Single Model Assembly Line Balancing Using Enhanced Genetic Algorithm

Abstract

Assembly line balancing (ALB) is used in many industries to minimize the number of stations, improve the efficiency and work load balance among stations. Enhanced Genetic Algorithm (EGA) is proposed using precedence preservative crossover and scramble mutation techniques, aiming to minimize two objectives; number of stations as a primary objective and smoothing index as a secondary objective. Benchmark problems were selected from the literature, used to test the efficacy of the algorithm and to compare the results with the well-known algorithm SALOME. The results showed high efficacy while handling two objectives. It outperformed SALOME in work load balance and efficiency. On the other, EGA outperformed the Genetic Algorithm (GA) developed by [1] in minimizing the number of stations.