考虑合格率和返工合格率的多阶段MTO生产系统投产量决策

Decision-making of planning quantity put into production based on eligibility-rate and rework eligibility-rate for multi-stage system with make-to-order mode

摘要:

按量交货是订单式生产(MTO)企业赢得客户的基本条件,投产过多,浪费资源,投产不足,不能满足客户需求。因此,投产量决策成为订单式生产(MTO)企业亟需解决的难题。合格率是影响投产量大小的重要原因,同时返工合格率不容忽视。考虑合格率和返工合格率等因素,以期望损失最小化为目标,建立了多阶段MTO生产系统投产量决策模型,给出了合格率和返工率都为常量、离散随机变量或连续随机变量三种情况的最优投产量表达式,通过算例分析,验证了模型的可行性,并得出提高合格率和返工合格率,减少合格率离散幅度,是企业减少期望损失的关键。

Abstract:

The mold of Make-To-Order are widely used by manufacturing enterprises because it can react quickly to orders, produce flexibly, meet the diverse needs of individual customers, reduce stocks. But, manufacturing enterprises with Make-To-Order mode face enormous challenges because of the strict delivery date and quantity. The quantity is essential requirement for manufacturing enterprises with Make-To-Order mode to win customers. If output is more than the customer’s order, enterprise will waste resources, otherwise, it will be punish. Thus, the planning quantity put into production is the urgent problem needed to solve for enterprises. Eligibility-rate is the important reason affecting the planning quantity put into production, and rework eligibility-rate can’t be ignored. Considering eligibility-rate and rework eligibility-rate and so on, the paper proposed a decision-making mold of planning quantity put-into-production for multi-stage manufacturing enterprises with Make-To-Order mode, which aimed at minimize the expected loss and carried out the optimal expression of planning quantity put into production by derivation in the assumptions of eligibility-rate and rework eligibility-rate both are constants or discrete random variables or continuous random variables, and verified the feasibility of the model by numerical analysis, then, the result showed that when eligibility-rate and rework eligibility-rate both are discrete random variables, the greater the degree of dispersion is, the larger the expected loss is. when eligibility-rate and rework eligibility-rate both are normal distribution, the greater the degree of dispersion is, the larger the expected loss is, and the more the planning quantity put into production is; the more the punishment cost of per unit of product is, the larger the expected loss is, and the more the planning quantity put into production is; the small the mean is, the more the planning quantity put into production is. Thus, we get the conclusion that improving the eligibility-rate and rework eligibility-rate and reducing the discrete amplitudes are the key to reduce the expected loss.