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Dealing with rapidly evolving customer needs, Facility Layout Problem (FLP) is a crucial leverage in industries to control the performance. FLP looks for efficient physical arrangements of pieces of equipment within a given area. The novelty here is to take account of practical aspects, very often neglected such as inputs/outputs and orientation of machines, to model and to solve 2D layout problems. The suggested resolution method allows to compute realistic transportation distances; closer to real-world requirements. An improved and fine-tuned genetic algorithm integrated with A* algorithm is proposed to solve the mathematically defined model. The technique is illustrated through examples. [ABSTRACT FROM AUTHOR]

Optimization metaheuristics have become necessary due to the growing demand for better and more realistic designs. This paper proposes a metaheuristic-based approach for solving design problems in a reasonable time while browsing large spaces of solutions. The objective of this article is to compare the performance of two methods Genetic Algorithm GA and Particle swarm optimization PSO, combined with A* algorithm, in solving a constrained facility layout problem. The two chosen metaheuristics have been successfully applied in many search problems. We consider their speed and performance. The performance of the obtained solutions is measured in terms of the total distance traveled by products in the workshop. In order to determine the shortest path in a realistic way between workstations in a given irregular area (with aisle structure, or material storage areas, lunchrooms and offices), the A* algorithm was integrated with them. The comparison therefore concerns and . GA and PSO algorithms generate configurations for which the shortest path for any couple of machines is identified through the A* search algorithm taking into account of obstacles. The mathematical model used and the parameters of the genetic algorithm are those developed in (Besbes et al. 2019). The numerical results show the feasibility and effectiveness of both approaches. Our results demonstrate that GA yields a better solution than Particle Swarm Optimization in total distance travelled while PSO is faster. [ABSTRACT FROM AUTHOR]