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In practice, modeling an assembly system often requires assigning a set of operations to a set of workstations. The aim is to optimize some performance indices of an assembly line. This assignation is usually a tedious design procedure so a significant amount of manpower is required to obtain a good work plan. Poor assembly planning may significantly increase the cost of products and reduce productivity. However, these optimization problems fall into the class of NP-hard problems. Finding an optimal solution in an acceptable time is difficult, even using a powerful computer. This study presents a hybrid genetic algorithm approach to the problems of assembly planning with various objectives, including minimizing cycle time, maximizing workload smoothness, minimizing the frequency of tool change, minimizing the number of tools and machines used, and minimizing the complexity of assembly sequences. A self-tuning method was developed to correct infeasible chromosomes. Several examples were employed to illustrate the proposed approach. Experimental results indicated that the proposed method can efficiently yield many alternative assembly plans to support the design and operation of a flexible assembly system.

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