Sustainable closed-loop supply chain design for the car battery industry with taking into consideration the correlated criteria for supplier selection and uncertainty conditions

Authors

  • Hamid Reza Jafari
  • Amirhosein Kazemi Abharian

DOI:

https://doi.org/10.20397/2177-6652/2020.v20i0.1749

Keywords:

closed- loop supply chain, Sustainable supply chain, Lexicography method, Principal Component Analysis (PCA), battery industry

Abstract

Based on the concept of Sustainability and sustainable development, focusing only on economic and profitable issues is not sufficient, and companies need to pay attention to environmental and social impacts of car industry. In this regard, in this research, a mathematical model for designing a sustainable closed-loop supply chain multi-Surface and multi-product for car battery industry under uncertainty conditions, considering the correlation between supplier selection criteria presented. The study of supply chain network is including suppliers, manufacturers, distributors, customers, recycling centers and destruction centers. The proposed model is able to locate the levels of producers, distributors, Recycling and disposal centers As well as the flow of materials between the different levels of the supply chain to minimize the total costs, minimize overall environmental impact, And maximize social utility and maximize utility of supplier selection In view of the set criteria correlated. Then Lexicography method was introduced to solve the mode and finally, in order to assess the proficiency and validity of the proposed model, a problem as a numerically issue for different priorities in goals is solved, and the results have been analyzed. The solution outcomes represent that the proposed model and method of solution have the required efficiency and validity

References

Amin SH, Zhang G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling; 37:6 4165–4176.

• Atefeh Baghalian a Shabnam Rezapour c, Reza Zanjirani Farahani, (2013), “ Robust supply chain network design with service level against disruptions and demand uncertainties: A reallife

• Bashiri, M. and T. H. Hejazi (2012). A Mathematical Model Based on Principal Component Analysis for Optimization of Correlated Multiresponse Surfaces. Journal of Quality 19(3): 223-239.

• Fleischmann M., Beullens P., Bloemhof-Ruwaard J.M.,Van Wassenhove, L.N., (2001), The impact of product recovery on logistics network design, Production and Operations Management, 10(2), 156–173.

• Garg, K., Sanjam, Jain A., Jha P.C., (2014), designing a closed-Loop Logistic Network in Supply Chain by Reducing its Unfriendly Consequences on Environment. Proceedings of the Second International Conference on Soft Computing for Problem Solving. Volume 236. 1483-1498.

• Govindan K., Soleimani H., Kannan D., (2014), Reverse logistics and closed-loop supply chain: a comprehensive review to explore the future, European Journal of Operational Research, 240(3), 603-626

• . Jafari, H. R., Seifbarghy, M., & Omidvari, M. (2017). Sustainable supply chain design with water

environmental impacts and justice-oriented employment considerations: A case study in textileindustry. Scientia Iranica, 24(4), 2119–2137.

• Jafari, R. J., and M. Seifbarghy. 2016. “Optimizing Bi-Objective Multi-Echelon Multi-Product Supply Chain Network Design Using New Pareto-Based Approaches.” Industrial Engineering & Management Systems 15 (4): 374–384.

• Klibi W, Martel A, Guitouni A. (2010). The design of robust value-creating supply chain networks: a critical review. European Journal of Operational Research; 283:93-203.

• Kannan D, Diabat A, Alrefaei M, Govindan K, Yong G. (2012). A carbon footprint based reverse logistics network design model. Resources, conservation and recycling; 67:7, 5-9.

• Lam, K.-C., R. Tao, and Lam, M.C.-K. (2010). A material supplier selection model for property developers using Fuzzy Principal Component Analysis. Automation in Construction 19(5): 608-618.

• Lieckens, K., Vandaele, N., (2007), Reverse logistic network design with stochastic lead times. Computer & Operations Resaerch, 34(2), 395-416.

• Melo MT, Nickel S, Saldanha-Da-Gama F. (2009). Facility location and supply chain management–A review. European Journal of Operational Research; 196:40 1-12.

• ÖzkTr V, BaUlTgil H. (2012). Multi-objective optimization of closed-loop supply chains in uncertain environment. Journal of Cleaner Production.

• Paksoy T, Özceylan E, Weber GW. (2010). A multi objective model for optimization of a green supply chain network. AIP Conference Proceedings; 311.

• Peidro. D., Mula. J., Jiménez. M., Botella. M.D.M., (2010), A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment. European Journal of Operational Research, Vol 205(1),65-80

• Pishvaee MS, Rabbani M, Torabi SA. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling; 35:637-49.

• Ramezani M, Bashiri M, Tavakkoli-Moghaddam R. (2013). A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Applied Mathematical Modelling; 37: 328-344.

• Ramezani, M., Bashiri, M., Tavakkoli- Moghaddam, R., (2013), A robust design for a closed-loop supply chain under an uncertain environment. International Journal Advanced Manufacturing Technology,66: 825-843, DOI10.1007/s00170-012-4369-8.

• Sarbu, C. and Pop, H. (2005). Principal component analysis versus fuzzy principal component analysis: a case study: the quality of Danube water (1985–1996). Talanta 65(5): 1215-1220.

• Sharma, S. (1995). Applied multivariate techniques, John Wiley & Sons, Inc.

• Simchi-Levi, D., Kaminsky, P., (2004).” Managing the Supply Chain: The Definitive Guide for the Business Professional”, Boston, Irwin McGraw-Hill.

• Subramanian, P., Ramkumar, N., Narendran, T.T., Ganesh, K., (2013), Priority based simulated annealing for closed loop supply chain network design problem, Applied Soft Computing 13, 1121 - 1135.

• Wang, F., Lai, X., Shi, N., (2011),” A multi-objective optimization for green supply chain network design”, Decision Support Systems 51, 262–269. case”, European Journal of Operational Research 227 199–215.

• Xu, J., Q. Liu, and R. Wang, (2008). A class of multi-objective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor. Information Sciences, 178(8): 2022-2043.

• Yabuuch, Y. and J. Watada (1997). Fuzzy principal component analysis and its application. Biomedical fuzzy and human sciences: the official journal of the Biomedical Fuzzy Systems Association 3(1): 83-92.

Downloads

Published

2020-04-06

How to Cite

Jafari, H. R., & Kazemi Abharian, A. (2020). Sustainable closed-loop supply chain design for the car battery industry with taking into consideration the correlated criteria for supplier selection and uncertainty conditions. Journal of Management & Technology, 20, 3–29. https://doi.org/10.20397/2177-6652/2020.v20i0.1749

Issue

Vol. 20 (2020): Edição Especial/Abril

Section

ARTIGO

License

Copyright (c) 2020 Journal of Management & Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Authors who publish with this journal agree to the following terms: • 1. The author(s) authorize the publication of the article in the journal. • 2. The author(s) ensure that the contribution is original and unpublished and is not being evaluated in other journal(s). • 3. The journal is not responsible for the opinions, ideas and concepts expressed in the texts because they are the sole responsibility of the author(s). • 4. The publishers reserve the right to make adjustments and textual adaptation to the norms of APA. • 5. Authors retain copyright and grant the journal right of first publication, with the work [SPECIFY PERIOD OF TIME] after publication simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. • 6. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. • 7. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access) at http://opcit.eprints.org/oacitation-biblio.html