Optimal Facility Layout Problem Solution Using Genetic Algorithm

Facility Layout Problem (FLP) is one of the essential
problems of several types of manufacturing and service sector. It is
an optimization problem on which the main objective is to obtain the
efficient locations, arrangement and order of the facilities. In the
literature, there are numerous facility layout problem research
presented and have used meta-heuristic approaches to achieve
optimal facility layout design. This paper presented genetic algorithm
to solve facility layout problem; to minimize total cost function. The
performance of the proposed approach was verified and compared
using problems in the literature.

• Maricar G. Misola
• Bryan B. Navarro

• Facility Layout Problem
• Genetic Algorithm
• Material Handling Cost
• Meta-heuristic Approach.

[1] J. A. Tompkins and J. A. White, Facilities Planning. 2nd Ed., New
York, John Wiley, 1996
[2] E. E. Aleisha and L. Lin, “For Effectiveness Facilities Planning: Layout
Optimization then simulation, or vice versa?”, Proceedings of the 2005
Winter Simulation Conference
[3] A. Drira, H. Pierreval, and S. Hajri-Gabouj, “Facility layout problems: A
survey”, Annual Reviews in Control 31, 2007, pp. 255-267.
[4] R. Haupt and S. Haupt, Practical Genetic Algorithms, Second Edition,
Copyright 2004 by John Wiley and Sons, Inc.
[5] K. C. Chan and H. Tansri, “A study of genetic crossover operations on
the facility layout problem”, Computers and Industrial Engineering,
1994, 26(3), pp. 537-550.
[6] I. Mihajlovic, Z. Zivkovic, N. Strbac, D. Zivkovic, and A. Jovanovic,
“Using Genetic Algorithms to Resolve Facility Layout Problem”,
Serbian Journal of Management, 2(1), 2007, pp. 35-46.
[7] M. Adel El-Baz, “A genetic algorithm for facility layout problems of
different manufacturing environments”, Computers and Industrial
Engineering, 2004, 47, pp. 233-246.
[8] K. L. Mak, Y. S. Wong, and T. S. Chan, “A genetic algorithm for
facility layout problems”, Journal of Computer Integrated
Manufacturing Systems, 1998, 1 (1-2), pp. 113-123.
[9] X. Liu and X. Li, “An Improved Genetic Algorithms-based Approach on
Supply Chain-oriented Facility Layout Scheduling System”,
Proceedings of the 6th World Congress on Intelligent Control and
Automation, China, June 2006, pp. 3081-3084.
[10] X. Liu, H. Bo, Y. Ma, and Q. Meng, “A New Approach for Planning and
Scheduling Problems in Hybrid Distributed Manufacturing Execution
System”, Proceedings of the 6th World Congress on Intelligent Control
and Automation, China, June 2006, pp. 7357-7361.
[11] P. Kulkarni and K. Shanker, “A Genetic Algorithm for Layout Problems
in Cellular Manufacturing Systems”, IEEE, 2007, pp. 694-698.
[12] Y. Zhang, H. Zhang, M. Xia, T. Lu, and L. Jiang, “Research on
Applying Unidirectional Loop Layout to Optimize Facility Layout in
Workshop Based on Improved Genetic Algorithms”, IITA International
Conference on Control, Automation and Systems Engineering, 2009, pp.
144-147.
[13] K. S. N. Ripon, K. Glette, M. Hovin, and J. Torresen, “A Multi-
Objective Evolutionary Algorithm for Solving Integrated Scheduling
and Layout Planning Problems in Manufacturing Systems”, IEEE, 2013,
pp. 157-163.
[14] G. Aiello, G. La Scalia, and M. Enea, “A multi objective genetic
algorithm for the facility layout problem based upon slicing structure
encoding”, Expert Systems with Applications, 2012.
[15] A. Chipperfield, P. Fleming, H. Pohlheim, and C. Ponseca, Genetic
Algorithm Toolbox for use with Matlab User’s Guide Version 1.2,
Department of Automatic Control and Systems Engineering of the
University of Sheffield.
[16] L. Xu, S. Yang, A. Li, and A. Matta, “An adaptive genetic algorithm for
facility layout problem in cylinder block line”, IEEE, 2011, pp. 749-753.