Optimizing a fuzzy multi-objective closed-loop supply chain model considering financial resources using meta-heuristic

Document Type : Article

Authors

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

2 - Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran - School of Strategy and Leadership, Faculty of Business and Law, Coventry University, Coventry, United Kingdom

3 Department of Industrial Engineering, Islamic Azad University, Bandar-e-Anzali International Islamic Azad Branch, Bandar-e-Anzali, Guilan, Iran

4 - Department of Mechanical and Industrial Engineering, The Reliability, Risk, and Maintenance Research Laboratory (RRMR Lab), Ryerson University, Toronto, Ontario, Canada - Department of Computer Science, Distributed Systems and Multimedia Processing Laboratory (DSMP lab), Ryerson University, Toronto, Ontario, Canada

Abstract

This paper presents a multi-objective mathematical model which aims to optimize and harmonize a supply chain to reduce costs, improve quality, and achieve a competitive advantage and position using meta-heuristic algorithms. The purpose of optimization in this field is to increase quality and customer satisfaction and reduce production time and related prices. The present research simultaneously optimized the supply chain in the multi-product and multi-period modes. The presented mathematical model was firstly validated. The algorithm's parameters are then adjusted to solve the model with the multi-objective simulated annealing (MOSA) algorithm. To validate the designed algorithm's performance, we solve some examples with General Algebraic Modeling System (GAMS). The MOSA algorithm has achieved an average error of %0.3, %1.7, and %0.7 for the first, second, and third objective functions, respectively, in average less than 1 minute. The average time to solve was 1847 seconds for the GAMS software; however, the GAMS couldn't reach an optimal solution for the large problem in a reasonable computational time. The designed algorithm's average error was less than 2% for each of the three objectives under study. These show the effectiveness of the MOSA algorithm in solving the problem introduced in this paper.

Keywords

References

References:
1. Ramezani, M., Bashiri, M., and Tavakkoli- Moghaddam, R. "A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level", Applied Mathematical Modelling, 37(1-2), pp. 328-344 (2013).
2. Basu, R. and Wright, J.N., Total Supply Chain Management, Routledge (2010).
3. Amiri, A. "Designing a distribution network in a supply chain system: Formulation and efficient solution procedure", European Journal of Operational Research, 171(2), pp. 567-576 (2006).
4. Ghazanfari, M. and Fathollah, M. "A holistic view of supply chain management", Iran University of Science and Technology Publications (2006).
5. Peng, Y., Ablanedo-Rosas, J.H., and Fu, P. "A multiperiod supply chain network design considering carbon emissions", Mathematical Problems in Engineering, 2016, pp. 1-11 (2016).
6. Hassanzadeh, A.S. and Zhang, G. "A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return", Applied Mathematical Modelling, 37(6), pp. 4165-4176 (2013).
7. Vahdani, B. and Sharifi, M. "An inexact-fuzzystochastic optimization model for a closed loop supply chain network design problem", Journal of Optimization on Industrial Engineering, 12(6), pp. 7-16 (2013).
8. Pishvaee, M.S., Razmi, J., and Torabi, S.A. "An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain", Transportation Research Part E: Logistics and Transportation Review, 67, pp. 14-38 (2014).
9. Braido, G.M., Borenstein, D., and Casalinho, G.D. "Supply chain network optimization using a Tabu search based heuristic", Gest~o and Produco, 23(1), pp. 3-17 (2016).
10. Qin, Z. and Ji, X. "Logistics network design for product recovery in fuzzy environment", European Journal of Operational research, 202(2), pp. 479-490 (2010).
11. Yang, G.Q., Liu, Y.K., and Yang, K. "Multi-objective biogeography-based optimization for supply chain network design under uncertainty", Computers and Industrial Engineering, 85, pp. 145-156 (2015).
12. Avakh Darestani, S. and Pourasadollah, F. "A multiobjective fuzzy approach to closed-loop supply chain network design with regard to dynamic pricing", Journal of Optimization in Industrial Engineering, 12(1), pp. 173-194 (2019).
13. Sarkar, B., Tayyab, M., Kim, N., et al. "Optimal production delivery policies for supplier and manufacturer in a constrained closed-loop supply chain for returnable transport packaging through metaheuristic approach", Computers and Industrial Engineering, 135, pp. 987- 1003 (2019).
14. Rahimi Sheikh, H., Sharifi, M., and Shahriari, M.R. "Designing a resilient supply chain model (Case study: The welfare organization of Iran)", Journal of Industrial Management Perspective, 7(3), pp. 127-150 (2017).
15. Govindan, K., Cheng, T.C.E., Mishra, N., et al. "Big data analytics and application for logistics and supply chain management", Transportation Research Part E: Logistics and Transportation Review, 114, pp. 343-349 (2018).
16. Vanaei, H., Sharifi, M., Radfar, R., et al. "Optimizing of an integrated production-distribution system with probabilistic parameters in a multi-level supply chain network considering the backorder", Journal of Operational Research in its Applications (Applied Mathematics)-Lahijan Azad University, 16(3), pp. 123-145 (2019).
17. Mahmoudi, H., Sharifi, M., Shahriari, M.R., et al. "Solving a reverse logistic model mahmoudi for multilevel supply chain using genetic algorithm", International Journal of Industrial Mathematics, 12(2), pp. 177-188 (2020).
18. Khorram Nasab, S.H., Hosseinzadeh Lotfi, F., Shahriari, M.R., et al. "Presenting an integrated management model for electronic supply chain of product and its effect on company' performance (Case study: National Iranian South Oil Company)", Journal of Investment Knowledge, 9(34), pp. 55-70 (2020).
19. Zahedi, A., Salehi-Amiri, A., Hajiaghaei-Keshteli, M., et al. "Designing a closed-loop supply chain network considering multi-task sales agencies and multi-mode transportation", Soft Computing, 25, pp. 6203-6235 (2021).
20. Srivastava, M. and Rogers, H. "Managing global supply chain risks: e ects of the industry sector", International Journal of Logistics Research and Applications, 25(7), pp. 1091-1114 (2022).
21. Jaggi, C.K., Hag, A., and Maheshwari, S. "Multiobjective production planning problem for a lock industry: A case study and mathematical analysis", Revista Investigacion Operacional, 41, pp. 893-901 (2020).
22. Talwar, S., Kaur, P., Fosso Wamba, S., et al. "Big data in operations and supply chain management: A systematic literature review and future research agenda", International Journal of Production Research, 59(11), pp. 3509-3534 (2021).
23. Maheshwari, S., Gautam, P., and Jaggi, C.K. "Role of big data analytics in supply chain management: current trends and future perspectives", International Journal of Production Research, 59(6), pp. 1875-1900 (2021).
24. Atabaki, M.S., Khamseh, A.A., and Mohammadi, M. "A priority-based fire y algorithm for network design of a closed-loop supply chain with price-sensitive demand", Computers and Industrial Engineering, 135, pp. 814-837 (2019).
25. Avakh Darestani, S. and Hemmati, M. "Robust optimization of a bi-objective closed-loop supply chain network for perishable goods considering queue system", Computers and Industrial Engineering, 136, pp. 277- 292 (2019).
26. Zaleta, N.C. and Socarras, A.M.A. "Tabu search-based algorithm for capacitated multicommodity network design problem", In 14th International Conference on Electronics, Communications and Computers, 2004. CONIELECOMP 2004, pp. 144-148, IEEE, (Feb, 2004).
27. Lee, Y. H. and Kwon, S. G. "The hybrid planning algorithm for the distribution center operation using tabu search and decomposed optimization", Expert Systems with Applications, 37(4), pp. 3094-3103 (2010).
28. Sharifi, M., Mousa Khani, M., and Zaretalab, A. "Comparing parallel simulated annealing, parallel vibrating damp optimization and genetic algorithm for joint redundancy-availability problems in a seriesparallel system with multi-state components", Journal of Optimization in Industrial Engineering, 7(14), pp. 13-26 (2014).
29. Hajipour, Y. and Taghipour, S. "Non-periodic inspection optimization of multi-component and k-out-of-m systems", Reliability Engineering and System Safety, 156, pp. 228-243 (2016).
Scientia Iranica
Volume 30, Issue 4 - Serial Number 4
Transactions on Industrial Engineering (E)
July and August 2023
Pages 1480-1497

Files

Share

How to cite

Statistics