Dynamic facility layout problem under uncertainty: a Pareto-optimality based multi-objective evolutionary approach
In this paper, we investigate an evolutionary approach to solve the multi-objective dynamic facility layout problem (FLP) under uncertainty that presents the layout as a set of Pareto-optimal solutions. Research examining the dynamic FLP usually assumes that data for each time period are deterministic and known with certainty. However, production uncertainty is one of the most challenging aspects in today’s manufacturing environments. Researchers have only recently modeled FLPs with uncertainty. Unfortunately, most solution methodologies developed to date for both static and dynamic FLPs under uncertainty focus on optimizing just a single objective. To the best of our knowledge, the use of Pareto-optimality in multi-objective dynamic FLPs under uncertainty has not yet been studied. In addition, the approach proposed in this paper is tested using a backward pass heuristic to determine its effectiveness in optimizing multiple objectives. Results show that our approach is an efficient evolutionary dynamic FLP approach to optimize multiple objectives simultaneously under uncertainty.
Keywordsforecast uncertainty Pareto-optimality dynamic facility layout problem multi-objective optimization backward pass pair-wise exchange heuristic
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- Chen G.Y.-H., Multi-objective evaluation of dynamic facility layout using ant colony optimization, PhD thesis, The University of Texas at Arlington, USA, 2007Google Scholar
- Deb K., Pratap A., Agarwal S., Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE T. Evolut. Comput., 6(2), 182–197Google Scholar
- Heragu S.S., Facilities design, BWS, Boston, 1997Google Scholar
- Ripon K.S.N., Khan K.N., Glette K., Hovin M., Torresen J., Using Pareto-optimality for solving multi-objective unequal area facility layout problem, In: Proceedings of 13th Annual Conference on Genetic and Evolutionary Computation (GECCO 2011), (July 2011, Dublin, Ireland), Krasnogor N. (Ed.), ACM, 681–688, 2011Google Scholar
- Ripon K.S.N., Glette K., Hovin M., Torresen J., Dynamic facility layout problem with hybrid genetic algorithm, In: Proceedings of IEEE 9th International Conference on Cybernetic Intelligent Systems (CIS 2010), (September 2010, Reading, UK), Oussalah M., Mitchell R., Siddique N.H. (Eds.), IEEE Sys. Man Cybern., 33–38, 2010Google Scholar
- Schott J.R., Fault tolerant design using single and multicriteria genetic algorithm optimization, MS thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts, 1995Google Scholar
- Tompkins A., Facilities planning, 3rd edition, John Wiley & Sons, New York, 2003Google Scholar