Study On Bi-criteria Approach To Facility Layout Problem Based On Genetic Algorithm

Nowadays, more and more people have realized the importance of logistics in enterprises in the competitive environment of the manufacturing industry. So does the facility layout problem. Reasonable facility layout can reduce the transportation costs of logistics, improve the quality of products, shorten the lead time, and improve the working environment effectively.This paper describes several classical facility layout problems, and discuses the methods and the algorithms to these problems. By analyzing the advantages and disadvantages of these approaches, this paper proposes a bi-criteria mathematic model for the facility layout problems with inner walls and passages. First, establishes a bi-criteria layout model that has walls and passages based on the traditional facility layout models and SLP method combining the actual needs of enterprises. Then, gives some candidate solutions maked by the improved genetic algorithm for decision-makers to choose.The model this paper proposes has highly systematic and practical use. First, the continuous layout problem of unequal area and irregular shape of facilities is more practical than others, and it is better when adding the factors of inner walls and passages; Second, it is more optimized when concerning material handing costs together with nonmaterial relation requirements; the shortest path and distance between two facilities is calculated using Dijkstra's algorithm of graph theory, and it is more accurate and objective than traditional methods of straight line or rectangular distance. For the design of the proposed genetic algorithm, different genetic operation is imposed on different chromosomes and segments to ensure the effectiveness and overall optimization of the algorithm; by using the refinement operation in the proposed algorithm, this paper has handled void spaces generated during the decoding process, and improved the utilization of the layout area. Finally, in the part of the empirical study, this paper uses C# language to achieve the genetic algorithm, and draw solutions of the genetic algorithm which can prove the superiority of the algorithm.