This study aims to select the optimal suppliers of production raw materials. Due to the specificity, diversity, and real-time nature of raw materials, it is crucial for enterprises to make comprehensive considerations when choosing suppliers. Therefore, establishing a comprehensive supplier evaluation system and formulating the best ordering plan is extremely important to ensure the production of enterprises. Based on the historical ordering data of a certain company, we extracted multiple supply digital features and comprehensively evaluated the suppliers from three aspects: supplier strength, supply-demand fit, and corporate ordering preferences. Specifically, we used five evaluation indicators, including average weekly supply volume, supply-demand curve similarity, supply compliance rate, average annual growth rate of order volume, and preference variation coefficient. Specifically, the entropy method was used to determine the weights of each indicator, and the TOPSIS method was used to score the suppliers. Next, we used 0-1 integer programming to minimize the total number of suppliers as the goal, and calculated the constraints for each week in turn to solve for the optimal 20 raw material suppliers.

Xuan Liu. The Optimal Supplier Selection Strategy for Enterprises Based on Entropy Weight TOPSIS. Academic Journal of Business & Management (2024) Vol. 6, Issue 11: 237-243. https://doi.org/10.25236/AJBM.2024.061135.

[1] Yao, Luorui, and Cong'an Ma. "Research on Equipment Supplier Selection for Coal Enterprises Based on Intuitionistic Fuzzy TOPSIS Method." Mathematics in Practice and Theory 50, no. 04 (2020): 288-294.

[2] Nafei A , Huang C Y , Chen Y C H .Neutrosophic Autocratic Multi-Attribute Decision-Making Strategies for Building Material Supplier Selection[J].Buildings (2075-5309), 2023, 13(6). DOI:10.3390/buildings13061373.

[3] Xu X J , Liu Y , Liu S T .Supplier Selection Method for Complex Product Based on Grey Group Clustering and Improved Criteria Importance[J].International Journal of Computational Intelligence Systems, 2023, 16(1).DOI:10.1007/s44196-023-00368-6.

[4] Labbé Martine, Landete Mercedes, and Monge Juan F. "Bilevel integer linear models for ranking items and sets. "Operations Research Perspectives 10. (2023).