摘要
面对大规模定制化订单,既要实现供应链利润最大,又需确保供应链平滑运营,如何进行订单决策成为关键问题之一。基于云制造供应链模式,根据仅考虑企业产能大小与兼而考虑企业产能利用(即产能波动率)两种情形,分别设计两种订单决策方式,并构建订单决策优化模型。进而结合双目标Pareto优化思想,参照分层法订单分批思路设计蚁群优化算法(ACO)和模型求解。仿真试验发现:(1)两种订单决策方式下供应链企业生产能力、生产成本及利润变化方向与订单数量的变化方向基本一致,但是兼而考虑产能波动率的订单决策方式相比而言各项指标的变化更平滑。(2)两种订单决策方式下订单数量对供应链利润均产生较大的影响,随着订单数量不断增加,供应链利润不断增大,在达到最大值后逐渐降低。(3)当订单数量达到某阈值之后,兼而考虑产能波动率的订单决策方式下供应链利润更高,订单类型则更偏向于订单价格更高的时间敏感型,同时订单交货期更短、订单生产成本增长率更低。(4)按照同样的订单分批方式并从订单生产规划甘特图上显示,采用订单分批思路下的ACO算法求解效果和效率上均优于常用的SPEA和GA-SA算法。
Faced with mass customization orders, it can not only maximize the profit of the supply chain, but also ensure the smooth operation. Based on the cloud manufacturing supply chain mode, two kinds of order decision-making methods are designed and the order decision optimization model is built according to the two cases of considering only the size of enterprise capacity or both that with the utilization of enterprise capacity(i.e. capacity fluctuation). Combined with the idea of Bi objective Pareto optimization, the ant colony optimization algorithm is designed to resolve the model according to the idea of order batching. The simulation results show that:(1) The change direction of production capacity, production cost and profit is basically the same under the two kinds of order decision-making methods, but the change of each index of the order decision-making method considering capacity fluctuation is smoother.(2) Under all the two order decision-making methods, the order quantity has a great impact on the profit of the supply chain;With the increase of the order quantity, the profit of the supply chain first increases and then decreases after reaching the maximum.(3) When the order quantity reaches a threshold, the order decision mode which considers the capacity fluctuation makes the profit of the supply chain higher, it’s order type is more time sensitive with higher order price, the order delivery time is shorter and the growth rate of order production cost is lower.(4) According to the same order batching method and the Gantt chart of order production planning, ACO algorithm is better than SPEA and GA-SA algorithm in solving effect and efficiency.
作者
周兴建
黎继子
戴金山
姜文可
ZHOU Xing-jian;LI Ji-zi;DAI Jin-shan;JIANG Wen-ke(Postdoctoral Research Station in Management Science and Engineering,Nanchang University.Nanchang 330031,China;School of Management,Wuhan Textile University,Wuhan 430200,China;School of Logistics Engineering,Wuhan Technology University,Wuhan 430063.China)
出处
《系统工程》
北大核心
2021年第5期81-91,共11页
Systems Engineering
基金
国家自然科学基金地区科学基金资助项目(72062019)
国家自然科学基金面上项目(71872076)
江西省博士后科研项目择优资助项目(2019KY13)。
关键词
云制造供应链
大规模定制
订单决策
订单分批
ACO算法
Cloud Manufacturing Supply Chain
Mass Customization
Order Decision
Order Batching
Ant Colony Optimization
