Application of simulated annealing algorithm for multi-criteria operation planning in flexible manufacturing systems

Document Type : Article

Authors

1 Department of Mechanical Engineering, Khayyam University of Mashhad, Mashhad, Iran

2 Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, P.O. Box 9177948944, Iran

Abstract

In this paper, a multi objective model in which operation planning and tool assignment in a flexible manufacturing system (FMS) has been considered simultaneously. In this regard, the main characteristics of FMS have been analyzed. Then, a comprehensive model, including major system parameters and cost components have been designed and presented. The proposed model contains cost factors including machining cost, earliness and tardiness penalties, tool and part movement or switch costs and idle time costs of tools and machines. Then, a multi-objective model for the problem has been proposed, in which the relative importance of each cost through weighting these costs based on the decision making goals and sum of the mentioned costs have been considered simultaneously. Based on the complex nature of the problem, standard solution techniques have not been employed. Therefore, to reduce computational times, simulated annealing (SA) algorithm has been used for about 30 minutes (10,000 movements). The total production costs has been decreased from 7,000 to 4333 units using SA algorithm. Based on the results, 38% reduction in total production costs has been achieved. Computational results revealed that the proposed method is quite efficient in multi objective optimization of FMS within a short computational time.

Keywords

Main Subjects

References

References:
1. Baozhong, Q. and Hongying, W. "The application of FCS-based architecture in the  flexible manufacturing system", Energy Procedia, 17(2), pp. 1395-1400 (2012).
2. Molenda, P., Drews, T., Oechsle, O., et al. "A simulation-based framework for the economic evaluation of  flexible manufacturing systems", Procedia CIRP, 63(15), pp. 201-206 (2017).
3. Lucas, A., Richardson, S., and Teixeira, R.M. "Modeling and control of  flexible context-dependent manufacturing systems", Information Sciences, 421(12), pp. 1-14 (2017).
4. Abu-Ali, M.G. and Shouman, M.A. "Effect of dynamic and static dispatching strategies on dynamically planned and unplanned FMS", Journal of Materials Processing Technology, 148(1), pp. 121-132 (2004).
5. Selim, M., Ghosh, B., and Gunes, D. "Scheduling with tool changes to minimize total completion time: Basic results and SPT performance", European Journal of Operational Research, 12(2), pp. 784-790 (2004).
6. Pacciarelli, D. "Loading parts and tools in a flexible manufacturing system", Universita Degli Studi DI ROMA TRE, pp. 127-145 (1998).
7. Gamila, M.A. and Motavalli, S. "A modeling technique for loading and scheduling problems in FMS", Robotics and Computer Integrated Manufacturing, 19(5), pp. 45-54 (2003).
8. Selim Akturk, M. and Onen, S. "Dynamic lot sizing and tool management in automated manufacturing systems", Computers and Operations Research, 29(3), pp. 1059-1079 (2002).
9. Zhijun, X. and Dehua, X. "Single-machine scheduling with workload-dependent tool change durations and equal processing time jobs to minimize total completion time", Journal of Scheduling, 21(3), pp. 461-482 (2018).
10. Selim Akturk, M., Ghosh, B., and Gunes, E.D. "Scheduling with tool changes to minimize total completion time: Basic results and SPT performance", European Journal of Operational Research, 157(12), pp. 784-790 (2004).
11. Bhosale, K.C. and Pawar, P.J. "Material  flow optimisation of flexible manufacturing system using Real Coded Genetic Algorithm (RCGA)", IMME17, Materials Today: Proceedings, 5(2), pp. 7160-7167 (2018).
12. Suna, L., Lina, L., Wanga, Y., et al. "A bayesian optimization-based evolutionary algorithm for flexible job shop scheduling", Procedia Computer Science, 61(16), pp. 521-526 (2015).
13. Gang, X. and Quan, Q. "Research on planning scheduling of flexible manufacturing system based on multilevel list algorithm", Procedia CIRP, 56(4), pp. 569- 573, 9th International Conference on Digital Enterprise Technology-DET2016-Intelligent Manufacturing in the Knowledge Economy Era (2016).
14. Herrmann, J.W., Ioannou, G., Minis, I., et al. "Design of material flow networks in manufacturing facilities", Journal of Manufacturing Systems, 14(4), pp. 277-289 (1995).
15. Herrmann, J.W., Ioannou, G., Minis, I., et al. "Minimization of acquisition and operational costs in horizontal material handling system design", IIE Transactions, 31(4), pp. 679-693 (1999).
16. Mohammed, A. "Analysis of the impact of routing flexibility on the performance of flexible system", International Journal of Industrial Engineering: Theory, Applications and Practice, 17(4), pp. 134-142 (2010).
17. Jahromi, M.H.M.A., Tavakkoli-Moghaddam, R., Makui, A., et al. "A novel mathematical model for a scheduling problem of dynamic machine-tool selection and operation allocation in a flexible manufacturing system a modified evolutionary algorithm", Scientia Iranica, E., 24(2), pp. 765-777 (2017).
18. Chawla, V.K., Chanda, A.K., Angra, S., et al. "Coexistent scheduling in the tandem flow path configuration of a flexible manufacturing system by using an advanced grey wolf optimizer", Scientia Iranica, E., 29(6), pp. 3404-3417 (2022).
19. Abbaszadeh, N., Asadi-Gangraj, A., and Emami, S. "Flexible flow shop scheduling problem to minimize makespan with renewable resources", Scientia Iranica, E., 28(3), pp. 1853-1870 (2021).
20. Priore, P., Ponte, B., Puente, J., et al. "Learningbased scheduling of flexible manufacturing systems using ensemble methods", Computers and Industrial Engineering, 126(6), pp. 282-291 (2018).
21. Azadi Moghaddam, M., Golmezerji, R., and Kolahan, F. "Simultaneous optimization of joint edge geometry and process parameters in gas metal arc welding using integrated ANN-PSO approach", Scientia Iranica, B., 24(1), pp. 260-273 (2017).
22. Azadi Moghaddam, M., Golmezerji, R., and Kolahan, F. "Multi-variable measurements and optimization of GMAW parameters for API-X42 steel alloy using a hybrid BPNN-PSO approach", Measurement, 92(6), pp. 279-287 (2016).
23. Rabbani, M., Manavizadeh N., and Shabanpour, N. "Sequencing of mixed models on U-shaped assembly lines by considering e-ective help policies in make-toorder environment", Scientia Iranica, E., 24(3), pp. 1493-1504 (2017).
24. Akram, K., Kamal, K., and Zeb, A. "Fast simulated annealing hybridized with quenching for solving job shop scheduling problem", Applied Soft Computing, 49(2), pp. 510-523 (2016).
25. Franzin, A. and Stutzle, T. "Revisiting simulated annealing: A component-based analysis", Computers and Operations Research, 104(8), pp. 191-206 (2019).
26. Kerr, A. and Mullen, K. "A comparison of genetic algorithms and simulated annealing in maximizing the thermal conductance of harmonic lattices", Computational Materials Science, 157(45), pp. 31-36 (2019).
27. Esfandeh, S. and Kaboli, M. "Using simulated annealing optimization algorithm for prioritizing protected areas in Alborz province, Iran", Environmental Nanotechnology, Monitoring and Management, Accepted Manuscript, 11, 100211 (2019).
Scientia Iranica
Volume 30, Issue 5
Transactions on Mechanical Engineering (B)
September and October 2023
Pages 1535-1547

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