Desarrollo de un algoritmo híbrido metaheurístico para el problema de distribución de planta en las MIPYMES textiles del Ecuador

The Facility Layout Problem (FLP) refers to finding the most effective arrangement of facilities in a factory, considering various aspects. These facilities could include departments, personnel, or machinery, to name a few examples. The FLP is an NP-Hard problem, meaning that there are no algorithms capable of providing an optimal solution in a reasonable polynomial time. Due to this, researchers have turned to metaheuristics and have attempted to combine them to create hybrids. Hybrid metaheuristics can leverage the strengths of each approach to generate higher-quality solutions that are close to optimal. However, many studies that develop hybrid metaheuristics do not use a methodology that explains the development process. For this reason, this work focuses on composing a hybrid metaheuristic following a methodology that encourages the use of SWOT analysis, the Strategic Choice Approach, and Convergent/Divergent Thinking. The hybrid metaheuristic called GENTSA was constructed using a Genetic Algorithm, Simulated Annealing, and Tabu Search. GENTSA solves the FLP that arises in small and medium-sized textile businesses in Ecuador, which was used as a case study. Additionally, as part of its validation, test functions obtained from the state of the art were used. GENTSA was compared to other metaheuristics where it achieves the best facility layout with the lowest cost and reasonably adapts to problem domains beyond its original design scope.