Minimizing Stoppage Cost of an Assembly Line Using Genetic Algorithm

This paper presents a nonlinear mixed-integer programming model to minimize the stoppage cost of mixed-model assembly lines. Nowadays, most manufacturing firms employ this type of line due to the increasing varieties of products in their attempts to quickly respond to diversified customer demands. Advancement of new technologies, competitiveness, diversification of products, and large customer demand have encouraged practitioners to use different methods of improving production lines. Minimizing line stoppage is regarded as a main factor in determining the sequence of processing products. Line stoppage results in idleness of operators and machines, reduced throughput, increased overhead costs, and decreased overall productivity. Due to the complexity of the model proposed, which belongs to a class of NP-hard problems, a meta-heuristic method based on a genetic algorithm (GA) is proposed to obtain near-optimal solutions in reasonable time, especially for large-scale problems. To show the efficiency of the proposed GA, the computational results are compared with those obtained by the Lingo software.