Optimal assembly line balancing using tabu search with partial random permutation technique

Abstract

A new hybrid intelligent method for solving the assembly line balancing (ALB) problems is proposed in this paper. The tabu search (TS) method, one of the most powerful AI search techniques, and the partial random permutation (PRP) technique are employed to identify and provide solutions for the ALB problems. With the proposed method, the workload variance is set as the objective function of the search process. The TS method is used to address the number of tasks assigned for each workstation, while the PRP technique is conducted to assign the sequence of tasks for each workstation according to precedence constraints. The proposed method is tested against three benchmark ALB problems and one real-world ALB problem from a survey of literature. From the simulation results, it was found that the proposed method is capable of producing very satisfactory solutions. The maximum reduction of the workload variance is of 96.73% when comparing with the conventional COMSOAL method. It can be concluded that the proposed method is an alternative potential algorithm to solve the ALB problems.