Sequencing mixed-model assembly lines in just-in-time production systems
This thesis proposes a new simulated annealing approach to solve multiple objective sequencing problems in mixed-model assembly lines. Mixed-model assembly lines are a type of production line where a variety of product models similar in product characteristics are assembled. Such an assembly line is increasingly accepted in industry to cope with the recently observed trend of diversification of customer demands. Sequencing problems are important for an efficient use of mixed-model assembly lines. There is a rich of criteria on which to judge sequences of product models in terms of line utilization. We consider three practically important objectives: the goal of minimizing the variation of the actual production from the desired production, which is minimizing usage variation, workload smoothing in order to reduce the chance of production delays and line stoppages and minimizing total set-ups cost. A considerate line manager would like to take into account all these factors. These are important for an efficient operation of mixed-model assembly lines. They work efficiently and find good solution in a very short time, even when the size of the problem is too large. The multiple objective sequencing problems is described and its mathematical formulation is provided. Simulated annealing algorithms are designed for near or optimal solutions and find an efficiency frontier of all efficient design configurations for the problem. This approach combines the SA methodology with a specific neighborhood search, which in the case of this study is a "swapping two sequence". Two annealing methods are proposed based on this approach, which differ only in cooling and freezing schedules. This research used correlation to describe the degree of relationship between results obtained by method B and other heuristics method and also for quality of our algorithm ANOVA's of output is constructed to analyse and evaluate the accuracy of the CPU time taken to determine near or optimal solution.