Diversified customer demands are raising importance of just-in-time and agile manufacturing much more than before. Accordingly, introduction of mixed-model assembly lines becomes popular to realize the small-lot-multi-kinds production. Since it produces various kinds on the same assembly line, a rational management is of special importance. With this point of view, this study focuses on a sequencing problem of mixed-model assembly line including a paint line as its preceding process. By taking into account the paint line together, reducing work-in-process (WIP) inventory between these heterogeneous lines becomes a major concern of the sequencing problem besides improving production efficiency. Finally, we have formulated the sequencing problem as a bi-objective optimization problem to prevent various line stoppages, and to reduce the volume of WIP inventory simultaneously. Then we have proposed a practical method for the multi-objective analysis. For this purpose, we applied the weighting method to derive the Pareto front. Actually, the resulting problem is solved by a meta-heuristic method like SA (Simulated Annealing). Through numerical experiments, we verified the validity of the proposed approach, and discussed the significance of trade-off analysis between the conflicting objectives.

According to diversified customer demands and global competition, introduction of mixed-model assembly lines becomes popular to realize the small-lot-multi-kinds production in a rational way. For recent years, we have been studying a sequencing problem of mixed-model assembly line that is operated under continuous and leveling production and includes a lot production line as its preceding process. By taking into account the difference of operation manners in both lines together, we have formulated the sequencing problems as a bi-objective optimization problem. It aims to prevent various line stoppages, and to reduce volume of WIP inventory simultaneously. Based on such formulation, this study concerns with the multi-objective analysis first. Then, we have proposed a two-stage multi-objective optimization method. It tries to apply multi-objective optimization method termed MOON 2R relying on the foregoing multi-objective analysis. Finally, through numerical experiments performed by one of the author as virtual decision makers, we have validated effectiveness of the proposed approach.

To meet higher customer satisfaction and shorter production lead time under rapidly changing demands and global competition, importance of just-in-time and agile manufacturing is raising much more attentions than before. To cope with such circumstances, assembly line is shifting to mixed-model assembly line. Under such situation, it is essential to extend system boundary wider and resolve the problem totally. Moreover, in this study, we pay our attention to the latest production manner applied in car industry and extend our conventional model associated with due dates of finished products. Finally, we have formulated a bi-objective optimization problem that aims at reducing total sum of tardiness and total volume of inventories at the same time. Besides the usual one-through approach to solve the resulting problem, we adopt a two stage approach associated with multi-objective analysis. To reveal some properties of the proposed approaches, we provide a case study and discuss effectiveness of the method and usefulness of the results.