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Manuscript ID : IMJ-2012-1520 (R1) Visit : 139 Page: 193 - 215

10.30495/imj.2022.689679

Article Type: Original Research

Investigating the feasibility of implementing scalable policies for optimal production capacity based on a reconfigurable production system with a dynamic systems approach

Subject Areas : Industrial Management

rohollah ranjbar 1 ( department of industrial management, islamic azad university,firoozkooh branch,firoozkooh, iran )
seyed ahmad shayan nia 2 ( department of industrial management, firoozkooh branch, firoozkooh , iran )
amirmehdi miandaragh 3 ( department of mathemathics,islamic azad university,firoozkooh branch, firoozkooh iran )
mohammadreza lotfi 4 ( department of industrial engineering,islamic azad university, firoozkooh, iran )

Received: 2020-12-31 Accepted : 2021-10-15 Published : 2022-01-21

Keywords: Reconfigurable production system, Dynamic systems, scalability of production capacity,

Abstract :

. In this research, a new model for evaluating capacity policies based on new product orders, financial inventory and budget based on dynamic systems is presented. The scope of research is the National Iranian Gas Company. The data obtained through exploratory interviews were selected from the experts of the National Iranian Gas Company. Their effects on each other were extracted and after expressing the dynamics hypothesis, a sample of the related cause-effect was prepared. After plotting the causal loops and in order to analyze the parameters involved in the model, the flow accumulation diagram was drawn. The designed model was implemented and the behavior of the variables was investigated and then the model was validated. The model includes four sections: orders, production, research and development of capacity building and the financial section. The results showed that the company adjusts its production capacity and inventory level by considering the order rate received from customers and considered the output of its product with the attitude of no stock output and as you saw in the model with this approach was able to Match production capacity and inventory level with market needs.

References:


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_||_

  1. Abdi, M. R., & Labib, A. W. (2003). A design strategy for reconfigurable manufacturing systems (RMSs) using analytical hierarchical process (AHP): a case study. International Journal of production research, 41(10), 2273-2299.
    2.
  2. Andersen, A. L., Brunoe, T. D., & Nielsen, K. (2015, September). Reconfigurable manufacturing on multiple levels: literature review and research directions. In IFIP International conference on advances in production management systems(pp. 266-273). Springer, Cham.
    3.
  3. Andersen, A. L., Brunoe, T. D., & Nielsen, K. (2019). Engineering education in changeable and reconfigurable manufacturing: Using problem-based learning in a learning factory environment. Procedia Cirp, 81, 7-12.
    4.
  4. Asghar, E., Baqai, A. A., & Homri, L. (2018). Optimum machine capabilities for reconfigurable manufacturing systems. The International Journal of Advanced Manufacturing Technology, 95(9), 4397-4417.
    5.
  5. Ashraf, M., & Hasan, F. (2018). Configuration selection for a reconfigurable manufacturing flow line involving part production with operation constraints. The international journal of advanced manufacturing technology, 98(5), 2137-2156.
    6.
  6. Bensmaine, A., Benyoucef, L., and Dahane, D. (2013). A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment. Computers & Industrial Engineering, 66(3), 519–524.
    7.
  7. Bortolini, M., Ferrari, E., Galizia, F. G., & Regattieri, A. (2021). An optimisation model for the dynamic management of cellular reconfigurable manufacturing systems under auxiliary module availability constraints. Journal of Manufacturing Systems, 58, 442-451.
    8.
  8. Bortolini, M., Galizia, F. G., & Mora, C. (2018). Reconfigurable manufacturing systems: Literature review and research trend. Journal of manufacturing systems, 49, 93-106.
    9.
  9. Bortolini, M., Galizia, F. G., & Mora, C. (2018). Reconfigurable manufacturing systems: Literature review and research trend. Journal of manufacturing systems, 49, 93-106.
    10.
  10. Bortolini, M., Galizia, F. G., & Mora, C. (2019). Dynamic design and management of reconfigurable manufacturing systems. Procedia manufacturing, 33, 67-74.
    11.
  11. Choi, Y. C., & Xirouchakis, P. (2015). A holistic production planning approach in a reconfigurable manufacturing system with energy consumption and environmental effects. International Journal of Computer Integrated Manufacturing, 28(4), 379-394.
    12.
  12. Deif, A. M., & ElMaraghy, H. A. (2007). Assessing capacity scalability policies in RMS using system dynamics. International journal of flexible manufacturing systems, 19(3), 128-150.
    13.
  13. Dou, J., Li, J., and Su, C. (2016). Bi objective optimization of integrating configuration generation and scheduling for reconfigurable flow lines using NSGA-II. The International Journal of Advanced Manufacturing Technology, 86(5-8), 1945–1962.
    14.
  14. Gao, Guibing., Yue, Wenhui, Wang, Junshen., Ou, Wenchu. (2020). Structural-vulnerability assessment of reconfigurable manufacturing system based on universal generating function, Reliability Engineering & System Safety, 20(3): 101-107.
    15.
  15. Haddou Benderbal, H., Dahane, M., & Benyoucef, L. (2017). Flexibility-based multi-objective approach for machines selection in reconfigurable manufacturing system (RMS) design under unavailability constraints. International Journal of Production Research, 55(20), 6033-6051.
    16.
  16. Hashemi-Petroodi, S. E., Dolgui, A., Kovalev, S., Kovalyov, M. Y., & Thevenin, S. (2021). Workforce reconfiguration strategies in manufacturing systems: a state of the art. International Journal of Production Research, 59(22), 6721-6744.
    17.
  17. Khan, A. S., Homri, L., Dantan, J. Y., & Siadat, A. (2020). Cost and quality assessment of a disruptive reconfigurable manufacturing system based on MOPSO metaheuristic. IFAC-PapersOnLine, 53(2), 10431-10436.
    18.
  18. Lamy, D., Delorme, X., Lacomme, P., & Fleury, G. (2020). Toward Scheduling for Reconfigurable Manufacturing Systems. IFAC-PapersOnLine, 53(2), 10443-10448.
    19.
  19. Lee, S., Ryu, K., & Shin, M. (2017). The development of simulation model for self-reconfigurable manufacturing system considering sustainability factors. Procedia manufacturing, 11, 1085-1092.
    20.
  20. Li, J., Wang, A., and Tang, C. (2014). Production planning in virtual cell of reconfiguration manufacturing system using genetic algorithm. The International Journal of Advanced Manufacturing Technology, 74(1-4), 47–64.
    21.
  21. Maganha, I., Silva, C., & Ferreira, L. M. D. (2018). Understanding reconfigurability of manufacturing systems: An empirical analysis. Journal of Manufacturing Systems, 48, 120-130.
    22.
  22. Moghaddam, S. K., Houshmand, M., & Fatahi Valilai, O. (2018). Configuration design in scalable reconfigurable manufacturing systems (RMS); a case of single-product flow line (SPFL). International Journal of Production Research, 56(11), 3932-3954.
    23.
  23. Ouaret, S., Kenné, J. P., & Gharbi, A. (2019). Production and replacement planning of a deteriorating remanufacturing system in a closed-loop configuration. Journal of Manufacturing Systems, 53, 234-248.
    24.
  24. Petroodi, S. E. H., Eynaud, A. B. D., Klement, N., & Tavakkoli-Moghaddam, R. (2019). Simulation-based optimization approach with scenario-based product sequence in a reconfigurable manufacturing system (RMS): A case study. IFAC-PapersOnLine, 52(13), 2638-2643.
    25.
  25. Singh, P. P., Madan, J., & Singh, H. (2020). A systematic approach for responsiveness assessment for product and material flow in reconfigurable manufacturing system (RMS). Materials Today: Proceedings, 28, 1643-1648.
    26.
  26. Touzout, F. A., & Benyoucef, L. (2019). Multi-objective multi-unit process plan generation in a reconfigurable manufacturing environment: a comparative study of three hybrid metaheuristics. International Journal of Production Research, 57(24), 7520-7535.
    27.
  27. Youssef, A. M., & ElMaraghy, H. A. (2008). Performance analysis of manufacturing systems composed of modular machines using the universal generating function. Journal of manufacturing systems, 27(2), 55-69.
    28.
  28. Zhang, Y., Zhao, M., Zhang, Y., Pan, R., & Cai, J. (2020). Dynamic and steady-state performance analysis for multi-state repairable reconfigurable manufacturing systems with buffers. European Journal of Operational Research, 283(2), 491-510.
    29.

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