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Eécoles Notre
Institut Universitaire des Sciences, Haïti
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

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Optimizing Sustainable Supply Chain Network Design using Hybrid AI and Real-Time Data Mocombe Celucien; Eécoles Notre
International Journal of Enterprise Modelling Vol. 13 No. 2 (2019): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v13i2.12

Abstract

This research focuses on optimizing sustainable supply chain network design by leveraging hybrid AI techniques and real-time data integration. The objective is to minimize costs while considering carbon emissions, transportation modes, supplier selection, and inventory allocation. The research proposes a mathematical formulation model that incorporates these variables and constraints, enabling companies to make data-driven decisions and enhance their sustainability performance. Real-time data from various sources, including suppliers, transportation providers, and inventory systems, is collected and processed using AI techniques. The model is then solved using advanced optimization algorithms to determine the optimal supply chain network design. Sensitivity analysis is conducted to assess the robustness of the model and evaluate the impact of changing parameters and constraints. A case example illustrates the practical application of the research findings, highlighting the benefits of the hybrid AI and real-time data approach in achieving cost efficiency and sustainability goals. The research contributes to the field of supply chain management by providing insights into the integration of real-time data, AI techniques, and sustainability considerations in supply chain network design. It also identifies limitations and suggests areas for future research to enhance the applicability and scalability of the proposed approach.
Co-Authors Mocombe Celucien