Strategic Robust Mixed Model Assembly Line Balancing Based on Scenario Planning  

作　　者：徐炜达 肖田元 

机构地区：[1]National Laboratory for Information Science and Technology,National Computer Integrated Manufacturing Systems Engineering Research Center,Department of Automation,Tsinghua University

出　　处：《Tsinghua Science and Technology》2011年第3期308-314,共7页清华大学学报（自然科学版（英文版）

基　　金：Supported by the National High-Tech Research Development (863) Program of China (No.2006AA04Z160)

摘　　要：Assembly line balancing involves assigning a series of task elements to uniform sequential stations with certain restrictions. Decision makers often discover that a task assignment which is optimal with respect to a deterministic or stochastic/fuzzy model yields quite poor performance in reality. In real environments, assembly line balancing robustness is a more appropriate decision selection guide. A robust model based on the α worst case scenario is developed to compensate for the drawbacks of traditional robust criteria. A robust genetic algorithm is used to solve the problem. Comprehensive computational experiments to study the effect of the solution procedure show that the model generates more flexible robust solutions. Careful tuning the value of α allows the decision maker to balance robustness and conservativeness of as- sembly line task element assignments.Assembly line balancing involves assigning a series of task elements to uniform sequential stations with certain restrictions. Decision makers often discover that a task assignment which is optimal with respect to a deterministic or stochastic/fuzzy model yields quite poor performance in reality. In real environments, assembly line balancing robustness is a more appropriate decision selection guide. A robust model based on the α worst case scenario is developed to compensate for the drawbacks of traditional robust criteria. A robust genetic algorithm is used to solve the problem. Comprehensive computational experiments to study the effect of the solution procedure show that the model generates more flexible robust solutions. Careful tuning the value of α allows the decision maker to balance robustness and conservativeness of as- sembly line task element assignments.

关 键 词：mixed model assembly line balancing ROBUST scenario planning genetic algorithm 

分 类 号：TH186[机械工程—机械制造及自动化]