知识驱动的冶金企业原料供应链订单分配方法

doi:10.11959/j.issn.2096-6652.202238

摘要/Abstract

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。

关键词: 供应链优化, 订单分配, 知识驱动, 冶金工业

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.

Key words: supply chain optimization, order allocation, knowledge-driven, metallurgical industry

中图分类号:

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.

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目标层G	准则层Z	指标层C	指标类型	指标属性
冶金企业原料供应商评价	供给数量Z₁	原料到货量C₁	定量	效益型
	供给质量Z₂	平均主品位C₂	定量	效益型
		杂质超标数量C₃	定量	成本型
		额外富含效益C₄	定量	效益型
	履约程度Z₃	合同履约率C₅	定量	效益型
	资质体系Z₄	资质健全C₆	定性	效益型
		公司规模C₇	定性	效益型
	服务水平Z₅	服务态度C₈	定性	效益型
		配合程度C₉	定性	效益型

三角模糊数中值m_ij	标度含义
1	与后一指标相比，前一指标同等重要
3	与后一指标相比，前一指标稍微重要
5	与后一指标相比，前一指标明显重要
7	与后一指标相比，前一指标强烈重要
9	与后一指标相比，前一指标极度重要
2，4，6，8	相邻两标度之间折中时的标度
上列标度的倒数	指标i对指标j的标度为m_ij，反之为1/m_ij

供应商	C₁	C₂	C₃	C₄	C₅	C₆	C₇	C₈	C₉
S1	89 684.19	0.48	1	545	1.01	5	5	5	5
S2	28 059.36	0.47	1	539	0.98	5	5	4	4
S3	23 627.69	0.48	1	534	1.03	5	5	5	5
S4	32 992.49	0.48	2	528	1.04	5	5	5	3
S5	32 020.18	0.47	1	522	1	5	5	4	4
S6	26 295.91	0.49	1	522	1.03	5	5	4	4
S7	26 243.39	0.48	1	522	1.04	5	5	4	4
S8	7 260.03	0.56	1	522	1.02	5	5	4	3
…	…	…	…	…	…	…	…	…	…
S24	3 194.68	0.49	1	458	0.63	5	5	4	3
S25	679.68	0.54	0	458	0.68	5	5	3	4
S26	2 953.01	0.49	0	458	0.7	5	5	3	4
S27	751.93	0.51	1	453	0.99	5	5	4	3
S28	1 627.12	0.47	1	453	1	5	5	5	4
S29	3 974.03	0.44	2	447	1.06	5	5	4	3
S30	1 758.27	0.49	1	441	0.88	5	5	3	3
S31	887.1	0.5	3	435	0.94	4	4	4	2
…	…	…	…	…	…	…	…	…	…

组合权系数	兼容度	兼容度排序	差异度	差异度排序	综合排序
0	0.828217	6	10	6	6
0.1	0.829293	4	9.8	5	4
0.2	0.831006	1	6.8	4	1
0.3	0.830786	2	5	2	2
0.4	0.829337	3	4.8	1	3
0.5	0.829007	5	6.4	3	5

方法	C₁	C₂	C₃	C₄	C₅	C₆	C₇	C₈	C₉
CRITIC法^[33]	0.1644	0.1171	0.1107	0.0701	0.0924	0.1291	0.1291	0.1148	0.0723
变异系数法^[34]	0.6057	0.0230	0.0614	0.0357	0.0814	0.0264	0.0264	0.0602	0.0798
熵权法	0.4170	0.0508	0.0310	0.0749	0.0476	0.0752	0.0752	0.1584	0.0699
模糊层次分析法	0.3289	0.2041	0.0605	0.1922	0.0918	0.0407	0.0376	0.0261	0.0181
所提方法	0.3465	0.1734	0.0546	0.1687	0.0830	0.0476	0.0451	0.0526	0.0285