Solving flexible flow-shop problem using a hybrid multi criteria Taguchi based computer simulation model and DEA approach

Document Type : Research Paper

Authors

Department of industrial engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract

In this paper, an efficient approach for production line policy and planning problem is presented. Here, all of activities are simulated by incorporating learning effects using historical data. After validation process, “what if analysis” is carried out for different scenarios derived from the Taguchi method. Several performance measures estimated for all simulation runs. Then for such homogenized multi-criteria problem, data envelopment analysis (DEA) is used to select the preferred policy. In order to show the applicability of the proposed approach, the data for a series production line is used. Results show that the proposed approach could help managers to identify the preferred strategy considering and investigating various parameters and policies. Finally this study introduces an integrated multi-criteria approach for optimum maintenance policy and planning. In this algorithm, many relevant parameters cover any uncertainty using statistical distribution. We used DEA as a multi-criteria decision making techniques to seek more appropriate assignment. Therefore, using a combination of computer simulation model and an attribute-deductive tool such as DEA, a near optimal solution can be achieved.

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References

Apornak, A., Raissi, S., Javadi, M., Ahmadizadeh-Tourzani, N., & Kazem, A. (2018). A simulation-based multi-objective optimisation approach in flexible manufacturing system planning. International Journal of Industrial and Systems Engineering, 29(4), 494-506.
Al-Refaie, A., Fouad, R. H., Li, M. H., & Shurrab, M. (2014). Applying simulation and DEA to improve performance of emergency department in a Jordanian hospital. Simulation Modeling Practice and Theory, 41, 59-72.
Azadeh, A., Anvari, M., Ziaei, B., & Sadeghi, K. (2010). An integrated fuzzy DEA–fuzzy C-means–simulation for optimization of operator allocation in cellular manufacturing systems. The International Journal of Advanced Manufacturing Technology, 46(1-4), 361-375.
Azadeh, A., Sheikhalishahi, M., Firoozi, M., & Khalili, S. M. (2013). An integrated multi-criteria Taguchi computer simulation-DEA approach for optimum maintenance policy and planning by incorporating learning effects. International Journal of Production Research, 51(18), 5374-5385.
 Azadeh, A., Ghaderi, S. F., & Izadbakhsh, H. (2008). Integration of DEA and AHP with computer simulation for railway system improvement and optimization. Applied Mathematics and Computation, 195(2), 775-785.
Azadeh, A., Bidokhti, B., & Sakkaki, S. M. R. (2005). Design of practical optimum JIT systems by integration of computer simulation and analysis of variance. Computers & Industrial Engineering, 49(4), 504-519.
Carson, Y., & Maria, A. (1997, December). Simulation optimization: methods and applications. In Proceedings of the 29th conference on winter simulation (pp. 118-126). IEEE Computer Society.
Chaouch, I., Driss, O. B., & Ghedira, K. (2019). A novel dynamic assignment rule for the distributed job shop scheduling problem using a hybrid ant-based algorithm. Applied Intelligence, 49(5), 1903-1924.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
Cordero, J. M., Santín, D., & Sicilia, G. (2015). Testing the accuracy of DEA estimates under endogeneity through a Monte Carlo simulation. European Journal of Operational Research, 244(2), 511-518.
Doyle, J., & Green, R. (1994). Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. Journal of the operational research society, 567-578.
Fu, Y., Wang, H., Wang, J., & Pu, X. (2020). Multiobjective Modeling and Optimization for Scheduling a Stochastic Hybrid Flow Shop With Maximizing Processing Quality and Minimizing Total Tardiness. IEEE Systems Journal.
Gheisariha, E., Tavana, M., Jolai, F., & Rabiee, M. A simulation–optimization model for solving flexible flow shop scheduling problems with rework and transportation. Mathematics and Computers in Simulation, 180, 152-177.
Hasani, A., & Hosseini, S. M. H. (2020). A bi-objective flexible flow shop scheduling problem with machine-dependent processing stages: Trade-off between production costs and energy consumption. Applied Mathematics and Computation, 386, 125533.
Ic, Y. T., Dengiz, B., Dengiz, O., & Cizmeci, G. (2014, December). Topsis based taguchi method for multi-response simulation optimization of flexible manufacturing system. In Simulation Conference (WSC), 2014 Winter (pp. 2147-2155). IEEE.
Kong, M., Pei, J., Xu, J., Liu, X., Yu, X., & Pardalos, P. M. (2019). A robust optimization approach for integrated steel production and batch delivery scheduling with uncertain rolling times and deterioration effect. International Journal of Production Research, 1-23.
Li, M., Lei, D., & Cai, J. (2019). Two-level imperialist competitive algorithm for energy-efficient hybrid flow shop scheduling problem with relative importance of objectives. Swarm and Evolutionary Computation, 49, 34-43.
Lin, J. T., & Chen, C. M. (2015). Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing. Simulation Modeling Practice and Theory, 51, 100-114.
Mak, L. C., Wong, W. K., & Leung, Y. S. (2014). A simulation analysis of the impact of production lot size and its interaction with operator competence on manufacturing system performance. Simulation Modeling Practice and Theory, 49, 203-214.
Rane, A. B., Sudhakar, D. S. S., Sunnapwar, V. K., & Rane, S. (2015, January). Improving the performance of assembly line: Review with case study. In Nascent Technologies in the Engineering Field (ICNTE), 2015 International Conference on (pp. 1-14). IEEE.
Sahrawian, Rashed, and Dehghan, Elham, (2010) , the selection of the best - case scenario in a matter of FSMP with the use of the simulation and data envelopment analysis , the second largest international conference, operations research, Iran, babolsar , mazandaran University.
Sexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. New Directions for Program Evaluation, 1986(32), 73-105.
Sharma, P., & Jain, A. (2017). Effect of routing flexibility and sequencing rules on performance of stochastic flexible job shop manufacturing system with setup times: Simulation approach. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 231(2), 329-345.
Sharma, P., & Jain, A. (2015). Effect of routing flexibility and sequencing rules on performance of stochastic flexible job shop manufacturing system with setup times: Simulation approach. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 0954405415576060.
Xu, W. J., He, L. J., & Zhu, G. Y. (2019). Many-objective flow shop scheduling optimisation with genetic algorithm based on fuzzy sets. International Journal of Production Research, 1-25.
Yildiz, A. R. (2013). Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. Applied Soft Computing, 13(3), 1433-1439.
Zhang, R., Chiang, W. C., & Wu, C. (2014). Investigating the impact of operational variables on manufacturing cost by simulation optimization. International Journal of Production Economics, 147, 634-646.
Zheng, J., Wang, L., & Wang, J. J. (2020). A cooperative coevolution algorithm for multi-objective fuzzy distributed hybrid flow shop. Knowledge-Based Systems, 105536.
 

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History

  • Receive Date: 29 August 2020
  • Revise Date: 19 November 2020
  • Accept Date: 29 November 2020

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