Research on Supplier Selection and Order Collaborative Optimization Strategies from the Perspective of Economics Assisted by Machine Learning

From an economic perspective, in the current business operating environment, reducing costs and increasing efficiency have become the core demands for the sustainable development of enterprises. Especially in the face of the increasing diversity of suppliers and continuous growth of supply risks, supply chain management is facing unprecedented complex challenges. Based on the historical supply and order data of 402 suppliers, this paper constructs indexes such as average supply intensity, completion rate, order rate and risk for quantitative analysis, and uses the Entropy Weight Method-TOPSIS model to evaluate 50 high-quality suppliers. Furthermore, by combining the multi-objective optimization model and the genetic algorithm, under the condition of meeting the maximum production capacity demand with the minimum number of suppliers, the combination of the minimum number of suppliers that can meet the production needs of the enterprise is found. The results show that these suppliers have significant advantages in enhancing supply stability, reducing risks and optimizing costs, providing theoretical basis and practical reference for enterprises in supplier selection and evaluation in supply chain management as well as promoting their strategic decisions in the complex market environment.