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A Multi-Objective Optimization Model for Blockchain-Enabled Smart Supply Chains under Uncertainty: Enhancing Transparency, Cost Efficiency, and Sustainability
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Abstract: (10 Views) |
This research proposes a robust multi-objective optimization model for blockchain-enabled smart supply chains under uncertainty. The model integrates forward and reverse logistics while incorporating blockchain transaction efficiency to enhance transparency, traceability, and trust among stakeholders. The objectives include minimizing total costs, reducing carbon emissions, maximizing service levels, and optimizing blockchain-related operations. To address uncertainties in demand and transportation costs, the model employs fuzzy robust optimization techniques, ensuring reliable decision-making. To solve the proposed model, several metaheuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the newly developed Greedy Man Optimization Algorithm (GMOA) are utilized. Comparative analysis demonstrates the superiority of GMOA in achieving high-quality solutions with lower computational time. The results highlight the model’s practical applicability in designing sustainable, transparent, and efficient supply chains. Sensitivity analyses provide managerial insights, emphasizing the impact of key parameters on total costs and operational performance.
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| Keywords: Blockchain-enabled supply chain, robust optimization, Greedy Man Optimization Algorithm (GMOA), metaheuristic algorithms |
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Type of Study: Special Issue-IPB2024 |
Subject:
Supply Chain Management and Logistics
Received: 2025/01/17 | Accepted: 2025/09/1 | Published: 2025/09/1
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