Abstract: Generally, chaos production planning and back order issues are mainly caused by the incomplete kit of parts owing to unreliable supply quantity in multi-variety and small batch production environment. In this paper, we study the integrated order quantity allocation and order scheduling problem, considering the situation of allowing shortage and partial backlogging with price discount. The objective functions in this research are to minimize the expected purchasing cost and to minimize the cost related to order scheduling. Based on linear weighted sum method, the model was transferred into a single objective optimization model. The solution space is huge and traditional mathematical programming method can not solve the model directly, from the perspective of enhancing the search performance, an improved particle swarm optimization algorithm was designed by incorporating local search scheme based on multi-neighborhood structure and mutation scheme based on random and reinitiate population. The feasibility and effectiveness of the model and algorithm was verified through numerical examples. The results show that, compared to the traditional decentralized decision making, the model in this paper has more advantage to reducing overall cost and the improved algorithm shows better searching performance, which could provide a reference for enterprises’ operating decisions under supply uncertainty.

Key words: assembly manufacturing enterprise, supply uncertainty, order quantity allocation, order scheduling, particle swarm optimization

摘要： 针对供应商交货数量不确定环境下,多品种小批量装配型制造企业因生产物料不配套造成生产计划不可行甚至客户订单拖期的问题,从企业运作整体出发,考虑订货量分配决策对订单生产和交货的影响,以最小化采购成本和最小化订单排产相关成本为优化目标,在允许零部件拖期交货且供应商提供拖期价格折扣条件下,建立订货量分配与订单排产联合优化模型。针对可行解空间巨大、传统数学规划方法难以求解的问题,从增强搜索性能角度出发,设计基于自定义邻域搜索算子的局部搜索机制和基于随机与种群重构变异机制的改进粒子群算法的模型求解策略。通过应用实例对本文模型和算法进行了有效性验证和灵敏度分析,结果表明,相比于传统的分散决策方案,本文模型能够有效降低整体成本水平,引入的改进机制能够显著提升算法搜索性能,为企业供应风险下的运营决策制定提供理论参考。

关键词: 装配型制造企业, 供应不确定, 订货量分配, 订单排产, 粒子群优化算法