Multi-objective assembly line balancing via adaptive tabu search method with partial random permutation technique

Abstract

This paper proposes a novel intelligent approach for solving the assembly line balancing (ALB) problems. The adaptive tabu search (ATS) method and the partial random permutation (PRP) technique are combined to provide optimal solutions for the ALB problems. In this work, the ATS is used to address the number of tasks assigned for each workstation, while the PRP is conducted to assign the sequence of tasks for each workstation according to precedence constraints. The multiple objectives including the workload variance, the idle time, and the line efficiency, are proposed and set as the objective function. The proposed approach is tested against three benchmark ALB problems and one real-world ALB problem. Obtained results are compared with results obtained from the single-objective approach. As results, the proposed multiple-objective approach based on the ATS and the PRP is capable of producing solutions superior to the single-objective.