A FIRST STUDY OF PARTICLE SWARM OPTIMIZATION ON THE DYNAMIC LOT SIZING PROBLEM WITH PRODUCT RETURNS

We study the behavior of a popular metaheuristic optimization algorithm, namely Particle Swarm Optimization (PSO), on the single-item dynamic lot sizing problem with returns and remanufacturing. The most suitable variants of the algorithm are identified and applied after the necessary modifications. The performance of the algorithm is assessed on an extensive test suite employed in previous studies. Its performance is compared with that of the adapted Silver-Meal algorithm as well as with its recently enhanced versions. The results suggest that PSO is very competitive and can be considered as a promising alternative for solving the considered problems.

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