A solution to the unequal area facilities layout problem by genetic algorithm

The majority of the issued facilities layout problems (FLPs) minimize the material handling cost and ignore other factors, such as area utilization, department shape and site shape size. These factors, however, might influence greatly the objective function and should give consideration. The research range of this paper is focus on the unequal areas department facilities layout problem, and implement analysis of variance (ANOVA) of statistics to find out the best site size of layout by genetic algorithm. The proposed module takes the minimum total layout cost (TLC) into account. TLC is an objective function combining material flow factor cost (MFFC), shape ratio factor (SRF) and area utilization factor (AUF). In addition, a rule-based of expert system is implemented to create space-filling curve for connecting each unequal area department to be continuously placed without disjoint (partition). In this manner, there is no gap between each unequal area department. The experimental results show that the proposed approach is more feasible in dealing with the facilities layout problems in the real world.

[1]  William J. Mitchell,et al.  Optimal space planning in practice , 1981 .

[2]  Richard A. Wysk,et al.  An expert system for machining data selection , 1986 .

[3]  Herbert Freeman,et al.  Computer Processing of Line-Drawing Images , 1974, CSUR.

[4]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[5]  S. Heragu,et al.  Efficient models for the facility layout problem , 1991 .

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  Sue Abdinnour-Helm,et al.  Tabu search based heuristics for multi-floor facility layout , 2000 .

[8]  Yavuz A. Bozer,et al.  An improvement-type layout algorithm for single and multiple-floor facilities , 1994 .

[9]  E. Munoko,et al.  Computers in Industry , 1963, Nature.

[10]  Yavuz A. Bozer,et al.  A new simulated annealing algorithm for the facility layout problem , 1996 .

[11]  J. Millis,et al.  THE UNIVERSITY OF , 2000 .

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[13]  Alice E. Smith,et al.  Unequal-area facility layout by genetic search , 1995 .

[14]  A. A. Islier A genetic algorithm approach for multiple criteria facility layout design , 1998 .

[15]  Marcos Ribeiro Pereira Barretto,et al.  A solution to the facility layout problem using simulated annealing , 1998 .

[16]  Andrew Kusiak,et al.  Machine layout: an optimization and knowledge-based approach , 1990 .

[17]  Wen-Chyuan Chiang,et al.  An improved tabu search heuristic for solving facility layout design problems , 1996 .

[18]  Elwood S. Buffa,et al.  A Heuristic Algorithm and Simulation Approach to Relative Location of Facilities , 1963 .

[19]  Rangasami L. Kashyap,et al.  A modelling of interactive facilities layout designer reasoning using qualitative patterns , 1992 .

[20]  K. Y. Tam,et al.  A simulated annealing algorithm for allocating space to manufacturing cells , 1992 .

[21]  L. A. Al-Hakim,et al.  On solving facility layout problems using genetic algorithms , 2000 .