Abstract
In this study, a new genetic algorithm is developed for solving multi-objective single-model assembly line balancing problems. The proposed genetic algorithm is called multiple-assignment genetic algorithm (MA-GA) which presents a new approach of tasks assignment. In addition to the forward assignment approach, backward and bidirectional assignment approaches are used with genetic algorithms to develop alternative assignment procedures that can result in improving the potential of finding better solutions. This approach has not been used before along with GAs in solving assembly line balancing (ALB) problems. The objectives considered here are minimization of number of workstations, maximization of assembly line efficiency, and minimization of workload variation between workstations. The performance of the proposed algorithm MA-GA was compared with GAs that only use the forward assignment approach called Forward-GAs. The proposed MA-GA has shown improved results in assembly line efficiency and workload variation between workstations in many test problems taken from the literature while achieving the optimal number of workstations in all test problems.
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