A heuristic-based genetic algorithm for workload smoothing in assembly lines

Workload smoothing in assembly lines has many beneficial features: it established the sense of equity among workers, and, more importantly, contributes to increasing the output. Although assembly line balancing has been studied extensively, workload smoothing as the objective has been relatively neglected in the literature. This study presents a new heuristic procedure based on genetic algorithm to balance an assembly line with the objective of maximizing workload smoothness. To improve the capability of searching good solutions, our genetic algorithm puts emphasis on the utilization of problem-specific information and heuristics in the design of representation scheme and genetic operators. Extensive computational experiments are performed for the algorithm. The advantages of incorporating problem-specific heuristic information into the algorithm are demonstrated. The performance comparison of our genetic algorithm with three existing heuristics and with an existing genetic algorithm is made. The experimental results show that our algorithm outperforms the existing heuristics and the compared genetic algorithm. In many cases, our algorithm also improves cycle time.