DATA ENVELOPMENT ANALYSIS BASED METAMODELING FOR MULTI OBJECTIVE SIMULATION OPTIMIZATION IN A MANUFACTURING LINE

Year 2019, Volume: 37 Issue: 4, 1435 - 1449, 01.12.2019

Onder Belgın

Abstract

To adapt changing market conditions, firms must make quick decisions and response them as fast as possible. Simulation is a powerful tool to analyze the effects of changes in an industrial or service system on a virtual environment and usage of simulation models have become widespread with the developments in computers. Simulation isn’t adequate to optimize the system parameters and additional methods are needed to integrate with simulation for optimization. In this study, a multi-objective optimization of a production system is considered. In this system, management aims to decide the optimal combination of workers in considered workstations. To cope with the problem a Data Envelopment Analysis (DEA) based metamodel is obtained and this metamodel is used as the objective function of the mathematical model with relevant constraints. In metamodeling stage two level factorial design is used.

Keywords

Simulation optimization, multi-objective simulation optimization, data envelopment analysis, metamodeling.

References

• [1] Jahangirian, M., Eldabi, T., Naseer, A., Stergioulas, L.K. Young, T. (2010) Simulation in manufacturing and business: A review, European Journal of Operational Research 203, 1–13.
• [2] Ammeri, A., Hachicha, W., Chabchoub, H., Masmoudi, F. (2011) A comprehensive litterature review of monoobjective simulation optimization methods, Advances in Production Engineering & Management 6(4), 291-302.
• [3] Medaglia, A.L., Fang, S.-C., Nuttle, H. L. W. (2002) Fuzzy controlled simulation optimization, Fuzzy Sets and Systems 127(1), 65–84.
• [4] Azadivar, F. (1999) Simulation optimization methodologies, Proceedings of the 1999 Winter Simulation Conference, 93-100.
• [5] Miranda, R.C., Montevechi, J.A.B., Silva, A.F., Marins, F.A.S. (2014) A New Approach to Reducing Search Space and Increasing Efficiency in Simulation Optimization Problems via the Fuzzy-DEA-BCC, Mathematical Problems in Engineering 1-15.
• [6] Lee, L.H., Chew, E.K., Teng, S., Chen, Y. (2008) Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem, European Journal of Operational Research 189, 476-491.
• [7] Azadeh, A., Jebreili, S., Chang, E., Saberi, M., Hussain, O.K. (2017) An integrated fuzzy algorithm approach to factory floor design incorporating environmental quality and health impact, International Journal of System Assurance Engineering and Management 8(4), 2071-2082.
• [8] Miranda, R.D., Montevechi, J.A.B., da Silva, A.F. Marins, A.S. (2017) Increasing the efficiency in integer simulation optimization: Reducing the search space through data envelopment analysis and orthogonal arrays, European Journal of Operational Research 262(2), 673-681.
• [9] Shadkam, E., Bijari, M. (2013) Multi-objective simulation optimization for selection and determination of order quantity in supplier selection problem under uncertainty and quality criteria, International Journal of Advanced Manufacturing Technology 93(5), 161-173.
• [10] Zarrin, M., Azadeh, A. (2017) Simulation optimization of lean production strategy by considering resilience engineering in a production system with maintenance policies, Simulation-Transactions of the Society for Modeling and Simulation International 93(1), 49-68.
• [11] Azadeh, A., Ahvazi, M.P., Haghighii, S.M., Keramati, A. (2016) Simulation optimization of an emergency department by modeling human errors, Simulation Modelling Practice and Theory 67, 117-136.
• [12] Azadeh, A., Moradi, B. (2014) Simulation Optimization of Facility Layout Design Problem with Safety and Ergonomics Factors, International Journal of Industrial Engineering-Theory Applications and Practice 21(4), 209-230.
• [13] Lin, R.C., Sir, M.Y., Pasupathy, K.S. (2013) Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services, Omega-International Journal of Management Science 41(5), 881-892.
• [14] Villarreal-Marroquin, M.G., Svenson, J.D., Sun, F.F., Santner, T.J., Dean, A., Castro, J.M. (2013) A comparison of two metamodel-based methodologies for multiple criteria simulation optimization using an injection molding case study, Journal of Polymer Engineering 33(3), 193-209.
• [15] Mirfenderesgi, G., Mousavi, S.J. (2016) Adaptive meta-modeling-based simulation optimization in basin-scale optimum water allocation: a comparative analysis of meta-models, Journal of Hydroinformatics 18(3), 446-465.
• [16] Dengiz, B., Ic, Y.T., Belgin, O. (2015) A meta-model based simulation optimization using hybrid simulation-analytical modeling to increase the productivity in automotive industry, Mathematics and Computers in Simulation 120, 120–128.
• [17] Pedrielli, G., Ng, S.H. (2016) G-STAR: A new kriging-based trust region method for global optimization, 2016 Winter Simulation Conference 803-814.
• [18] Ryu, J., Kim, S, Wan, H. (2009) Pareto front approximation with adaptive weighted sum method in multiobjective simulation optimization, Proceedings of the 2009 Winter Simulation Conference 623-633.
• [19] Yang, T., Tseng, L. (2002). Solving a multi-objective simulation model using a hybrid response surface method and lexicographical goal programming approach—a case study on integrated circuit ink-marking machines, Journal of the Operational Research Society 53, 211-221.
• [20] Zakerifar, M., Biles, W.E., Evans, G.W. (2009) Kriging metamodeling in multi-objective simulation optimization, Proceedings of the 2009 Winter Simulation Conference 2115-2122.
• [21] Dengiz, B., Bektas, T., Ultanir, A. E. (2006) Simulation optimization based DSS application: A diamond tool production line in industry, Simulation Modelling Practice and Theory 14, 296–312.
• [22] Yang, T., Kuo, Y., Chou, P. (2005) Solving a multiresponse simulation problem using a dual-response system and scatter search method, Simulation Modelling Practice and Theory 13, 356-369.
• [23] Yang, T., Chou, P. (2005) Solving a multiresponse simulation-optimization problem with discrete variables using a multiple-attribute decision-making method, Mathematics and Computers in Simulation 68, 9-21.
• [24] Kuo, Y.,Yang, T., Huang, G.W. (2008) The use of a grey-based Taguchi method for optimizing multi-response simulation problems, Engineering Optimization 40(6), 517-528.
• [25] Belgin, O. (2015) Multi-objective simulation optimization using grey-based Taguchi method with fuzzy AHP weighting, Sigma Journal Engineering and Natural Sciences 33(3), 341-350.
• [26] Nezhad, A.M., Mahlooji, H. (2014) An artificial neural network meta-model for constrained simulation optimization, Journal of the Operational Research Society 65(8), 1232-1244.
• [27] Shirazi, B., Mahdavi, I., Mahdavi-Amiri, N. (2011) iCoSim-FMS: An intelligent co-simulator for the adaptive control of complex flexible manufacturing systems, Simulation Modelling Practice and Theory 19(7), 1668-1688.
• [28] Barton, R.R., Meckesheimer, M. (2006) Metamodel-based simulation optimization, S.G. Henderson and B.L. Nelson (Eds.), Handbook in OR & MS 13, Elsevier, 539.
• [29] Fanou EH, Wang X. Assessment of transit transport corridor efficiency of landlocked African countries using data envelopment analysis. S Afr J Sci. 2018;114(1/2), Art. # 2016-0347, 7 pages.
• [30] Charnes, A., Cooper, W. W., Rhodes, E. (1978) Measuring the efficiency of decision making units. European Journal of Operational Research 2, 429–444.
• [31] Nahra, T.A., Mendez, D., Alexander, J.A. (2009) Employing super-efficiency analysis as an alternative to DEA: An applicationin outpatient substance abuse treatment. European Journal of Operational Research, 196, 1097-1106.
• [32] Andersen, P., Petersen, N.C. (1993) A procedure for ranking efficient units in Data Envelopment Analysis, Management Science, 39, 1261-1264.
• [33] Martic, M. M., Novakovic, M. S., Baggia, A. (2009) Data Envelopment Analysis - Basic Models and their Utilization, Organizacija 42(2), 37-43.
• [34] Montgomory, D.C. (1997) Design and analysis of experiments, John Wiley & Sons, 290.
• [35] Zhu, J. (2014) Quantitative Models for Performance Evaluation and Benchmarking, International Series in Operations Research & Management Science 213.
• [36] Scheel, H. (2000) EMS: Efficiency Measurement System User’s Manual, 1-12.