Sequencing mixed-model assembly lines with risk-averse stochastic mixed-integer programming

作     者：Guo, Ge Ryan, Sarah M. 

作者机构：Univ Baltimore Dept Informat Syst & Decis Sci 1420 N Charles St Baltimore MD 21201 USA Iowa State Univ Dept Ind & Mfg Syst Engn Ames IA USA 

出 版 物：《INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH》 (国际生产研究杂志)

年 卷 期：2022年第60卷第12期

页      面：3774-3791页

核心收录：

学科分类：12[管理学] 120202[管理学-企业管理（含：财务管理、市场营销、人力资源管理）] 0202[经济学-应用经济学] 02[经济学] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0802[工学-机械工程] 

基　　金：Digital Manufacturing and Design Innovation Institute [15-02-08] 

主　　题：Mixed-model assembly line sequencing part unavailability stochastic mixed-integer programming risk-averse optimisation Progressive Hedging algorithm 

摘      要：Sequencing decisions in mixed-model assembly lines are complicated by various uncertainty factors. This paper addresses a real-life uncertainty factor identified in a manufacturer of large vehicles, by modelling unreliable part delivery and quality. Stochastic optimisation is applied to find sequencing policies that improve the on-time performance of its mixed-model assembly lines. As schedulers have different levels of risk aversion, a risk-averse programme is further presented to protect against the decision maker s chosen fraction of worst scenarios. Computational studies with Progressive Hedging as the solution method, and its lower bounding approach, demonstrate the high quality of resulting sequencing decisions and the time efficiency of the solution method.