The raw material supply chain is the premise of safe and stable production with an important position.As the front link of the supply chain, order allocation is the focus of enterprises.Due to the complex composition of raw materials, large qualitative differences in suppliers, and information coupling in the metallurgical industry, the current expert decision-making model is labor-intensive and difficult to deal with such complex order allocation problems, resulting in low decision-making efficiency, high procurement costs, and difficult to guarantee the quality of raw materials.Aiming at this problem, a knowledge-driven order allocation method for the raw material supply chain in metallurgical enterprises was proposed.First, on the basis of a multi-level supplier evaluation system, the entropy weight method and the fuzzy analytic hierarchy process were adopted to make full use of data knowledge and experience knowledge, and compatibility degree and different degree were introduced to reasonably allocate the importance of each evaluation index.Then, a multi-attribute decision-making evaluation model was built based on the technique for order preference by similarity to an ideal solution (TOPSIS) to automatically obtain the comprehensive performance and ranking of suppliers, so as to realize the efficient evaluation and management of suppliers.Finally, a multi-objective order quantity allocation model was established by comprehensively considering the supplier characteristics knowledge, ore blending mechanism knowledge, business status knowledge, etc., and the optimal order quantity of the each supplier under complex resource constraints was automatically obtained.Taking a domestic zinc smelting enterprise as an example, the validity and applicability of the proposed method are verified by the relevant data of the raw material supply chain.The results show that the proposed method can automatically complete knowledge-based work such as supplier evaluation and order quantity allocation, which will greatly liberate manual labor, improve decision-making efficiency, effectively reduce procurement cost and improve the quality of raw materials.
