| Title | Green-resilient model for smartphone closed-loop supply chain network design: A novel four-valued refined neutrosophic optimization |
|---|---|
| ID_Doc | 22705 |
| Authors | Saeed, A; Jian, M; Imran, M; Freen, G; Majid, AUR |
| Title | Green-resilient model for smartphone closed-loop supply chain network design: A novel four-valued refined neutrosophic optimization |
| Year | 2024 |
| Published | |
| Abstract | This study endeavors to integrate green supply chain and resilience proactive strategies within a closed-loop supply chain network. The research employs a three-step methodology. In the first phase, a multi-objective, multi-period, multi-node, mixed integer linear programming model (MILP) is developed. The objective of this study is to maximize resilience while minimizing both greenhouse gas (GHG) emissions and costs. The environmental footprint is assessed using the life cycle assessment (LCA) tool as part of the green supply chain strategy. To optimize resilience strategies, proximate nodes in the closed-loop supply chain network implement measures involving node criticality and inventory availability. In the second step, a novel four-valued refined multi-objective neutrosophic optimization algorithm is introduced to resolve the model. In contrast to traditional optimization approaches, neutrosophic optimization integrates elements of truth, falsity, contradictions, and uncertainty into the decision-making process. In the third phase, a case study of the smartphone supply chain is presented, advocating for the adoption of circular economy practices to prevent future waste. To assess the resilience of the model, a sensitivity analysis has been conducted, examining various key parameters associated with the market, and studying their impact on the objective function. The model's performance is validated by comparing the solution methodology with other goal programming and interactive fuzzy multi-objective methods. The results highlight the better performance of four-valued refined neutrosophic optimization, achieving 3.59 and 3.38 times greater cumulative gap reduction than other methods. The optimal solution recommends green strategies for an optimal trade-off among cost, GHG emissions, and resilience. |
Similar Articles
| ID | Score | Article |
|---|---|---|
10474
|
Hasan, KW; Ali, SM; Paul, SK; Kabir, G Multi-objective closed-loop green supply chain model with disruption risk(2023) | |
| 26382
|
Karunakaran, SK; Ramasamy, N; Anand, MD; Santhi, N Factor analysis of environmental effects in circular closed-loop supply chain network design and modelling under uncertainty in the manufacturing industry(2024)Environmental Quality Management, 34, 1 | |
| 72862
|
Seydanlou, P; Jolai, F; Tavakkoli-Moghaddam, R; Fathollahi-Fard, AM A multi-objective optimization framework for a sustainable closed-loop supply chain network in the olive industry: Hybrid meta-heuristic algorithms(2022) | |
| 74681
|
Mogale, DG; De, A; Ghadge, A; Aktas, E Multi-objective modelling of sustainable closed-loop supply chain network with price-sensitive demand and consumer's incentives(2022) | |
| 73281
|
Pourjavad, E; Mayorga, RV Multi-objective Fuzzy Programming of Closed-Loop Supply Chain Considering Sustainable Measures(2019)International Journal Of Fuzzy Systems, 21, 2 | |
| 13686
|
Tsao, YC; Balo, HT; Lee, CKH Resilient and sustainable semiconductor supply chain network design under trade credit and uncertainty of supply and demand(2024) | |
| 72939
|
Madani, B; Saihi, A; Abdelfatah, A A Systematic Review of Sustainable Supply Chain Network Design: Optimization Approaches and Research Trends(2024)Sustainability, 16, 8 | |
| 17793
|
Kazancoglu, Y; Yuksel, D; Sezer, MD; Mangla, SK; Hua, LL A Green Dual-Channel Closed-Loop Supply Chain Network Design Model(2022) | |
| 8498
|
Lotfi, R; Khanbaba, A; Ali, SS; Afshar, M; Mehrjardi, MS; Omidi, S Net-zero, resilience, and agile closed-loop supply chain network design considering robustness and renewable energy(2024) | |
| 5012
|
Foroozesh, N; Karimi, B; Mousavi, SM; Mojtahedi, M A hybrid decision-making method using robust programming and interval-valued fuzzy sets for sustainable-resilient supply chain network design considering circular economy and technology levels(2023) |