Car sequencing for mixed-model assembly lines with consideration of changeover complexity

Implementing mass customization can inevitably lead to a large product variety, which makes the assembly process in mixed-model assembly lines (MMALs) very complex. In this paper, the concept of product variety induced changeover complexity, as one major source of uncertainty in mixed-model assembly, is proposed. Three types of changeover complexities measured using information entropy are presented. As the negative impact of changeover complexity on the performance of a MMAL can be reduced by selecting a suitable model sequence, a bi-objective car sequencing problem taking it into account is proposed. The problem is aimed at finding a model sequence with the minimum number of violating sequencing rules as a primary criterion, and the minimum level of total changeover complexity as a secondary criterion. A lexicographic approach based on ant colony optimization (ACO) is applied to solve the problem. Computational experiments show that both objectives can be effectively addressed using the presented ACO algorithm. (C) 2017 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.