Sustainable supply chain network design: Integrating risk management, resilient multimodal transportation, and production strategy

Abstract

This study presents a novel advancement in sustainable supply chain network design (SSCND) by incorporating risk management, resilience, and production strategies within a multi-modal transportation framework. Focusing on the distribution, production, and inventory (DPI) triad, the literature on the SSCND emphasizes the need for strategic alignment to create a resilient supply chain capable of mitigating risks in multimodal transport. To achieve this, we develop a novel multi-objective mixed-integer programming (MOMIP) model customized to the SSCND aimed at maximizing profit, minimizing transportation time, and reducing environmental impacts. The model is solved using a specialized goal programming approach, ensuring that no objective is compromised at the expense of others. A hybrid solution methodology, combining a local search algorithm with machine learning predictive models, is introduced to navigate the complexity of the MOMIP model efficiently. The model’s validity is confirmed through real-world data from the Iranian chemicals industry, and the proposed algorithm’s performance is tested. On average, the algorithm achieves an optimality gap of <3 %, with a gap of 2.67 % for profit maximization, 1.63 % for transportation time reduction, and 0.71 % for minimizing environmental impact, demonstrating its efficiency and reliability. Sensitivity analyses further highlight the significant impact of risks including environmental, policy, and operational on transportation and financial outcomes, showing up to a 12 % decrease in profits due to environmental risks alone. These findings underscore the robustness of the model and its applicability in complex, real-world industrial scenarios, making valuable contributions to the literature on sustainable supply chain management, risk mitigation, and multimodal transportation optimization.