International Journal of Nonlinear Analysis and Applications

Designing a sustainable-resilient supply chain network with an emphasis on financing and investment decisions using the NSGA-II algorithm

Document Type : Research Paper

Authors

1 Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial Management, Faculty of Management, South Tehran Branch, Islamic Azad University, Tehran, Iran

3 Visitor of Science and Research Branch, Islamic Azad University, Tehran, Iran

4 Department of Industrial Management, Faculty of Management, University of Allameh Tabatabai, Tehran, Iran

5 Department of Industrial Management, Faculty of Management, Accounting and Humanities, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Abstract

Nowadays, product recovery and waste recycling are receiving more attention in order to reduce environmental pollution and production costs in the form of a closed-loop and sustainable supply chain. Also, designing a supply chain by considering resilience approaches can protect buyers against disruptions such as natural, human or technological disasters. On the other hand, efficient and effective financial supply chain management (FSCM) way is known as one of the main structures in line with the continuity and stability of the chain's performance, and budget restrictions are of great importance considering the issue of scarcity of resources in the economy. This study has presented a multi-objective mixed integer linear programming (MOMILP) model of a single-period, multi-product and multi-level closed-loop supply chain, taking into account the dimensions of sustainability and resilience, emphasizing the balance between the initial budget and the cost of establishing facilities under uncertainty. subsequently, to eliminate the uncertainty of the demand parameters and costs, its robust counterpart was presented based on Pishvaee’s robust possibilistic programming (RPP) model. The augmented Epsilon Constraint method (AEC) and Non-dominated Sorting Genetic Algorithm (NSGA-II) were used to solve and evaluate it.

Keywords

[1] R. Aldrighetti, D. Battini, D. Ivanov, and I. Zennaro, Costs of resilience and disruptions in supply chain network design model: A review and future research directions, Int. J. Product. Econ. 235 (2021), 317–329.
[2] S. Elluru, H. Gupta, H Kaur and S. Singh, Proactive and reactive models for disaster resilient supply chain, Annal. Oper. Res. 283 (2019), 199–224.
[3] B. Fahimnia, J. Sarkis, and S. Talluri, Design and management of sustainable and resilient supply chains, IEEE Trans. Engin. Manag. 66 (2019), no. 1, 2–7.
[4] S. Feizollahi, H. Soltanpanah, and A. Rahimzadeh, Development of closed-loop supply chain mathematical model (cost-benefit- environmental effects) under uncertainty conditions by approach of genetic algorithm, Adv. Math. Finance Appl. 6 (2021), no. 2, 245–262.
[5] J. Fiksel, Sustainability and resilience: toward a systems approach, Sustainability: Sci. Practice Policy 2 (2006), no. 2, 14–21.
[6] A. Haddadsisakht and S. Ryan, Closed-loop supply chain network design with multiple transportation modes under stochastic demand and uncertain carbon tax, Int. J. Product. Econ. 195 (2018), 118–131.
[7] N. Haghjoo, R. Tavakkoli-Moghaddam, H. Shahmoradi-Moghadam, and Y. Rahimi, Reliable blood supply chain network design with facility disruption: A real-world application, Eng. Appl. Artif. Intell. 90 (2020),  103493.
[8] S. Hosseini-Motlagh, M.R. Ghatreh Samani, and V. Shahbazbegian, Innovative strategy to design a mixed resilientsustainable electricity supply chain network under uncertainty, Appl. Energy 280 (2020), 244–259.
[9] J. Huang, W. Yang, and Y. Tu, Financing mode decision in a supply chain with financial constraint, Int. J. Product. Econ. 220 (2020), 107441.
[10] A. Jabbarzadeh, B. Fahimnia, H. Sheu, and H. Shahmoradi Moghadam, Designing a supply chain resilient to major disruptions and supply/demand interruptions, Transport. Res. Part B: Methodol. 94 (2016), 121–149.
[11] H. Kaur and S. Singh, Sustainable procurement and logistics for disaster resilient supply chain, Annal. Oper. Res. 283 (2016), 309–354.
[12] H. Kwon and J. Lee, Exploring the differential impact of environmental sustainability, operational efficiency, and corporate reputation on market valuation in high-tech-oriented firms, Int. J. Product. Econ. 211 (2019), 1–14.
[13] B. Liu and K. Iwamura, Chance constrained programming with fuzzy parameters, Fuzzy Sets Syst. 94 (1998), 227–237.
[14] P. Longinidis and M. Georgiadis, Managing the trade-offs between financial performance and credit solvency in the optimal design of supply chain networks under economic uncertainty, Comput. Chem. Eng. 48 (2013), 691–705.
[15] L.F. Lopez-Castro and E.L. Solano-Charris, Integrating resilience and sustainability criteria in the supply chain network design. A systematic literature review, Sustainability 13 (2021), no. 19, 10925.
[16] R. Lotfi, B. Kargar, S. Hoseini, S. Nazari, S. Safavi, and G. Weber, Resilience and sustainable supply chain network design by considering renewable energy, Int. J. Energy Res. 45 (2021), no. 12, 17749–17766.
[17] A. Malik and B. Kim, A multi constrained supply chain model with optimal production rate in relation to quality of products under stochastic fuzzy demand, Comput. Industr. Eng. 149 (2020), p. 106814.
[18] G. Mavrotas, Effective implementation of the e-constraint method in multi-objective mathematical programming problems, Appl. Math. Comput. 213 (2009), no. 2, 455–465.
[19] R. Meharjan and H. Kato, Resilient supply chain network design: a systematic literature review, Transport Rev. 42 (2022), no. 6, 739–761.
[20] M. Moats, L. Alagha, and K. Awuah-Offei, Towards resilient and sustainable supply of critical elements from the cooper supply chain: A review, J. Cleaner Product. 307 (2021),  127207.
[21] P. Mousavi Ahranjani, S.F. Ghaderi, A. Azadeh, and R. Babazadeh, Robust design of a sustainable and resilient bioethanol supply chain under operational and disruption risks, Clean Technol. Envir. Policy 22 (2020), 119–151.
[22] S. Nayeri, A. Torabi, M. Tavakoli, and Z. Sazvar, A multi-objective fuzzy robust stochastic model for designing a sustainable-resilient-responsive supply chain network, J. Cleaner Product. 311 (2021), 147–161.
[23] M. Negri, E. Cagno, C. Colicchia, and J. Sarkis, Integrating sustainability and resilience in the supply chain: A systematic literature review and a research agenda, Bus. Strat. Envir. 30 (2021), 2858–2886.
[24] S. Nickel, F. Saldanha-da-Gama, and H. Ziegler, A multi-stage stochastic supply network design problem with financial decisions and risk management, Omega 40 (2012), no. 5, 511–524.
[25] L. Novoszel and T. Wakolbinger, Meta-analysis of supply chain disruption research, Oper. Res. Forum 3 (2022), no. 10, 314–331.
[26] M. Patidar, M. Sharma, R. Agrawal, and K.S. Sangwan, Antecedents of a resilient sustainable supply chain, Procedia CIRP 116 (2023), 558–563.
[27] M.S. Pishvaee, J. Razmi, and S.A. Torabi, Robust possibilistic programming for socially responsible supply chain network design: A new approach, Fuzzy Sets Syst. 206 (2012), 1–20.
[28] R. Rajesh, Social and environmental risk management in resilient supply chains: A periodical study by the Grey- Verhulst model, Int. J. Product. Res. 57 (2019), no. 11, 3748–3765.
[29] S. Roostaie, N. Nawari and C.J. Kibert, Sustainability and resilience: A review of definitions, relationships and their integration into a combined building assessment framework, Build. Envir. 154 (2019), 132–144.
[30] Z. Sadeghi, O. Boyer, S. Sharifzadeh, and N. Saeidi, A robust mathematical model for sustainable and resilient supply chain network design: Preparing a supply chain to deal with disruptions, Complexity 2021 (2021).
[31] H. Sahebi, S. Ranjbar, and A. Teymouri, Investigating different reverse channels in a closed-loop supply chain: a power perspective, Operat. Res. 22 (2022), 1939–1985.
[32] Z. Sazvar, K. Tafakkori, N. Oladzad, and S. Nayeri, A capacity planning approach for sustainable-resilient supply chain network design under uncertainty: A case study of vaccine supply chain, Comput. Industr. Eng. 19 (2021), 366–379.
[33] J.F. Shapiro, Challenges of strategic supply chain planning and modeling, Comput. Chem. Eng. 28 (2004), no. 6, 855–861.
[34] E. Shekarian, B. Ijadi, A. Zare, and J. Majava, Sustainable supply chain management: A comprehensive systematic review of industrial practices, Sustainability 14 (2022), no. 13, p. 7892.
[35] P. Shrivastava, Ecocentric management for a risk society, Acad. Manag. Rev. 20 (1995), no. 1, 118–137.
[36] P. Taebi, M. Modiri, S.M.A. Khatami Firouzabadi, and A. Mohtashami, Presenting a multi-objective linear programming mathematical model of a resilience and sustainable supply chain with an emphasis on environmental factors with a robust optimization approach, Int. J. Nonlinear Anal. Appl. In press, (2023). doi: 10.22075/ijnaa.2023.28697.3968
[37] B. Zahiri, J. Zhuang, and M. Mohammadi, Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study, Transport. Res. Part E 103 (2017), 109–142.
[38] M.R. Zamanian, E. Sadegh, Z. Amini Sabegh, and R. Ehtesham Rasi, A multi-objective optimization model for the resilience and sustainable supply chain: A case study, Int. J. Supply Oper. Manag. 7 (2020), no. 1, 51–75.
[39] Y. Zare Mehrjerdi and M. Shafiee, A resilient and sustainable closed-loop supply chain using multiple sourcing and information sharing strategies, J. Cleaner Product. 289 (2021), 117–128.
[40] A. Zavala-Alcivar, M. Verdecho, and J. Alfaro-Saiz, A conceptual framework to manage resilience and increase sustainability in the supply chain, Sustainability 12 (2020), no. 16, 6300.
International Journal of Nonlinear Analysis and Applications
Volume 15, Issue 9
September 2024
Pages 93-112
  • Receive Date: 22 February 2023
  • Accept Date: 17 July 2023