Unified Particle Swarm Optimization for Solving Multi-Item Inventory Models with Supplier Selection

The multi-item inventory optimization problem with supplier selection is considered to be among the most interesting problems in Operations Research. Constraints such as limited capacity and defective items render the problem a demanding challenge for most solvers. Metaheuristic algorithms have been considered as promising approaches for tackling problems of this type. The present work investigates the performance of the Unified Particle Swarm Optimization on this problem type. The employed model also assumes individual transaction costs and productdependent holding cost. Different parametrizations of the algorithm are considered under various configurations. The results are statistically analyzed, offering valuable insight.