Affiliations: [a] Research Base of Beijing Modern Manufacturing Development, College of Economics and Management, Beijing University of Technology, China
| [b] School of Reliability and Systems Engineering, Beihang University, China
Correspondence:
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Corresponding author. Zaoli Yang, Research Base of Beijing Modern Manufacturing Development, College of Economics and Management, Beijing University of Technology, China. E-mail: yangzaoli@bjut.edu.cn.
Abstract: Recently, assembly line balancing problem with uncertain task time gains more and more attention in the literature. Task time uncertainty may overload workstations. Uncertain task time attributes were studied in the frameworks of the learning theory, fuzzy theory, and probability theory. In this paper, we use a new method, which is the uncertainty theory, to model the uncertain task time as the historical task time information is unavailable. We incorporate the uncertainty into the constraints of the line balancing type-1 problem and propose two new optimization models. We also derive some useful theorems related to the optimal solutions. Further, we develop an algorithm based on the branch and bound remember algorithm to solve the models. Finally, numerical studies are conducted to illustrate our models and to show the efficiency of the proposed algorithm.
Keywords: Assembly line balancing, uncertainty theory, uncertain programming, uncertain task time attribute
