An Effective Grouping Evolution Strategy Algorithm Enhanced with Heuristic Methods for Assembly Line Balancing Problem

Abstract

Assembly line balancing problem (ALBP) is an allocation of given tasks to the workstations in a way that the number of workstations or the idle times of workstations get minimized. ALBP is a well-known problem in mass-production systems with high production volume and low diversity. In division and grouping allocation problems like ALBP, metaheuristic algorithms based on group structure, i.e. grouping genetic algorithm, are more efficient. The aim of this study is developing a new solution procedure to minimize the number of workstations for a given cycle time based on grouping evolution strategy. A modification of the ranked positional weight method is proposed to construct the initial solution, and a new heuristic method based on a modified version of the COMSOAL method is provided to tighten the solution after performing the mutation operator. Different strategies based on line efficiency and line smoothness indexes are considered to select the best result and transfer it to the next generation. Moreover, to evaluate the performance of proposed algorithms, some well-known standard test problems are utilized. Computational results indicate that the proposed solution algorithm performs efficiently and can obtain the optimal global solution in most of the high dimensional problems.