Optimizing A Fuzzy Multi-Objective Closed-loop Supply Chain Model Considering Financial Resources using meta-heuristic

Document Type : Article

Authors

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

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

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

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

10.24200/sci.2021.57308.5171

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

1. Reference

[1] Ramezani, M., Bashiri, M., & 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., & 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., & Fathollah, M. “A holistic view of supply chain management”. Iran University of Science & Technology Publications. (2006).

[5] Peng, Y., Ablanedo-Rosas, J. H., & Fu, P. “A multiperiod supply chain network design considering carbon emissions”. Mathematical Problems in Engineering, (2016).

[6] Hassanzadeh, A.S., & 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., & Sharifi, M. “An inexact-fuzzy-stochastic optimization model for a closed loop supply chain network design problem”. Journal of Optimization on Industrial Engineering, 12, pp. 7-16. (2013).

[8] Pishvaee, M. S., Razmi, J., & 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., & Casalinho, G. D. “Supply chain network optimization using a Tabu Search based heuristic” Gestão & Produção, 23(1), pp. 3-17. (2016).

[10] Qin, Z., & 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., & Yang, K. “Multi-objective biogeography-based optimization for supply chain network design under uncertainty”. Computers & Industrial Engineering, 85, pp. 145-156. (2015).

[12] Avakh Darestani, S., & Pourasadollah, F. “A Multi-Objective 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 & Industrial Engineering, pp. 135, 987-1003. (2019).

[14] Rahimi Sheikh, H., Sharifi, M., & Shahriari, M. R. “Designing a Resilient Supply Chain Model (Case Study: the Welfare Organization of Iran)”. Journal of Industrial Management Perspective, 7(3, Autumn 2017), 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, pp. 114. 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”. Springer International Publishing. (2021).

[20] Srivastava, M., & Rogers, H. “Managing global supply chain risks: effects of the industry sector”. International Journal of Logistics Research and Applications, pp. 1-24. (2021).

[21] Jaggi, C. K., Hag, A., & Maheshwari, S. “Multi-objective 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”. Springer International Publishing. (2021).

[23] Maheshwari, S. Gautam, P., & Jaggi, C. K. “Role of Big Data Analytics in supply chain management: current trends and future perspectives”. International Journal of Production Research. (2020).

[24] Atabaki, M. S., Khamseh, A. A., & Mohammadi, M. “A priority-based firefly algorithm for network design of a closed-loop supply chain with price-sensitive demand”. Computers & Industrial Engineering, 135, pp. 814- 837. (2019).

[25] Avakh Darestani, S., & Hemmati, M. “Robust optimization of a bi-objective closed-loop supply chain network for perishable goods considering queue system”. Computers & Industrial Engineering, pp. 136, 277-292. (2019).

[26] Zaleta, N. C., & Socarrás, 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. (2004, February).

[27] Lee, Y. H., & 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., & Zaretalab, A. “Comparing Parallel Simulated Annealing, Parallel Vibrating Damp Optimization and Genetic Algorithm for Joint Redundancy-Availability Problems in a Series-Parallel System with Multi-State Components”. Journal of Optimization in Industrial Engineering, 7(14), pp. 13-26. (2014).

[29] Hajipour, Y., & Taghipour, S. (2016). Non-periodic inspection optimization of multi-component and k-out-of-m systems. Reliability Engineering & System Safety, 156, 228-243.