Optimizing Supply Chain Network Design through Hybrid Artificial Intelligence Approaches: A Comparative Study
DOI:
https://doi.org/10.35335/emod.v13i2.10Keywords:
Supply chain network design, Hybrid artificial intelligence, Optimization, Comparative study, Performance evaluationAbstract
Supply chain network design plays a critical role in achieving operational efficiency and cost optimization. This research focuses on optimizing supply chain network design through the use of hybrid artificial intelligence (AI) approaches and presents a comparative study of different methods. The objective is to evaluate the effectiveness of combining machine learning, optimization algorithms, and expert systems in enhancing the design of supply chain networks. The research begins by formulating a mathematical model that captures the key decision variables, objectives, and constraints associated with supply chain network design. The model aims to minimize overall costs while considering factors such as facility selection, transportation routing, and product flows. To evaluate the performance of the hybrid AI approaches, various methods are compared, including genetic algorithms, particle swarm optimization, and reinforcement learning. Through extensive testing and analysis, the comparative study assesses the strengths and weaknesses of each approach in terms of solution quality, computational efficiency, and robustness. The study also considers the scalability of the methods to handle large-scale supply chain networks. The findings of the research demonstrate the benefits of integrating hybrid AI approaches in supply chain network design optimization. The hybrid AI methods outperform traditional optimization techniques, providing more accurate and efficient solutions. The comparative analysis highlights the specific scenarios in which each method excels, aiding decision-makers in selecting the most appropriate approach for their supply chain network design challenges. The research identifies opportunities for further enhancements and advancements in the field. Future research directions may include incorporating real-time data, considering uncertainty and risk factors, and extending the analysis to industry-specific applications. This research contributes to the optimization of supply chain network design by leveraging the power of hybrid AI approaches. The findings provide valuable insights for supply chain managers and decision-makers, enabling them to make informed choices that enhance operational efficiency, reduce costs, and improve overall supply chain performance.
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