Optimization of Multi-Manned Assembly Line Balancing with Workload Smoothing using Simulated Annealing Based Hyper-Heuristic
DOI:
https://doi.org/10.9744/jti.28.2.103%20-%20116Keywords:
Multi-manned ALBP, Workload Smoothing, Simulated Annealing Based Hyper-HeuristicAbstract
In most cases, an assembly line with multi-manned configuration will increase efficiency in productivity. However, the complexity of multi-manned assembly can sometimes lead to bottlenecks rather than improvements in productivity. The smoothness index is a crucial performance indicator in the assembly line balancing problem, as uneven smoothness can create bottlenecks and disparities among workers; unfortunately, it is often neglected. This study developed a model that represents the multi-manned assembly line balancing problem that considers workload smoothing. Although an exact method may give the most optimal answer, due to the NP hard nature of the problem, the exact method becomes infeasible as it will take time to solve. Although in some cases, metaheuristic or hyper-heuristic methods may not guarantee the best solution quality, a near optimal solution can be given in a more reasonable time. Data from real-world cases of assembly lines and benchmark datasets were used to test the model and examine the algorithm's performance. It was found that the hyper-heuristic method based on simulated annealing could find an optimal solution for the number of workers and stations, although it required a slightly longer computation time compared to pure metaheuristic methods.
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