Chinese Journal of Intelligent Science and Technology ›› 2022, Vol. 4 ›› Issue (3): 355-370.doi: 10.11959/j.issn.2096-6652.202238
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Yishun LIU, Chunhua YANG, Keke HUANG
Revised:
2022-07-26
Online:
2022-09-15
Published:
2022-09-01
Supported by:
CLC Number:
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.
"
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 |
… | … | … | … | … | … | … | … | … | … |
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方法 | C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 |
CRITIC法[ | 0.1644 | 0.1171 | 0.1107 | 0.0701 | 0.0924 | 0.1291 | 0.1291 | 0.1148 | 0.0723 |
变异系数法[ | 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 |
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多属性决策评价 | 人工评价 | |||||
供应商 | 绩效值 | 排名 | 供应商 | 评分 | 排名 | |
S1 | 0.9669 | 1 | S1 | 92 | 1 | |
S2 | 0.3187 | 4 | S2 | 91 | 2 | |
S3 | 0.2716 | 7 | S3 | 90 | 3 | |
S4 | 0.3697 | 2 | S4 | 89 | 4 | |
S5 | 0.3615 | 3 | S5 | 88 | 5 | |
S6 | 0.2997 | 5 | S6 | 88 | 6 | |
S7 | 0.2991 | 6 | S7 | 88 | 7 | |
S8 | 0.1102 | 18 | S8 | 88 | 8 | |
S9 | 0.1475 | 10 | S9 | 87 | 9 | |
S10 | 0.1029 | 19 | S10 | 86 | 10 | |
… | … | … | … | … | … | |
S36 | 0.0654 | 38 | S36 | 71 | 36 | |
S37 | 0.0836 | 27 | S37 | 71 | 37 | |
S38 | 0.0654 | 39 | S38 | 70 | 38 | |
S39 | 0.0823 | 29 | S39 | 70 | 39 | |
S40 | 0.0843 | 26 | S40 | 70 | 40 | |
S41 | 0.0649 | 41 | S41 | 70 | 41 | |
S42 | 0.0652 | 40 | S42 | 69 | 42 | |
S43 | 0.037 | 45 | S43 | 69 | 43 | |
S44 | 0.0632 | 42 | S44 | 66 | 44 | |
S45 | 0.0810 | 30 | S45 | 65 | 45 |
"
供应商 | 绩效值 | 供给能力/吨 主品位 | 单价/元 | 富含价值/元 | SiO2含量 | Pb含量 | Fe含量 | Co含量 |
S1 | 0.9669 | 1 8750.56 | 11 441 | 474 | 0.88% | 2.75% | 6.87% | 0.00263% |
S1 | 0.9669 | 1 8630.46 | 9 776 | 430 | 0.95% | 1.84% | 11.74% | 0.00638% |
S1 | 0.9669 | 6100.42 | 8 481 | 732 | 1.33% | 2.17% | 6.89% | 0.00758% |
S2 | 0.3187 | 7500.48 | 10 751 | 617 | 1.70% | 1.28% | 10.06% | 0.00555% |
S2 | 0.3187 | 1 6550.52 | 10 740 | 848 | 2.02% | 1.45% | 14.07% | 0.00339% |
S3 | 0.2716 | 1 9600.5 | 10 313 | 393 | 1.64% | 1.36% | 10.07% | 0.02474% |
S3 | 0.2716 | 2 5850.47 | 9 483 | 678 | 2.17% | 1.69% | 7.76% | 0.01507% |
… | … | …… | … | … | … | … | … | … |
S40 | 0.0843 | 6900.47 | 10 654 | 675 | 2.00% | 1.34% | 7.39% | 0.01285% |
S41 | 0.0649 | 6950.39 | 8 477 | 805 | 2.75% | 2.08% | 12.38% | 0.00326% |
S42 | 0.0652 | 2000.46 | 9 410 | 378 | 1.47% | 1.37% | 8.40% | 0.00094% |
S43 | 0.037 | 6320.38 | 7 800 | 561 | 1.05% | 1.31% | 3.24% | 0.00117% |
S44 | 0.0632 | 4200.41 | 8 892 | 741 | 1.49% | 5.03% | 15.26% | 0.01830% |
S45 | 0.0810 | 8000.46 | 10 148 | 614 | 1.49% | 0.94% | 4.21% | 0.00112% |
"
订单号 | 供应商 | 人工决策订货 | 经济成本订货 | 所提模型 | 订单号 | 供应商 | 人工决策订货 | 经济成本订货 | 所提模型 |
1 | S1 | 1 875 | 0 | 1 875 | 29 | S19 | 758 | 0 | 0 |
2 | S1 | 1 863 | 1 863 | 1 863 | 30 | S20 | 1 350 | 0 | 1 546 |
3 | S1 | 0 | 610 | 610 | 31 | S20 | 1 000 | 1 000 | 1 000 |
4 | S2 | 250 | 0 | 750 | 32 | S21 | 1 250 | 1 250 | 0 |
5 | S2 | 1 655 | 1 655 | 1 655 | 33 | S22 | 1 506 | 1 965 | 1 965 |
6 | S3 | 1 960 | 1 960 | 0 | 34 | S23 | 780 | 0 | 0 |
7 | S3 | 1 450 | 2 585 | 2 585 | 35 | S24 | 595 | 184.4 | 595 |
8 | S4 | 444 | 752 | 752 | 36 | S25 | 695 | 695 | 695 |
9 | S4 | 0 | 0 | 0 | 37 | S26 | 2 230 | 2 230 | 2 230 |
10 | S5 | 595 | 0 | 595 | 38 | S27 | 0 | 1 815 | 0 |
11 | S5 | 1 637 | 1 637 | 1 637 | 39 | S28 | 765 | 0 | 765 |
12 | S6 | 2 850 | 2 850 | 2 850 | 40 | S29 | 670 | 670 | 670 |
13 | S6 | 150 | 0 | 180 | 41 | S30 | 660 | 1 370 | 1 370 |
14 | S7 | 1 195 | 0 | 1 150 | 42 | S31 | 725 | 775 | 775 |
15 | S8 | 200 | 0 | 490 | 43 | S32 | 855 | 1 200 | 0 |
16 | S8 | 1 750 | 1 750 | 1 750 | 44 | S33 | 950 | 1 220 | 1 220 |
17 | S9 | 1 600 | 1 800 | 1 800 | 45 | S34 | 310 | 810 | 810 |
18 | S10 | 548 | 0 | 567 | 46 | S35 | 1 000 | 1 000 | 1 000 |
19 | S11 | 1 113 | 2 120 | 2 120 | 47 | S36 | 0 | 920 | 0 |
20 | S11 | 960 | 0 | 960 | 48 | S37 | 340 | 850 | 0 |
21 | S12 | 1 870 | 0 | 0 | 49 | S38 | 635 | 635 | 0 |
22 | S13 | 1 650 | 1 650 | 1 650 | 50 | S39 | 590 | 590 | 590 |
23 | S14 | 2 630 | 2 630 | 2 630 | 51 | S40 | 690 | 339 | 0 |
24 | S15 | 950 | 0 | 1 220 | 52 | S41 | 0 | 695 | 695 |
25 | S16 | 865 | 1 955 | 1 955 | 53 | S42 | 135 | 200 | 200 |
26 | S17 | 400 | 742 | 742 | 54 | S43 | 497 | 632 | 632 |
27 | S17 | 1 696 | 1 696 | 1 696 | 55 | S44 | 215 | 420 | 0 |
28 | S18 | 0 | 1 120 | 0 | 56 | S45 | 800 | 800 | 800 |
"
方案 | 人工决策订货 | 经济成本订货 | 所提模型 |
订货总量/吨 | 521 57 | 51 640 | 51 640 |
订货总价/元 | 539 661 266 | 511 304 427 | 528 423 237.4 |
金属量/吨 | 24 932 | 23 699.98 | 24 512.63 |
主品位 | 47.80% | 45.89% | 47.47% |
金属单价/元 | 21 645.33 | 21 574.04 | 21 557.18 |
富含值/元 | 33 463 424 | 34 830 326 | 34 935 660 |
供应商效用 | 11 123.98 | 9 659.23 | 11 555.13 |
平均SiO2含量 | 1.77% | 1.81% | 1.77% |
平均Pb含量 | 1.36% | 1.43% | 1.36% |
平均Fe含量 | 8.83% | 8.84% | 8.77% |
平均Co含量 | 0.0063% | 0.0099% | 0.0040% |
[1] | 柴天佑, 丁进良 . 流程工业智能优化制造[J]. 中国工程科学, 2018,20(4): 51-58. |
CHAI T Y , DING J L . Smart and optimal manufacturing for process industry[J]. Strategic Study of CAE, 2018,20(4): 51-58. | |
[2] | LIU Y S , YANG C H , HUANG K K ,et al. Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network[J]. Knowledge-Based Systems, 2020,188:105006. |
[3] | 袁小锋, 王雅琳, 阳春华 ,等. 深度学习在流程工业过程数据建模中的应用[J]. 智能科学与技术学报, 2020,2(2): 107-115. |
YUAN X F , WANG Y L , YANG C H ,et al. The application of deep learning in data-driven modeling of process industries[J]. Chinese Journal of Intelligent Science and Technology, 2020,2(2): 107-115. | |
[4] | LIU C L , WANG K , WANG Y L ,et al. Learning deep multimanifold structure feature representation for quality prediction with an industrial application[J]. IEEE Transactions on Industrial Informatics, 2022,18(9): 5849-5858. |
[5] | TSAI W C , WANG C H . Decision making of sourcing and order allocation with price discounts[J]. Journal of Manufacturing Systems, 2010,29(1): 47-54. |
[6] | WANG L , DENG T H , SHEN Z J M ,et al. Digital twin-driven smart supply chain[J]. Frontiers of Engineering Management, 2022,9(1): 56-70. |
[7] | HAMDAN S , CHEAITOU A . Supplier selection and order allocation with green criteria:an MCDM and multi-objective optimization approach[J]. Computers & Operations Research, 2017,81: 282-304. |
[8] | 袁小锋, 桂卫华, 陈晓方 ,等. 人工智能助力有色金属工业转型升级[J]. 中国工程科学, 2018,20(4): 59-65. |
YUAN X F , GUI W H , CHEN X F ,et al. Transforming and upgrading nonferrous metal industry with artificial intelligence[J]. Strategic Study of CAE, 2018,20(4): 59-65. | |
[9] | HUANG K K , TAO Z , LIU Y S ,et al. Adaptive multimode process monitoring based on mode-matching and similarity-preserving dictionary learning[J]. IEEE Transactions on Cybernetics, 2022: 1-14. |
[10] | KUMAR D , RAHMAN Z , CHAN F T S . A fuzzy AHP and fuzzy multi-objective linear programming model for order allocation in a sustainable supply chain:a case study[J]. International Journal of Computer Integrated Manufacturing, 2017,30(6): 535-551. |
[11] | 丁进良, 杨翠娥, 陈远东 ,等. 复杂工业过程智能优化决策系统的现状与展望[J]. 自动化学报, 2018,44(11): 1931-1943. |
DING J L , YANG C E , CHEN Y D ,et al. Research progress and prospects of intelligent optimization decision making in complex industrial process[J]. Acta AutomaticaSinica, 2018,44(11): 1931-1943. | |
[12] | 牟天昊, 李少远 . 流程工业控制系统的知识图谱构建[J]. 智能科学与技术学报, 2022,4(1): 129-141. |
MOU T H , LI S Y . Knowledge graph construction for control systems in process industry[J]. Chinese Journal of Intelligent Science and Technology, 2022,4(1): 129-141. | |
[13] | 桂卫华, 陈晓方, 阳春华 ,等. 知识自动化及工业应用[J]. 中国科学:信息科学, 2016,46(8): 1016-1034. |
GUI W H , CHEN X F , YANG C H ,et al. Knowledge automation and its industrial application[J]. Scientia Sinica (Informationis), 2016,46(8): 1016-1034. | |
[14] | 桂卫华, 曾朝晖, 陈晓方 ,等. 知识驱动的流程工业智能制造[J]. 中国科学:信息科学, 2020,50(9): 1345-1360. |
GUI W H , ZENG H Z , CHEN X F ,et al. Knowledge-driven process industry smart manufacturing[J]. Scientia Sinica (Informationis), 2020,50(9): 1345-1360. | |
[15] | 柴天佑, 丁进良, 桂卫华 ,等. 大数据与制造流程知识自动化发展战略研究[M]. 北京: 科学出版社, 2018. |
CHAI T Y , DING J L , GUI W H ,et al. Research on the development strategy of knowledge automation of big data and manufacturing process[M]. Beijing: Science Press, 2018. | |
[16] | GUPTA H , BARUA M K . Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS[J]. Journal of Cleaner Production, 2017,152: 242-258. |
[17] | GOVINDAN K , KADZI?SKI M , SIVAKUMAR R . Application of a novel PROMETHEE-based method for construction of a group compromise ranking to prioritization of green suppliers in food supply chain[J]. Omega, 2017,71: 129-145. |
[18] | WU T , BLACKHURST J . Supplier evaluation and selection:an augmented DEA approach[J]. International Journal of Production Research, 2009,47(16): 4593-4608. |
[19] | HSU B M , CHIANG C Y , SHU M H . Supplier selection using fuzzy quality data and their applications to touch screen[J]. Expert Systems With Applications, 2010,37(9): 6192-6200. |
[20] | GOLMOHAMMADI D , CREESE R C , VALIAN H ,et al. Supplier selection based on a neural network model using genetic algorithm[J]. IEEE Transactions on Neural Networks, 2009,20(9): 1504-1519. |
[21] | 李益兵, 宋东林, 王磊 . 基于混合PSO-Adam神经网络的外协供应商评价决策模型[J]. 控制与决策, 2018,33(12): 2142-2152. |
LI Y B , SONG D L , WANG L . Based on hybrid PSO-Adam neural networks decision making model for outsourcing supplier evaluation[J]. Control and Decision, 2018,33(12): 2142-2152. | |
[22] | KAWTUMMACHAI R , VAN HOP N . Order allocation in a multiple-supplier environment[J]. International Journal of Production Economics, 2005,93/94: 231-238. |
[23] | 孟懂懂 . 集中采购模式下多目标订单分配模型研究[D]. 合肥:合肥工业大学, 2016. |
MENG D D . Research on multi-objective order allocation model under centralized procurement[D]. Hefei:Hefei University of Technology, 2016. | |
[24] | LIN R H . An integrated FANP-MOLP for supplier evaluation and order allocation[J]. Applied Mathematical Modelling, 2009,33(6): 2730-2736. |
[25] | ?EBI F , OTAY ? , . A two-stage fuzzy approach for supplier evaluation and order allocation problem with quantity discounts and lead time[J]. Information Sciences, 2016,339: 143-157. |
[26] | DICKSON G W . An analysis of vendor selection systems and decisions[J]. Journal of Purchasing, 1966,2(1): 5-17. |
[27] | WEBER C A , CURRENT J R , BENTON W C . Vendor selection criteria and methods[J]. European Journal of Operational Research, 1991,50(1): 2-18. |
[28] | ZHANG W Y , DING J P , WANG Y ,et al. Multi-perspective collaborative scheduling using extended genetic algorithm with interval-valued intuitionistic fuzzy entropy weight method[J]. Journal of Manufacturing Systems, 2019,53: 249-260. |
[29] | AL-HARBI K M A S . Application of the AHP in project management[J]. International Journal of Project Management, 2001,19(1): 19-27. |
[30] | LIU Y , ECKERT C M , EARL C . A review of fuzzy AHP methods for decision-making with subjective judgements[J]. Expert Systems With Applications, 2020,161:113738. |
[31] | 王金星, 汪海涛, 姜瑛 ,等. 基于三角模糊数层次分析法的软件质量评价模型研究[J]. 计算机与数字工程, 2017,45(9): 1693-1697. |
WANG J X , WANG H T , JIANG Y ,et al. Research on software quality evaluation model based on triangular fuzzy number analytic hierarchy process[J]. Computer & Digital Engineering, 2017,45(9): 1693-1697. | |
[32] | 张益, 张俊容 . 基于兼容度和差异度的多属性评价方案优化方法[J]. 西南大学学报(自然科学版), 2010,32(11): 78-82. |
ZHANG Y , ZHANG J R . A multi-attribute optimization method based on compatibility degree and difference degree[J]. Journal of Southwest University (Natural Science Edition), 2010,32(11): 78-82. | |
[33] | 刘传修, 张菁, 刘小康 ,等. 基于IVIF-AHP与改进CRITIC法的配电网规划方案综合评估[J]. 控制工程, 2022,29(2): 322-329. |
LIU C X , ZHANG J , LIU X K ,et al. Comprehensive evaluation of distribution network planning scheme based on IVIF-AHP and improved CRITIC method[J]. Control Engineering of China, 2022,29(2): 322-329. | |
[34] | 白丽丽, 白尚旺, 党伟超 ,等. 基于离差最大化组合赋权的煤矿安全评价研究[J]. 计算机应用与软件, 2021,38(4): 82-87. |
BAI L L , BAI S W , DANG W C ,et al. Coal mine safety assessment based on maximum deviation combination empowerment[J]. Computer Applications and Software, 2021,38(4): 82-87. |
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