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Dynamic facility layout problem under uncertainty: a Pareto-optimality based multi-objective evolutionary approach

Kazi Ripon / Kyrre Glette / Mats Hovin / Jim Torresen
Published Online: 2011-12-27 | DOI: https://doi.org/10.2478/s13537-011-0027-8


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.

Keywords: forecast uncertainty; Pareto-optimality; dynamic facility layout problem; multi-objective optimization; backward pass pair-wise exchange heuristic

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About the article

Published Online: 2011-12-27

Published in Print: 2011-12-01

Citation Information: Open Computer Science, Volume 1, Issue 4, Pages 375–386, ISSN (Online) 2299-1093, DOI: https://doi.org/10.2478/s13537-011-0027-8.

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© 2011 Versita Warsaw. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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