Simulation-based optimization using DEA and DOE in production systems

Document Type : Article

Authors

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

Abstract

Production System (PS) is the process of planning, organizing, directing and controlling the tactical and strategic planning of the different components of the company, to transform inputs into finished products which must be effectively managed.the present study aimed to increasing the efficiency and determining the useful methods to evaluate and optimize the performance in different part of PS. To this end, an integrated Discrete-event Simulation (DES), Design of Experiments (DOE), Data Envelopment Analysis (DEA), and Multi-attribute Decision Making (MADM) models were implemented to analyze and optimize the real PS process. In the case study of the automobile manufacturing industry in Iran, the accurate analysis was applied to the proposed approach and its different aspects were considered as well. The results indicated that the proposed approach is a practical way for evaluating and optimizing the performance of different part of PS, compared to previous models and helps the manufacturing companies to make efficient decisions regarding increasing productivity while decreasing the essential problems.

Keywords

References

References:
1. Martins, L.M., Fernandes, N.O.G., Varela, M.L.R., et al. "Comparative study of autonomous production control methods using simulation", Simulation Modelling Practice and Theory, 104, 102142 (2020). DOI: 10.1016/j.simpat.2020.102142.
2. Abdulaziz, O., Cheng, J.K., Tahar, R.M., et al. "A hybrid simulation model for green logistics assessment in automotive industry", Procedia Engineering, 100, pp. 960-969 (2015). DOI: 10.1016/j.proeng.2015.01.455.
3. Dumetz, L., Gaudreault, J., Thomas, A., et al. "A simulation framework for the evaluation of production planning and order management strategies in the sawmilling industry", IFAC-PapersOnLine, 48(3), pp. 622-627 (2015). DOI: 10.1016/j.ifacol.2015.06.151.
4. Heidarzadeh, S., Doniavi, A., and Solimanpur, M. "Development of supply chain strategy in the Iranian automotive industry based on system dynamics and game theory", Scientia Iranica, 24(6), pp. 3345-3354 (2017). DOI: 10.24200/sci.2017.4393.
5. Vieira, G.E., Kuck, M., Frazzon, E., et al. "Evaluating the robustness of production schedules using discreteevent simulation", IFAC-PapersOnLine, 50(1), pp. 7953-7958 (2017). DOI: 10.1016/j.ifacol.2017.08.896.
6. Muller, R. (Prof. Dr-Ing), Horauf, L., Speicher, C., et al. "Simulation based online production planning", Procedia Manufacturing, 38, pp. 1473-1480 (2019). DOI: 10.1016/j.promfg.2020.01.140.
7. Zahraee, S.M., Rohani, J.M., and Wong, K.Y. "Application of computer simulation experiment and response surface methodology for productivity improvement in a continuous production line: Case study", Journal of King Saud University- Engineering Sciences, 30(3), pp. 207-217 (2018). DOI: 10.1016/j.jksues.2018.04.003.
8. Caterino, M., Greco, A., D'Ambra, S., et al. "Simulation techniques for production lines performance control", Procedia Manufacturing, 42, pp. 91-96 (2020). DOI: 10.1016/j.promfg.2020.02.027.
9. Pawlewski, P. "Using PFEP for simulation modeling of production system", Procedia Manufacturing, 17, pp. 811-818 (2018). DOI: 10.1016/j.promfg.2018.10.132.
10. Motlagh, M.M., Azimi, P., Amiri, M., et al. "An efficient simulation optimization methodology to solve a multi-objective problem in unreliable unbalanced production lines", Expert Systems with Applications, 138, pp. 112-836 (2019). DOI: 10.1016/j.eswa.2019.112836.
11. Vaisi, B. and Ebrahimi, A. "Utilizing computer simulation and DEAGP to enhance productivity in a manufacturing system", International Journal of Data Envelopment Analysis, 3(5), pp. 857-866 (2015).
12. Galankashi, M.R., Fallahiarezoudar, E., Moazzami, A., et al. "Performance evaluation of a petrol station queuing system: A simulation-based design of experiments study", Advances in Engineering Software, 92, pp. 15- 26 (2016). DOI: 10.1016/j.advengsoft.2015.10.004.
13. Torabi, M. and Mahlooji, H. "An integrated simulation-DEA approach to multi-criteria ranking of scenarios for execution of operations in a construction project", Iranian Journal of Management Studies, 9(4), pp. 801-827 (2016). DOI: 10.22059/ijms.2017.60097.
14. Kaylani, H. and Atieh, A.M. "Simulation approach to enhance production scheduling procedures at a pharmaceutical company with large product mix", Procedia CIRP, 41, pp. 411-416 (2016). DOI:10.1016/j.procir.2015.12.072.
15. Kumar, S.R., Nathan, V.N., Ashique, S.I.M., et al. "Productivity enhancement and cycle time reduction in Toyota production system through jishuken activitycase study", Materials Today: Proceedings, 37(2), pp. 964-966 (2020). DOI: 10.1016/j.matpr.2020.06.181.
16. Gyulai, D., Pfeiffer, A., Kadar, B., et al. "Simulationbased production planning and execution control for reconfigurable assembly cells", Procedia CIRP, 57, pp. 445-450 (2016). DOI: 10.1016/j.procir.2016.11.077.
17. Marlin, B. and Sohn, H. "Using DEA in conjunction with designs of experiments: an approach to assess simulated futures in the Afghan educational system", Journal of Simulation, 10(4), pp. 272-282 (2017). DOI: 10.1057/jos.2015.14.
18. Berenguer, G., Lyer, A.V., et al. "Disentangling the efficiency drivers in country-level global health programs: An empirical study", Journal of Operations Management, 45, pp. 30-43 (2016). DOI: 10.1016/j.jom.2016.05.005.
19. Amini, A. and Alinezhad, A. "Integrating DEA and group AHP for efficiency evaluation and the identification of the most efficient DMU", International Journal of Supply and Operations Management, 4(4), pp. 318-327 (2017). DOI: 10.22034/2017.4.03.
20. Biuki, M., Kazemi, A., and Alinezhad, A. "An integrated location-routing-inventory model for sustainable design of a perishable products supply chain network", Journal of Cleaner Production, 260(10), pp. 1-14 (2020). DOI: 10.1016/j.jclepro.2020.120842.
21. Mirmozaffari, M. and Alinezhad, A. "Window analysis using two-stage DEA in heart hospitals", International Conference on Innovation in Science, Engineering Computer and Technology (2017).
22. Zarbakhshnia, N. and Jamali Jaghdani, T. "Sustainable supplier evaluation and selection with a novel two-stage DEA model in the presence of uncontrollable inputs and undesirable outputs: a plastic case study", International Journal of Advanced Manufacturing Technology, 97, pp. 2933-2945 (2018). DOI:10.1007/s00170-018-2884-y.
23. Park, S., Ok, C., and Ha, C. "A stochastic simulation-based holistic evaluation approach with DEA for vendor selection", Computers and Operations Research, 100, pp. 368-378 (2018). DOI: 10.1016/j.cor.2017.08.005.
24. Ebrahiminejad, A., Tavana, M., and Sanatos-Arteaga, F.J. "An integrated data envelopment analysis and simulation method for group consensus ranking", Mathematics and Computers in Simulation, 119, pp.1-17 (2016). DOI: 10.1016/j.matcom.2015.08.022.
25. Azadeh, A., Nazari, T., and Charkhand, H. "Optimization of facility layout design problem with safety and environmental factors by stochastic DEA and simulation approach", International Journal of Production Research, 53(11), pp. 3370-3389 (2015). DOI:10.1080/00207543.2014.986294.
26. Kersuliene, V., Zavadskas, E.K., and Turskis, Z. "Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA)", Journal of Business Economics and Management, 11(2), pp. 243-258 (2010). DOI: 10.3846/jbem.2010.12.
27. Farahmand, M. and Desa, M.I. "RED: a new method for performance ranking of large decision making units", Soft Computing, 21(5), pp. 1271-1290 (2017). DOI: 10.1007/s00500-015-1860-9.
28. Aghaie, A. and Heidary, M.H. "Simulation-based optimization of a stochastic supply chain considering supplier disruption: Agent-based modeling and reinforcement learning", Scientia Iranica, 26(6), pp. 3780-3795 (2019). DOI: 10.24200/sci.2018.20789.
29. Pergher, I., Frej, E.A., Roselli, L.R.P., et al.  Integrating simulation and FITradeoff method for scheduling rules selection in job-shop production systems", International Journal of Production Economics, 227, 107669 (2020). DOI: 10.1016/j.ijpe.2020.107669.
30. Laurindo, Q.M.G., Peixoto, T.A., and Rangel, J.J.A. "Communication mechanism of the discrete event simulation and the mechanical project software for manufacturing systems", Journal of Computational Design and Engineering, 6(1), pp. 70-80 (2019). DOI: 10.1016/j.jcde.2018.02.005.
31. Piccinini, A., Previdi, F., Cimini, C., et al. "Discrete event simulation for the reconfiguration of a  flexible manufacturing plant", IFAC-PapersOnLine, 51(11), pp. 465-470 (2018). DOI: 10.1016/j.ifacol.2018.08.362.
32. Hatami-Marbini, A., Sajadi, S.M., and Malekpour, H. "Optimal control and simulation for production planning of network failure-prone manufacturing systems with perishable goods", Computers and Industrial Engineering, 146, p. 106614 (2020). DOI: 10.1016/j.cie.2020.106614.
33. Montgomery, D.C., Design and Analysis of Experiments, Wiley, 9th Edn. (2017).
34. Garg, H. and Kaur, G. "Extended TOPSIS method for multi-criteria group decision-making problems under cubic intuitionistic fuzzy environment", Scientia Iranica, 27(1), pp. 396-410 (2020). DOI:10.24200/sci.2018.5307.1194.
35. Alinezhad, A., Sarrafha, K., and Amini, A. "Sensitivity analysis of SAW technique: the impact of changing the decision making matrix elements on the final ranking of alternatives", Iranian Journal of Operations Research, 5(1), pp. 82-94 (2014). DOI:10.1016/j.ijor.2014.10.132.
36. Mousavi-Nasab, S.H. and Sotoudeh-Anvai, A. "A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems", Materials & Design, 121, pp. 237-253 (2017). DOI: 10.1016/j.matdes.2017.02.041.
37. Alinezhad, A. and Khalili, J., New Methods and Applications in Multiple Attribute Decision Making (MADM), International Series in Operations Research & Management Science, Springer (2017).
38. Alinezhad, A., Makui, A., and Mavi, R.K.M. "An inverse DEA model for inputs/outputs estimation with respect to decision maker's preferences: The case of Refah bank of IRAN", Mathematical Sciences, 1(1,2), pp. 61-70 (2007).
39. Omrani, H., Keshavarz, M., and Ghaderi, S.F. "Evaluation of supply chain of a shipping company in Iran by a fuzzy relational network data envelopment analysis model", Scientia Iranica, 25(2), pp. 868-890 (2018). DOI: 10.24200/sci.2017.4415.
Scientia Iranica
Volume 29, Issue 6 - Serial Number 6
Transactions on Industrial Engineering (E)
November and December 2022
Pages 3470-3488

Files

Share

How to cite

Statistics