A comparative analysis of meta-heuristic methods on disassembly line balancing problem with stochastic time

Abstract

The balancing of the disassembly line directly affects the productivity of the disassembly process. The disassembly line balancing (DLB) problem can be determined as assigning the tasks to serial workstations to optimize some performance measures like number of workstations, cycle time, removing hazardous parts earlier, etc. The aim of the paper is to develop an efficient heuristic algorithm to minimize the number of workstations under a pre-known cycle time. In this paper, a genetic algorithm (GA) and a constructive heuristic based on the Dijkstra algorithm is proposed to solve the DLB problem with stochastic task times that is caused by the nature of disassembly operation. The proposed algorithms are tested on benchmark problems and compared with the results of the piecewise-linear model (PLM) and simulated annealing (SA). The average relative percentage deviation is applied to transfer the obtained number of workstations. The results obtained by GA are clearly superior in all tests problem according to average relative percentage deviation. Moreover, the proposed constructive heuristic based on the Dijkstra algorithm is also superior to PLM and SA algorithm with respect to number of workstations and the computational times. The proposed approaches can be a very competitive and promising tool for further research in DLB literature and real cases in industries according to test results. Disassembly lines which need less time or number of workstations for balancing may be simply designed by the proposed techniques.