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

Mixed-model assembly line balancing problem (MMALBP) is an NP-hard problem whichrequires an effective algorithm for solution. In this study, an assessment of metaheuristic algorithms to optimize MMALBP was conductedby using four popular metaheuristics , 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) wereused,ranging from 8 to 100 tasks.For computational experiment, MATLAB software wasused toinvestigate the metaheuristic algorithmperformances to optimize the designated objective functions. Results revealedthat the ACO algorithm performed better in termsof finding the best fitness functions when dealing with many tasks.Averagely, it producedbetter solution qualitythan PSO by 5.82%, GA by 9.80%, and SA by 7.66%. However, PSO was more superior in termsof processing time as compared to ACO by 29.25%, GA by 40.54%, and SA by 73.23%.Therefore, future research directions,such as by using the actual manufacturing assembly line data to test the algorithm performances,are likely to happen