Stochastic Programming Models for Dynamic Facility Layout Problem in Flexible Manufacturing Systems

Document Type : Research Paper

Authors

1 Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.

2 Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran.

3 Department of Industrial Engineering, Payame Noor University, Iran.

Abstract

An appropriate facility layout is required to reduce total manufacturing cost, especially in uncertain environments. The design of a desirable facility layout is essential when the rearrangement of the facilities is expensive. Using Routing Flexibility (RF) as a principle of the Flexible Manufacturing System (FMS) can lead to the fulfillment of this need. This paper propounds two new mathematical models for the Dynamic Facility Layout Problem (DFLP) with stochastic approaches. The RF is considered when the independent parts demands follow Exponential and Normal distributions in which their parameters randomly alter from period to period. The primary nonlinear models are first linearized by the proposed innovative technique. Then, the performance of the proposed models and the linearization technique is assessed by solving two test problems. Next, the RF effect on the manufacturing system is analyzed. The obtained results verify the validity and applicability of the proposed models. It is also shown that the suggested linearization technique is an efficient technique with 99% accuracy, even if convexity conditions are not met.

Keywords

References

       [1]        Tompkins, J., White , J., Bozer , Y., & Tanch, J. (2003). Facilities Planning. New York: Wiley.
       [2]        Moslemipour, G., Lee, T. S., & Loong, Y. T. (2017). Performance Analysis of Intelligent Robust Facility Layout Design. Chinese Journal of Mechanical Engineering, 30(2), 407–418.
       [3]        Nordin, N. N., Zainuddin, Z. M., Salim, S., & Ponnusamy, R. R. (2014). Mathematical modelling and hybrid heuristic for unequal size facility layout problem. Malaysian Journal of Fundamental and Applied Sciences (MJFAS), 5(1), 87-89.
       [4]        Benjaafar, S., Heragu, S. S., & Irani, S. A. (2002). Next generation factory layouts: Research challenges and recent progress. Interfaces, 32(6), 58-77.
       [5]        Moslemipour, G., & Lee, T. (2012). Intelligent design of a dynamic machine layout in uncertain environment of flexible manufacturing systems. Journal of Intelligent Manufacturing, 23, 1849-1860.
       [6]        Ripon, K., Glette, K., Khan, K., & Hovin, M. (2013). Adaptive variable neighborhood search for solving multi-objective facility layout problems with unequal area facilities. Swarm and Evolutionary Computation, 8, 1-12.
       [7]        Kulturel-Konak, S., Smith, A., & Norman, B. (2004). Layout Optimization Considering Production Uncertainty and Routing Flexibility. International Journal Of Production Research, 42(21), 4475-4493.
       [8]        Castill, I., & Sim, T. (2004). A spring-embedding approach for the facility layout problem. Journal of the Operational Research Society (JORS), 55, 73-81.
       [9]        Moslemipour, G., Lee, T., & 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, 11-27.
     [10]      Sahni, S., & Gonzalez, T. (1976). P-complete Approximation Problems. Journal of the ACM, 23, 555-565.
     [11]      Pillai, V. M., Hunagund, I. B., & Krishnan, K. K. (2011). Design of robust layout for dynamic plant layout problems. Computers and Industrial Engineering, 61, 813-823.
     [12]      Hosseini-Nasab, H., Fereidouni, S., & Fatemi Ghomi, S. (2017). Classification of facility layout problems: a review study. The International Journal of Advanced Manufacturing Technology, 94(1), 957-977.
     [13]      Shore, R., & Tompkins, J. (1980). Flexible facilities design (Vol. 12). AIIE Transactions.
     [14]      Rosenblatt, M., & Lee, H. (1987). A robustness approach to facilities design. International Journal of Production Research, 25, 479–486.
     [15]      Kouvelis, P., & Kiran, A. (1991). Single and multiple period layout models for automated manufacturing systems. European Journal of Operational Research (52), 300–314.
     [16]      Kouvelis, P., Kurawarwala, A., & Gutierre, G. (1992). Algorithms for robust single and multiple period layout planning for manufacturing systems. European Journal of Operations Research, 63, 287–303.
     [17]      Palekar, U., Batta, R., Bosch, R., & Elhence, S. (1992). Modeling uncertainties in plant layout problems. European Journal of Operational Research, 63, 347–359.
     [18]      Montreuil, B., & Laforge, A. (1992). Dynamic layout design given a scenario tree of probable futures. European Journal of Operational Research, 63, 271–286.
     [19]      Yang, T., & Peters, B. (1998). Flexible machine layout design for dynamic and uncertain production environments. European Journal of Operational Research, 108, 49–64.
     [20]      Irappa-Basappa, H., Madhusudanan-Pillai, V., & Krishna, K. (2001). Design of robust layout for dynamic plant layout problems. Computers & Industrial Engineering, 61, 813–823.
     [21]      Krishnan, K., Cheraghi, S., & Chandan, N. (2008). Facility layout design for multiple production scenarios in a dynamic environment. International Journal of Industrial and Systems Engineering, 3(2), 105–133.
     [22]      Irappa-Basappa, H., & Madhusudanan-Pillai, V. (2008). Development of a heuristic for layout formation and design of robust layout under dynamic demand. International Conference on Digital Factory ICDF, August 11–13, pp. 1398–1405.
     [23]      Pillai, V., & Subbarao, K. (2008). A robust cellular manufacturing system design for dynamic part population using a genetic algorithm. International Journal of Production Research, 46(18), 5191–5210.
     [24]      Soolaki, M., & Izadi, A. (2013). A robust optimisation model for manufacturing cell design problem under uncertainty. International Journal of Services and Operations Management, 13(2), 238–258.
     [25]      Forghani, K., Mohammadi, M., & Ghezavati, V. (2013). Designing robust layout in cellular manufacturing systems with uncertain demands. International Journal of Industrial Engineering Computations, 4(2), 215–226.
     [26]      Neghabi, H., Eshghi, K., & Salmani, M. H. (2014). A new model for robust facility layout problem. Information Sciences, 278, 498–509.
     [27]      Nematian, J. (2014). A robust single row facility layout problem with fuzzy random variables. International Journal Advanced Manufacturing Technology, 72, 255–267.
     [28]      Azadeh, S., Haghighi, S. M., & Asadzadeh, S. M. (2014). A novel algorithm for layout optimization of injection process with random demands and sequence dependent setup times. International Journal of Manufacturing Systems, 33, 287–302.
     [29]      Vitayasak, S., Pongcharoen, P., & Hicks, C. (2016). A tool for solving stochastic dynamic facility layout problems with stochastic demand using either a genetic algorithm or modified backtracking search algorithm. 190, 146-157.
     [30]      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 Industrial and Systems Engineering, 28(1).
     [31]      Tayal , A., & Singh, S. (2019). Formulating multi-objective stochastic dynamic facility layout problem for disaster relief. Annals of Operations Research, 283, 837–863.
     [32]      Tayal , A., Kose , U., Solanki, A., Nayyar, A., & Saucedo, J. (2019). Efficiency analysis for stochastic dynamic facility layout problem using meta-heuristic, data envelopment analysis and machine learning. Computational Intelligence, 1-31.
     [33]      Ghadirpour, S. M., Rahmani, D., & Moslemipour, G. (2020). Routing Flexibility for Unequal-Area Stochastic Dynamic Facility Layout Problem in Flexible Manufacturing Systems. International Journal of Industrial Engineering & Production Research, 31, 269- 285.
     [34]      Khajemahalle, L., Emami , S., & Nemati Keshteli, R. (2021). A hybrid nested partitions and simulated annealing algorithm for dynamic facility layout problem: a robust optimization approach. INFOR: Information Systems and Operational Research, 59(1), 74-101 .
     [35]      Moslemipour, G., & Ghadirpour, S. (2021). Intelligent Design of a Dynamic Facility Layout in the Stochastic Environment of Flexible Manufacturing Systems Considering Routing Flexibility. Journal of Industrial Management Perspective, 11(1), 175-209.
     [36]      Nazari-Ganje, N., & Mirzapour Al-E Hashem, S. (2020). An Integrated Location-Inventory Routing Problem for ATMs in Banking Industry: A Green Approach. Modeling and Optimization in Green Logistics, 27-52.
     [37]      Mirzapour Al-e-hashem, S., Rekik, Y., & Mohammadi Hoseinhajlou, E. (2019). A hybrid L-shaped method to solve a bi-objective stochastic transshipment-enabled inventory routing problem. International Journal of Production Economics, 209, 381-398.
     [38]      Koopmans, T. C., & Bechman, M. (1957). Assignment problems and the location of economic activities (25). Econometric.
     [39]      Freund, J. (1992). Mathematical Statistics. (5th, Ed.) Prentice-Hall, Inc.
     [40]      Rahil, A. (2012, October 1). Linearization of Mixed Integer Programming. Retrieved from www.iems.ucf.edu/qzheng/grpmbr/seminar/Anees_Linear_General_Slides.pdf.
     [41]      Nagarur, N. (1992). Some performance measures of flexible manufacturing systems. International Journal of Production Research, 30(4), 799-809.
Advances in Industrial Engineering
Volume 54, Issue 3
July 2020
Pages 267-291

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