知识驱动的冶金企业原料供应链订单分配方法

刘一顺; 阳春华; 黄科科

doi:10.11959/j.issn.2096-6652.202238

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知识驱动的冶金企业原料供应链订单分配方法

学术论文 | 更新时间：2024-06-05

- 知识驱动的冶金企业原料供应链订单分配方法
- Knowledge-driven order allocation method for raw material supply chain in metallurgical enterprises
- 智能科学与技术学报 2022年4卷第3期页码：355-370
- 作者机构：
- 作者简介：
  
  [ "刘一顺（1995- ），男，中南大学自动化学院博士生，主要研究方向为供应链优化、工业过程监测、工业互联网等" ]
  [ "阳春华（1965- ），女，博士，中南大学自动化学院院长、教授、博士生导师，主要研究方向为复杂工业过程建模与优化控制、智能感知与自动化装置、工业大数据分析与深度学习、流程工业智能优化制造等" ]
  [ "黄科科（1990- ），男，博士，中南大学自动化学院人工智能系副主任、教授、博士生导师，主要研究方向为复杂系统与复杂网络、大数据分析与处理、智能制造与工业互联网等" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61988101);国家自然科学基金资助项目;The National Natural Science Foundation of China(61860206014);国家重点研发计划基金资助项目;The National Key Research and Development Program of China(2018YFB1701100)
- DOI：10.11959/j.issn.2096-6652.202238
  中图分类号： TP2
- 网络出版日期：2022-09，
  
  纸质出版日期：2022-09-15
- 稿件说明：
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刘一顺, 阳春华, 黄科科. 知识驱动的冶金企业原料供应链订单分配方法[J]. 智能科学与技术学报, 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.
刘一顺, 阳春华, 黄科科. 知识驱动的冶金企业原料供应链订单分配方法[J]. 智能科学与技术学报, 2022,4(3):355-370. DOI： 10.11959/j.issn.2096-6652.202238.

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

Keywords

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