管理工程学报2017，Vol.31Issue(3)：108-116,9.DOI:10.13587/j.cnki.jieem.2017.03.013
考虑需求时序关联性的MTO企业订单选择和调度综合决策
Integrated order selection and scheduling decisions in the MTO environment considering the time series associations
摘要
Abstract
In today's fierce competitive environment,an increasing nnmber of manufacturers are adopting the make-to-order (MTO) production mode in order to satisfy the distinct requirements of customers.As the production capacity of a MTO manufacturer is actually limited,accepting and processing all potential orders may not be a wise decision.Thus,making full use of the limited capacity to achieve its target is an important issue a MTO manufacturer must solve.The issue has also attracted attention from scholars.A series of recent researches focused on order selection and order scheduling problems for MTO manufacturers by considering factors,such as processing time,time series setup times,due-dates,and weighted tardiness.However,the time series associations may also exist in the order of the current planning period and that of the future.The order decision of the current planning horizon not only affects the immediate revenue,but also affects the demand and revenue in the future planning horizon.Therefore,it is necessary to take these time series associations into account when making order decisions.This paper is concerned with the integrated order selection and scheduling decision making problems of MTO manufacturers with a single-machine production facility to manufacture multiple products,where setup times,setup costs and order demands in different planning horizons are time series.Taking time series associations,processing times and capacity constrains into consideration and weighing the revenue of the current and future planning horizons,the paper formulates the problem as a mixed integer programming model to optimize the long-term expected benefit of MTO manufacturers.A dummy order is devised,and some transformation and relaxation techniques are applied in order to make the model easily to be solved.We further develop an algorithm and test its performance by numerical simulation.In the simulation part,it firstly gives simulations and analyses for the order decisions with 30 orders to be handled at different production capacity levels,based on detailed comparisons between the scenarios considering the future revenue and the ones not.Secondly,it analyzes the effectiveness of the algorithm when setup times and costs vary.Finally,the effectiveness and efficiency of the algorithm are proved through simulating problem instances with different order sizes.The simulatien results show that the model formulated and the algorithm developed can be effectively used to solve integrated order selection and scheduling problems for the MTO manufacturer with limited capacity to optimize its long-term expected revenue.It enables the manufacturer to obtain more long-term revenue while guarantee a high rate of order acceptance and capacity utilization.Moreover,it is favorable for the long-term development of the enterprise when considering the time series associations of the orders in the current planning period and in the future ones.The algorithm is effective in solving the problems of different setup times and costs,and different scales.All in all,because of the existence of time series associations in different planning periods' orders,MTO manufacturers should consider not only processing time,setup times,and setup costs,but also the potential future revenue of the current orders when making integrated order selection and scheduling decisions for the current planning period.It may help the manufacturer avoid the short-sighted decision-making and bring higher long-term benefits.The scarcer the manufacturer's capacity is,the more necessary it is to do so.关键词
按订单制造(MTO)/订单选择与调度/时序关联/转换时间/转换成本Key words
Make-To-Order/Integrated order selection and scheduling/Time series associations/Setup time/Setup cost分类
管理科学引用本文复制引用
高华丽,但斌,闫建..考虑需求时序关联性的MTO企业订单选择和调度综合决策[J].管理工程学报,2017,31(3):108-116,9.基金项目
国家自然科学基金资助项目(71272086) （71272086）
国家科技支撑计划资助项目(2012BAF12B09) （2012BAF12B09）
