Abstract

Assembly line balancing (ALB) is a well-known combinatorial optimization problem in production and operations management area. Due to the NP-hard nature of the ALB problem, many attempts have been made to solve the problem efficiently. In this study, biologically inspired evolutionary computing tool which is genetic algorithm (GA) is adopted to solve the ALB problem with the objective of minimizing the idle time in the workstation. The key issue in solving ALB is how to generate a feasible task sequence which does not violate the precedence constraints. This task sequencing is a vital work to be solved prior assigning tasks to workstation. In order to generate only feasible solution, a repairing strategy based topological sort is integrated in the GA procedure. The ALB test problems benchmarked from the literature are used in the study and the computational results show that the proposed approach is capable to obtain feasible solution with minimum idle time for a simple model assembly line.