Sustainable dynamic cellular facility layout: a solution approach using simulated annealing-based meta-heuristic

Abstract

The fiercely competitive business environment may require the manufacturing layouts to be modified across the entire planning horizon owing to addition or deletion of new/existing products, machines or processes. The existing layout may not be appropriate for the next time periods as product combination and part demand tend to vary under multi-time scenario. Also the increased awareness of environmental concerns and paucity of vital resources like electric energy has led organizations to rethink about their manufacturing strategies and design layouts which are both cost effective as well as environmentally sustainable. An appropriately planned layout not only helps in reduction of material handling distance but can also greatly contribute to enhancement of the energy efficiency of manufacturing systems and contribute to resource productivity and sustainable value creation. To address these issues in the facility layout design, this paper models a dynamic cellular facility layout problem incorporating the sustainability aspect by considering the minimization of net electric energy consumption along with material handling and rearrangement costs. The model presented in this work is a mixed integer non-linear program. The model aims to minimize the aggregated cost of overall material handling for both the inter and intra-cell movements simultaneously. Additionally the model also minimizes the net electrical energy consumption across the entire time horizon. Twenty five data sets corresponding to varying combinations of machines, time periods and cells have been taken from extant literature to validate the proposed model. LINGO 10 optimization software has been used to solve the proposed model. However, due to NP-Hard nature of cellular facility layout problem, the proposed model is computationally difficult to be solved in reasonable time using LINGO 10, particularly, for layouts pertaining to larger dimensions. To overcome these complexities, a meta-heuristic based on simulated annealing (SA) is also employed to solve the model. It is discerned from the experimental results that LINGO is not able to optimally solve the model whereas the SA optimally solves the model for larger dimensions as well in reasonable computational time.

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References

  1. Alhourani, F., & Saxena, U. (2009). Factors affecting the implementation rates of energy and productivity recommendations in small and medium sized companies. Journal of Manufacturing Systems,28(1), 41–45.

    Google Scholar 

  2. Aljuneidi, T., & Bulgak, A. A. (2017). Designing a cellular manufacturing system featuring remanufacturing, recycling, and disposal options: A mathematical modeling approach. CIRP Journal of Manufacturing Science and Technology,19, 25–35.

    Google Scholar 

  3. Altuntas, S., Dereli, T., & Selim, H. (2013). Fuzzy weighted association rule based solution approaches to facility layout problem in cellular manufacturing system. International Journal of Industrial and Systems Engineering,15(3), 253–271.

    Google Scholar 

  4. Ariafar, S., & Ismail, N. (2009). An improved algorithm for layout design in cellular manufacturing systems. Journal of Manufacturing Systems,28(4), 132–139.

    Google Scholar 

  5. Balakrishnan, J., & Cheng, C. H. (2007). Multi-period planning and uncertainty issues in cellular manufacturing: A review and future directions. European Journal of Operational Research,177(1), 281–309.

    Google Scholar 

  6. Baykasoğlu, A., & Gindy, N. N. (2001). A simulated annealing algorithm for dynamic layout problem. Computers & Operations Research,28(14), 1403–1426.

    Google Scholar 

  7. Bayram, H., & Şahin, R. (2016). A comprehensive mathematical model for dynamic cellular manufacturing system design and linear programming embedded hybrid solution techniques. Computers & Industrial Engineering,91, 10–29.

    Google Scholar 

  8. Benjaafar, S. (2002). Modeling and analysis of congestion in the design of facility layouts. Management Science,48(5), 679–704.

    Google Scholar 

  9. Bougain, S., Gerhard, D., Nigischer, C., & Uĝurlu, S. (2015). Towards energy management in production planning software based on energy consumption as a planning resource. Procedia CIRP,26, 139–144.

    Google Scholar 

  10. Brown, J. R. (2015). A capacity constrained mathematical programming model for cellular manufacturing with exceptional elements. Journal of Manufacturing Systems,37, 227–232.

    Google Scholar 

  11. Dalfard, V. M. (2013). New mathematical model for problem of dynamic cell formation based on number and average length of intra and intercellular movements. Applied Mathematical Modelling,37(4), 1884–1896.

    Google Scholar 

  12. de Oliveria Gomes, V. E., de Oliveira Gomes, J., & Grote, K. H. (2013). Discrete event simulation inserted into Kaizen event to assess energy efficiency. Re-engineering manufacturing for sustainability (pp. 499–503). Singapore: Springer.

    Google Scholar 

  13. Duflou, J. R., Sutherland, J. W., Dornfeld, D., Herrmann, C., Jeswiet, J., Kara, S., et al. (2012). Towards energy and resource efficient manufacturing: A processes and systems approach. CIRP Annals-Manufacturing Technology,61(2), 587–609.

    Google Scholar 

  14. Feng, H., Da, W., Xi, L., Pan, E., & Xia, T. (2017). Solving the integrated cell formation and worker assignment problem using particle swarm optimization and linear programming. Computers & Industrial Engineering,110, 126–137.

    Google Scholar 

  15. Gupta, M., & Sharma, K. (1996). Environmental operations management: An opportunity for improvement. Production and Inventory Management Journal,37, 40–46.

    Google Scholar 

  16. Hu, S., Liu, F., He, Y., & Hu, T. (2012). An on-line approach for energy efficiency monitoring of machine tools. Journal of Cleaner Production,27, 133–140.

    Google Scholar 

  17. Iqbal, A., & Al-Ghamdi, K. A. (2018). Energy-efficient cellular manufacturing system: Eco-friendly revamping of machine shop configuration. Energy,163, 863–872.

    Google Scholar 

  18. Kao, Y., & Fu, S. C. (2006). An ant-based clustering algorithm for manufacturing cell design. The International Journal of Advanced Manufacturing Technology,28(11–12), 1182–1189.

    Google Scholar 

  19. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science,220(4598), 671–680.

    Google Scholar 

  20. Kumar, R., & Singh, S. P. (2018). Simulated annealing-based embedded meta-heuristic approach to solve bi-objective robust stochastic sustainable cellular layout. Global Journal of Flexible Systems Management,19(1), 69–93.

    Google Scholar 

  21. Kumar, R., Singh, S. P., & Lamba, K. (2018). Sustainable robust layout using big data approach: A key towards industry 4.0. Journal of Cleaner Production,204, 643–659.

    Google Scholar 

  22. Langer, T., Schlegel, A., Stoldt, J., & Putz, M. (2014). A model-based approach to energy-saving manufacturing control strategies. Procedia CIRP,15, 123–128.

    Google Scholar 

  23. Mahdavi, I., Aalaei, A., Paydar, M. M., & Solimanpur, M. (2012). A new mathematical model for integrating all incidence matrices in multi-dimensional cellular manufacturing system. Journal of Manufacturing Systems,31(2), 214–223.

    Google Scholar 

  24. May, G., Stahl, B., Taisch, M., & Kiritsis, D. (2017). Energy management in manufacturing: From literature review to a conceptual framework. Journal of Cleaner Production,167, 1464–1489.

    Google Scholar 

  25. McKendall, A. R., Jr., Shang, J., & Kuppusamy, S. (2006). Simulated annealing heuristics for the dynamic facility layout problem. Computers & Operations Research,33(8), 2431–2444.

    Google Scholar 

  26. Meller, R. D., & Bozer, Y. A. (1996). A new simulated annealing algorithm for the facility layout problem. International Journal of Production Research,34(6), 1675–1692.

    Google Scholar 

  27. Mohammadi, M., & Forghani, K. (2016). Designing cellular manufacturing systems considering S-shaped layout. Computers & Industrial Engineering,98, 221–236.

    Google Scholar 

  28. Moslemipour, G., & Lee, T. S. (2012). Intelligent design of a dynamic machine layout in uncertain environment of flexible manufacturing systems. Journal of Intelligent Manufacturing,23(5), 1849–1860.

    Google Scholar 

  29. Niakan, F., Baboli, A., Moyaux, T., & Botta-Genoulaz, V. (2016). A bi-objective model in sustainable dynamic cell formation problem with skill-based worker assignment. Journal of Manufacturing Systems,38, 46–62.

    Google Scholar 

  30. Olson, D. L., & Swenseth, S. R. (1987). A linear approximation for chance-constrained programming. Journal of the Operational Research Society,38(3), 261–267.

    Google Scholar 

  31. Rabbani, M., Farrokhi-Asl, H., Rafiei, H., & Khaleghi, R. (2017). Using metaheuristic algorithms to solve a dynamic cell formation problem with consideration of intra-cell layout design. Intelligent Decision Technologies,11(1), 109–126.

    Google Scholar 

  32. Rafiee, K., Rabbani, M., Rafiei, H., & Rahimi-Vahed, A. (2011). A new approach towards integrated cell formation and inventory lot sizing in an unreliable cellular manufacturing system. Applied Mathematical Modelling,35(4), 1810–1819.

    Google Scholar 

  33. Raoofpanah, H., Ghezavati, V., & Tavakkoli-Moghaddam, R. (2018). Solving a new robust green cellular manufacturing problem with environmental issues under uncertainty using Benders decomposition. Engineering Optimization,51, 1–22.

    Google Scholar 

  34. Safaei, N., Saidi-Mehrabad, M., & Jabal-Ameli, M. S. (2008). A hybrid simulated annealing for solving an extended model of dynamic cellular manufacturing system. European Journal of Operational Research,185(2), 563–592.

    Google Scholar 

  35. Selim, H. M., Askin, R. G., & Vakharia, A. J. (1998). Cell formation in group technology: Review, evaluation and directions for future research. Computers & Industrial Engineering,34(1), 3–20.

    Google Scholar 

  36. Shang, J. S. (1993). Multicriteria facility layout problem: An integrated approach. European Journal of Operational Research, 66(3), 291–304.

    Google Scholar 

  37. Singh, S. P., & Sharma, R. R. (2006). A review of different approaches to the facility layout problems. The International Journal of Advanced Manufacturing Technology,30(5–6), 425–433.

    Google Scholar 

  38. Singh, S. P., & Singh, V. K. (2011). Three-level AHP-based heuristic approach for a multi-objective facility layout problem. International Journal of Production Research,49(4), 1105–1125.

    Google Scholar 

  39. Solimanpur, M., & Jafari, A. (2008). Optimal solution for the two-dimensional facility layout problem using a branch-and-bound algorithm. Computers & Industrial Engineering,55(3), 606–619.

    Google Scholar 

  40. Tavakkoli-Moghaddam, R., Aryanezhad, M. B., Safaei, N., & Azaron, A. (2005). Solving a dynamic cell formation problem using metaheuristics. Applied Mathematics and Computation,170(2), 761–780.

    Google Scholar 

  41. Tavakkoli-Moghaddam, R., Javadian, N., Javadi, B., & Safaei, N. (2007). Design of a facility layout problem in cellular manufacturing systems with stochastic demands. Applied Mathematics and Computation,184(2), 721–728.

    Google Scholar 

  42. Tayal, A., Gunasekaran, A., Singh, S. P., Dubey, R., & Papadopoulos, T. (2017). Formulating and solving sustainable stochastic dynamic facility layout problem: A key to sustainable operations. Annals of Operations Research,253(1), 621–655.

    Google Scholar 

  43. Tayal, A., & Singh, S. P. (2017). Formulating multi-objective stochastic dynamic facility layout problem for disaster relief. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2592-2.

    Article  Google Scholar 

  44. Tompkins, J. A., White, J. A., Bozer, Y. A., & Tanchoco, J. M. A. (2010). Facilities planning. Hoboken: Wiley.

    Google Scholar 

  45. Urban, T. L. (1998). Solution procedures for the dynamic facility layout problem. Annals of Operations Research,76, 323–342.

    Google Scholar 

  46. Van Laarhoven, P. J., & Aarts, E. H. (1987). Simulated annealing. Simulated annealing: Theory and applications (pp. 7–15). Dordrecht: Springer.

    Google Scholar 

  47. Vikhorev, K., Greenough, R., & Brown, N. (2013). An advanced energy management framework to promote energy awareness. Journal of Cleaner Production,43, 103–112.

    Google Scholar 

  48. Wang, T. Y., Wu, K. B., & Liu, Y. W. (2001). A simulated annealing algorithm for facility layout problems under variable demand in cellular manufacturing systems. Computers in Industry,46(2), 181–188.

    Google Scholar 

  49. Wang, R., Zhao, H., Wu, Y., Wang, Y., Feng, X., & Liu, M. (2018). An industrial facility layout design method considering energy saving based on surplus rectangle fill algorithm. Energy,158, 1038–1051.

    Google Scholar 

  50. Wemmerlov, U., & Johnson, D. J. (1997). Cellular manufacturing at 46 user plants: Implementation experiences and performance improvements. International Journal of Production Research,35(1), 29–49.

    Google Scholar 

  51. Wu, X., Chu, C. H., Wang, Y., & Yan, W. (2007). A genetic algorithm for cellular manufacturing design and layout. European Journal of Operational Research,181(1), 156–167.

    Google Scholar 

  52. Yang, L., Deuse, J., & Jiang, P. (2013). Multiple-attribute decision-making approach for an energy-efficient facility layout design. The International Journal of Advanced Manufacturing Technology,66(5–8), 795–807.

    Google Scholar 

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Appendix: SA solutions for the proposed sustainable-DCFLP for datasets DS1–DS25

Appendix: SA solutions for the proposed sustainable-DCFLP for datasets DS1–DS25

  T = 1 T = 2
DS1 (OFV = 38,512)
 Location 1 2 3 4 5 1 2 3 4 5
 Machine 1 3 5 2 4 1 3 5 2 4
 Cell 1 0 1 1 0 1 0 1 1 0 1
 Cell 2 1 0 0 1 0 1 0 0 1 0
  T = 1 T = 2 T = 3
DS2 (OFV = 53,650)
 Location 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
 Machine 5 4 1 3 2 5 4 1 3 2 5 4 1 3 2
 Cell 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0 1
 Cell 2 0 0 1 1 0 0 0 1 1 0 0 0 1 1 0
  T = 1 T = 2 T = 3 T = 4
DS3 (OFV = 71,137)
 Location 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
 Machine 5 3 1 4 2 5 3 1 4 2 5 3 1 4 2 5 3 1 4 2
 Cell 1 0 1 1 0 1 0 1 1 0 1 0 1 1 0 1 0 1 1 0 1
 Cell 2 1 0 0 1 0 1 0 0 1 0 1 0 0 1 0 1 0 0 1 0
  T = 1 T = 2
DS4 (OFV = 67,789)
 Location 1 2 3 4 5 1 2 3 4 5
 Machine 2 1 3 4 5 2 1 3 4 5
 Cell 1 0 0 1 1 1 0 0 1 1 1
 Cell 2 1 1 0 0 0 1 1 0 0 0
  T = 1 T = 2 T = 3
DS5 (OFV = 88,222)
 Location 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
 Machine 3 1 2 5 4 3 1 2 5 4 3 2 1 5 4
 Cell 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 0
 Cell 2 0 1 0 0 1 0 1 0 0 1 0 1 0 0 1
b T = 1 T = 2 T = 3 T = 4
DS6 (OFV = 109,776)
 Location 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
 Machine 5 3 1 4 2 5 3 1 4 2 4 1 3 5 2 5 1 3 4 2
 Cell 1 1 1 0 1 0 1 1 0 1 0 1 0 1 1 0 1 0 1 1 0
 Cell 2 0 0 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1
  T = 1 T = 2
DS7 (OFV = 130,395)
 Location 1 2 3 4 5 1 2 3 4 5
 Machine 1 2 4 3 5 1 2 4 3 5
 Cell 1 1 1 0 0 1 1 1 0 0 1
 Cell 2 0 0 1 1 0 0 0 1 1 0
  T = 1 T = 2
DS8 (OFV = 171,578)
 Location 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
 Machine 4 5 3 2 1 4 5 3 2 1 4 2 3 5 1
 Cell 1 0 0 1 1 1 0 0 1 1 1 0 1 1 0 1
 Cell 2 1 1 0 0 0 1 1 0 0 0 1 0 0 1 0
  T = 1 T = 2 T = 3 T = 4
DS9 (OFV = 232,990)
 Location 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
 Machine 5 3 1 4 2 5 3 1 4 2 5 3 1 4 2 4 5 1 2 3
 Cell 1 1 1 0 0 1 0 1 0 1 1 0 1 0 1 1 1 1 0 1 0
 Cell 2 0 0 1 1 0 1 0 1 0 0 1 0 1 0 0 0 0 1 0 1
  T = 1 T = 2
DS10 (OFV = 57,218)
 Location 1 2 3 4 5 6 7 1 2 3 4 5 6 7
 Machine 1 2 4 3 5 6 7 1 2 4 3 5 6 7
 Cell 1 1 1 0 0 1 0 1 1 0 1 0 1 0 1
 Cell 2 0 0 1 1 0 1 0 0 1 0 1 0 1 0
  T = 1 T = 2
DS11 (OFV = 57,276)
 Location 1 2 3 4 5 6 7 1 2 3 4 5 6 7
 Machine 5 3 1 4 2 7 6 1 3 5 2 4 7 6
 Cell 1 0 0 1 1 0 0 0 0 0 0 0 1 0 1
 Cell 2 0 0 0 0 1 0 1 0 0 1 1 0 0 0
 Cell 3 1 1 0 0 0 1 0 1 1 0 0 0 1 0
  T = 1 T = 2
DS12 (OFV = 103,936)
 Location 1 2 3 4 5 6 7 1 2 3 4 5 6 7
 Machine 7 3 2 6 4 1 5 1 3 5 2 4 7 6
 Cell 1 1 0 1 0 1 1 0 1 0 0 1 1 1 0
 Cell 2 0 1 0 1 0 0 1 0 1 1 0 0 0 1
  T = 1 T = 2
DS13 (OFV = 103,936)
 Location 1 2 3 4 5 6 7 1 2 3 4 5 6 7
 Machine 7 3 2 6 4 1 5 1 3 5 2 4 7 6
 Cell 1 0 0 0 0 0 1 1 1 0 1 0 0 0 0
 Cell 2 0 1 1 0 0 0 0 0 1 0 1 0 0 0
 Cell 3 1 0 0 1 1 0 0 0 0 0 0 1 1 1
  T = 1 T = 2
DS14 (OFV = 192,708)
 Location 1 2 3 4 5 6 7 1 2 3 4 5 6 7
 Machine 7 3 1 6 2 5 4 1 3 5 2 4 7 6
 Cell 1 1 1 1 0 1 0 0 1 1 0 0 0 1 1
 Cell 2 0 0 0 1 0 1 1 0 0 1 1 1 0 0
  T = 1 T = 2
DS15 (OFV = 192,723)
 Location 1 2 3 4 5 6 7 1 2 3 4 5 6 7
 Machine 1 3 5 2 4 7 6 1 3 5 2 4 7 6
 Cell 1 0 0 0 0 1 1 0 0 1 1 0 0 0 0
 Cell 2 0 1 0 1 0 0 0 0 0 0 1 1 0 0
 Cell 3 1 0 1 0 0 0 1 1 0 0 0 0 1 1
  T = 1 T = 2
DS16 (OFV = 64,761)
 Location 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
 Machine 6 1 2 7 3 4 5 8 4 6 5 2 3 7 8 1
 Cell 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 0 0
 Cell 2 0 0 0 1 0 1 1 0 1 0 0 0 0 0 1 1
  T = 1 T = 2
DS17 (OFV = 120,365)
 Location 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
 Machine 3 1 2 7 6 4 5 8 4 6 5 2 3 7 8 1
 Cell 1 1 1 1 0 0 0 1 1 1 1 0 1 1 1 0 0
 Cell 2 0 0 0 1 1 1 0 0 0 0 1 0 0 0 1 1
  T = 1 T = 2
DS18 (OFV = 221,636)
 Location 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
 Machine 6 1 2 7 3 4 5 8 4 6 5 2 3 7 8 1
 Cell 1 1 0 1 1 1 0 0 1 1 1 0 0 0 1 1 1
 Cell 2 0 1 0 0 0 1 1 0 0 0 1 1 1 0 0 0
  T = 1 T = 2
DS19 (OFV = 98,377)
 Location 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
 Machine 4 5 6 2 3 7 8 1 4 6 5 2 3 7 8 1
 Cell 1 1 0 0 1 0 1 1 1 0 0 1 1 0 1 1 1
 Cell 2 0 1 1 0 1 0 0 0 1 1 0 0 1 0 0 0
  T = 1 T = 2
DS20 (OFV = 160,290)
 Location 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
 Machine 4 6 7 2 3 8 5 1 4 6 5 2 3 7 8 1
 Cell 1 1 1 1 1 0 0 1 0 0 1 1 1 1 1 0 0
 Cell 2 0 0 0 0 1 1 0 1 1 0 0 0 0 0 1 1
  T = 1 T = 2
DS21 (OFV = 323,310)
 Location 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
 Machine 4 6 7 2 3 8 5 1 4 6 5 2 3 7 8 1
 Cell 1 0 0 1 1 1 1 1 0 1 0 1 0 1 0 1 1
 Cell 2 1 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0
DS22 (OFV = 293,560)
T = 1
 Location 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
 Machine 3 2 15 14 13 4 5 10 11 12 1 6 7 8 9
 Cell 1 0 0 1 0 0 0 1 1 1 1 1 0 0 0 1
 Cell 2 0 0 0 1 1 1 0 0 0 0 0 1 1 0 0
 Cell 3 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0
T = 2
 Location 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
 Machine 13 11 10 2 9 14 12 7 5 4 15 6 8 3 1
 Cell 1 1 0 1 0 0 1 0 1 0 1 0 1 1 0 0
 Cell 2 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1
 Cell 3 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0
T = 3
 Location 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
 Machine 1 5 9 11 12 2 6 7 10 15 3 4 8 13 14
 Cell 1 0 1 1 0 1 0 0 1 0 1 0 1 1 0 0
 Cell 2 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0
 Cell 3 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1
DS23 (OFV = 405,414)
T = 1
 Machine 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
 Location 1 9 19 8 16 5 20 12 2 18 13 4 10 6 11 7 17 14 15 3
 Cell 1 1 1 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1
 Cell 2 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0
 Cell 3 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0
 Cell 4 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0
T = 2
 Machine 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
 Location 18 6 13 8 3 10 14 16 19 9 20 11 5 12 4 7 1 15 2 17
 Cell 1 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 1 1 1 0 0
 Cell 2 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 0
 Cell 3 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1
 Cell 4 0 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0
T = 3
 Machine 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
 Location 5 10 1 11 6 15 20 19 3 18 7 17 8 4 12 9 16 14 13 2
 Cell 1 0 1 1 0 0 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0
 Cell 2 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0
 Cell 3 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1
 Cell 4 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0
DS24 (OFV = 529,748)
T = 1
 Machine 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
 Location 13 14 20 21 22 12 15 19 23 24 11 16 18 17 25 10 7 5 3 2 9 8 6 4 1
 Cell 1 0 0 0 1 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0
 Cell 2 0 1 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0
 Cell 3 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1
 Cell 4 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0
T = 2
 Machine 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
 Location 19 20 14 13 17 24 21 16 12 11 23 22 15 10 9 25 18 4 7 6 1 2 3 5 8
 Cell 1 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 1 0 1
 Cell 2 1 1 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0
 Cell 3 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0
 Cell 4 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0
T = 3
 Machine 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
 Location 10 12 25 23 21 9 8 24 22 20 7 13 14 16 19 3 11 6 15 17 2 1 4 5 18
 Cell 1 0 1 1 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 0 0
 Cell 2 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 1
 Cell 3 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0
 Cell 4 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0
DS25 (OFV = 627,062)
T = 1
 Location 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
 Machine 5 4 2 3 19 20 22 24 26 23 6 7 1 12 18 14 15 25 27 30 8 9 10 11 13 17 16 21 28 29
 Cell 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1
 Cell 2 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0
 Cell 3 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0
 Cell 4 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0
 Cell 5 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0
T = 2
 Location 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
 Machine 24 25 21 19 16 14 12 1 4 2 27 29 26 20 17 13 11 9 8 3 28 23 22 30 18 15 10 7 6 5
 Cell 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0
 Cell 2 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 1 0 0 1 0 0 1 0 0 0 0
 Cell 3 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0
 Cell 4 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0
 Cell 5 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1
T = 3
 Location 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
 Machine 24 26 25 21 22 23 5 2 1 3 28 27 20 19 15 13 11 9 7 4 29 30 18 17 16 14 12 10 8 6
 Cell 1 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0
 Cell 2 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0
 Cell 3 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1
 Cell 4 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0
 Cell 5 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0

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Lamba, K., Kumar, R., Mishra, S. et al. Sustainable dynamic cellular facility layout: a solution approach using simulated annealing-based meta-heuristic. Ann Oper Res 290, 5–26 (2020). https://doi.org/10.1007/s10479-019-03340-w

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Keywords

  • Dynamic cellular facility layout
  • Electrical energy consumption (EEC)
  • Cellular manufacturing systems (CMS)
  • Inter/intra cell
  • Sustainable
  • Simulated annealing (SA)