The Assembly Line Balancing problem is an industrial optimization problem of considerable importance in lean systems. It has been extensively studied in literature through classical optimization methods. However, conventional computing paradigms have not proved practical utility for complex problems. Metaheuristic solutions such as “Tabu Search”, “Simulated Annealing”, “Genetic Algorithms”, “Evolutionary Programming”, "Ant Colony", "Particle Swarm Optimization" were a preoccupation mainly for the last two decades. This paper presents a model of a multi-objective Assembly Line Balancing problem and a solution approach based on Particle Swarm Optimization (PSO) with a fuzzy controller for tuning inertia weight. This prevents the premature convergence and, in addition, the algorithm demonstrates improved search features. For the considered test instance, the algorithm obtains a better result compared to the results reported in the literature, regarding the number of stations actually used, the line efficiency, the total unused time, the variation in charging stations and the uniformity index of the line.
Particle Swarm Optimization (PSO), Assembly Line Balancing (ALB) problem, fuzzy controller, multiobjective optimization.
Simona DINU, "Multi-objective Assembly Line Balancing Using Fuzzy Inertia-adaptive Particle Swarm Algorithm", Studies in Informatics and Control, ISSN 1220-1766, vol. 24(3), pp. 283-292, 2015. https://doi.org/10.24846/v24i3y201505
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