A Mathematical Model for Unequal Area Stochastic Dynamic Facility Layout Problems

Document Type : Research Paper

Authors

Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

10.22070/jqepo.2021.5101.1124

Abstract

The present research formulates unequal area stochastic dynamic facility layout problems for minimizing the total cost of material handling while considering their upper bound. In the proposed model, the area and shape of departments are considered to be stable during the implementation of an algorithm, and product demands are normally distributed with a known expected value and variance. Since these problems are NP-hard, thus particle swarm optimization (PSO) was employed, and a theoretical problem instance was presented to evaluate the efficiency and effectiveness of the algorithm. The findings confirmed the efficiency and validity of the proposed algorithm.

Keywords

References

Armour, G.C., & Buffa, E.S., (1963). A heuristic algorithm and simulation approach to relative location of facilities.Management Science9(2):294-309.
Casella, G., & Berger, R.L., (2002). Statistical inference. Thomson, India.
Derakhshan Asl, A., Wong, K.Y., &Tiwari, M.K., (2016). Unequal-area stochastic facility layout problems: solutions using
improved covariance matrix adaptation evolution strategy, particle swarm optimisation, and genetic algorithm.International
Journal of Production Research 54(3):799-823.
Derakhshan Asl, A., & Wong, K.Y., (2017). Solving unequal-area static and dynamic facility layout problems using modified particle swarm optimization.Journal of Intelligent Manufacturing 28(6):1317-1336.
Engel Brecht, A.P., (2019). Computational intelligence: an introduction. John Wiley & Sons, Chichester.
Kennedy, J., & Eberhart, R., (1995). Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942-1948.
Koopmans, T.C., & Beckmann, M., (1957). Assignment problems and the location of economic activities. Econometrica: Journal of the Econometric Society 25(1):53-76.
Kouvelis, P., & Kiran, A.S., (1991). Single and multiple period layout models for automated manufacturing systems.European
Journal of Operational Research52(3):300-314.
Krishnan, K.K., Cheraghi, S.H., & Nayak, C.N., (2006). Dynamic From-Between Chart: a new tool for solving dynamic facility
layout problems. International Journal of Industrial and Systems Engineering 1(1-2):182-200.
Krishnan, K.K., Cheraghi, S.H., & Nayak, C.N., (2008). Facility layout design for multiple production scenarios in a
dynamic environment. International Journal of Industrial and Systems Engineering 3(2):105-133.
Lee, T.S., Moslemipour, G., Ting, T.O., & Rilling, D., (2012). A Novel Hybrid ACO/SA Approach to Solve Stochastic Dynamic
Facility Layout Problem (SDFLP). Emerging Intelligent Computing Technology and Applications 304:100-108.
Mazinani, M., Abedzadeh, M., &Mohebali, N., (2012).Dynamic facility layout problem based on flexible bay structure and solving by genetic algorithm.The International Journal of Advanced Manufacturing Technology65(5-8):929-943.
Moslemipour, G., & Lee, T., (2012).Intelligent design of a dynamic machine layout in uncertain environment of flexible manufacturing systems.Journal of Intelligent Manufacturing23(5):1-12.
Moslemipour, G., Lee, T.S., &Rilling, D., (2012). A review of intelligent approaches for designing dynamic and robust layouts in flexible manufacturing systems.The International Journal of Advanced Manufacturing Technology 60(1-4):11-27.
Moslemipour, G., Lee, T. S., &Loong, Y. T. (2018).Solving stochastic dynamic facility layout problems using proposed hybrid AC-CS-SA meta-heuristic algorithm. International Journal of Industrial and Systems Engineering, 28(1), 1-31.
Nayak, C.N., (2007). Solutions to dynamic facility layout problems: Development of Dynamic From Between Chart (DFBC) and its applications to continuous layout modeling. Dissertation, Wichita State University.
Palekar, U.S., Batta, R., Bosch, R.M., &Elhence, S., (1992).Modeling uncertainties in plant layout problems.European Journal of Operational Research63(2):347-359.
Peng, Y., Zeng, T., Fan, L., Han, Y., & Xia, B. (2018). An Improved Genetic Algorithm Based Robust Approach for Stochastic Dynamic Facility Layout Problem. Discrete Dynamics in Nature and Society, 2018.
Tayal, A., Kose, U., Solanki, A., Nayyar, A., & Saucedo, J. A. M. (2020).Efficiency analysis for stochastic dynamic facility layout problem using meta‐heuristic, data envelopment analysis and machine learning.Computational Intelligence, 36(1), 172-202.
Tompkins, J.A., White, J.A., Bozer, Y.A., & Tanchoco, J.M.A., (2018). Facilities Planning. Wiley, New York.
Yang, T., & Peters, B.A., (1998).Flexible machine layout design for dynamic and uncertain production environments. European Journal of Operational Research 108(1):49-64.
Vitayasak, S., Pongcharoen, P., & Hicks, C. (2017).A tool for solving stochastic dynamic facility layout problems with stochastic demand using either a Genetic Algorithm or modified Backtracking Search Algorithm. International Journal of Production Economics, 190, 146-157.
Journal of Quality Engineering and Production Optimization
Volume 6, Issue 1
May 2021
Pages 147-164

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