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Knowledge-driven order allocation method for raw material supply chain in metallurgical enterprises
- Chinese Journal of Intelligent Science and Technology Vol. 4, Issue 3, Pages: 355-370(2022)
- 作者机构：
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.2096-6652.202238
CLC： TP2-
Published Online：2022-09，
Published：15 September 2022
- 稿件说明：
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Yishun LIU, Chunhua YANG, Keke HUANG. Knowledge-driven order allocation method for raw material supply chain in metallurgical enterprises[J]. Chinese journal of intelligent science and technology, 2022, 4(3): 355-370.
Yishun LIU, Chunhua YANG, Keke HUANG. Knowledge-driven order allocation method for raw material supply chain in metallurgical enterprises[J]. Chinese journal of intelligent science and technology, 2022, 4(3): 355-370. DOI： 10.11959/j.issn.2096-6652.202238.
摘要
原料供应链是安全稳定生产的前提和保障，订单分配作为供应链的前置环节，是企业关注的重点。冶金行业原料成分复杂、供应商量大质异、信息耦合关联，当前专家决策模式面对如此复杂的订单分配问题时劳动强度大且难以适应海量信息，导致决策效率低、采购成本高、原料质量难以保障。针对该问题，提出了知识驱动的冶金企业原料供应链订单分配方法。首先，在确定多层次供应商评价体系的基础上，通过熵权法和模糊层次分析法充分利用供货数据知识和专家经验知识，结合兼容度和差异度合理分配评价指标的重要度。然后，基于TOPSIS构建多属性决策评价模型，自动获得各供应商综合绩效与排名，实现对供应商的高效评价管理。最后，综合考虑供应商供货特性知识、配矿机理知识、经营状况知识等，建立多目标订货量分配模型，自动获得复杂资源约束下各供应商的最优订货量。以国内某锌冶炼企业为例，采用原料供应链相关数据对所提方法的有效性及适用性进行验证。结果表明，所提方法可以自动地完成供应商评价、订货量分配等知识型工作，极大地降低了人工劳动强度，提高了决策效率，并有效降低了采购成本，提升了原料质量。
Abstract
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.
关键词
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references
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