Optimal Warehouse Distribution Center Placement Using Evolutionary Algorithms.

In this paper, we propose an approach to solve an "use-inspired" problem: the management of warehouses containing equipment to be installed at various sites throughout the territory by specialized personnel. The challenges to be addressed include the distribution of the warehouse, so as not to slow down work, but also the fact that the tasks to complete change constantly, requiring the opening, closing, and relocation of the corresponding warehouses. The study models the problem as an optimization problem and investigates how to compose multiple objectives to select the warehouse location, in a single objective function, necessary to obtain a single solution, and evaluates the performance of a set of stochastic optimization algorithms to generate cost-effective solutions while adhering to the constraints of the problem. The results demonstrate that metaheuristic algorithms are highly effective in identifying optimal solutions, with Binary Particle Swarm Optimization and Population Based Incremental Learning having good performances.