodes of scheduling path, we confine pheromone concentration within τ_min and τ_max, thus get optimal benefits regarding individual service of customer, operation time, and cost. An actual case experiment shows that the presented optimization algorithm has fast search speed, better convergence, and good stability. Furthermore, it also proves our designed scheduling model is reasonable, which can provide optimal strategy for real-world scheduling.