Strategic Robust Mixed Model Assembly Line Balancing Based on Scenario Planning

• Published in: Tsinghua science and technology Vol. 16; no. 3; pp. 308 - 314
• Main Author: 徐炜达 肖田元
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2011; National Laboratory for Information Science and Technology, National Computer Integrated Manufacturing Systems Engineering Research Center, Department of Automation, Tsinghua University, Beijing 100084, China
• Subjects: assembly line balancing; Assembly lines; Decision making; Fuzzy logic; genetic algorithm; Mathematical models; mixed model; robust; Robustness; scenario planning; Stations; Tasks; Tuning; 任务分配; 元素分配; 混合模型; 组装线; 装配生产线; 装配线平衡; 规划; 鲁棒性; genetic algorithm; assembly line balancing; scenario planning; robust; mixed model
• ISSN: 1007-0214; 1878-7606; 1007-0214
• DOI: 10.1016/S1007-0214(11)70045-1

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.