Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints

Abstract

Mixed-model assembly line balancing problem (MMALBP) is an NP-hard problem which requires an effective algorithm for solution. In this study, an assessment of metaheuristic algorithms to optimize MMALBP is conducted using four popular metaheuristics for this problem, namely Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Genetic Algorithm (GA). Three categories of test problem (small, medium, and large) were used ranging from 8 to 100 number of tasks. For computational experiment, MATLAB software is used in investigate the metaheuristic algorithms performances to optimize the designated objective functions. The results reveal that ACO algorithm performed better in term of finding the best fitness functions when dealing with a large number of tasks. Averagely, it has produces better solution quality than PSO by 5.82%, GA by 9.80%, and SA by 7.66%. However, PSO more superior in term of processing time compared to ACO by 29.25%, GA by 40.54%, and SA by 73.23%. Hence, future research directions such as using the actual manufacturing assembly line data to test the algorithm performances are likely to happen.