Hybrid Particle Swarm Algorithm Applied to Flexible Job-Shop Problem

In the globalized world, with highly competitive markets, companies are looking for ways to reduce costs in a sustainable manner, optimizing their production lines to increase their economic advantages. Thus, several studies appeared with the objective of modeling the productive sectors, among which it is possible to highlight the Flexible Job-Shop. This model aims to efficiently organize the distribution of tasks to be processed in a set of available machines so that the complete execution of these tasks takes the shortest possible time considering several productive constraints. The resolution of this model involves complex combinatorial calculations, which allow the development of computational tools for this purpose, supporting the decision-making process. Therefore, this work presents a hybrid computational proposal based on Particle Swarm Optimization and Simulated Annealing algorithms to use the intrinsic advantages of these approaches to scheduling industrial productions. The results show that the proposed hybrid algorithm efficiently solves the production scheduling problem in a partially flexible scenario, overcoming the minimization of the production completeness time present in some benchmarks found in the literature for this class of problems.