A novel collaborative decision-making method based on generalized abductive learning for resolving design conflicts

Abstract

In complex product design, lots of time and resources are consumed to choose a preference-based compromise decision from non-inferior preliminary design models with multi-objective conflicts. However, since complex products involve intensive multi-domain knowledge, preference is not only a comprehensive representation of objective data and subjective knowledge but also characterized by fuzzy and uncertain. In recent years, enormous challenges are involved in the design process, within the increasing complexity of preference. This article mainly proposes a novel decision-making method based on generalized abductive learning (G-ABL) to achieve autonomous and efficient decision-making driven by data and knowledge collaboratively. The proposed G-ABL framework, containing three cores: classifier, abductive kernel, and abductive machine, supports preference integration from data and fuzzy knowledge. In particular, a subtle improvement is presented for WK-means based on the entropy weight method (EWM) to address the local static weight problem caused by the fixed data preferences as the decision set is locally invariant. Furthermore, fuzzy comprehensive evaluation (FCE) and Pearson correlation are adopted to quantify domain knowledge and obtain abducted labels. Multi-objective weighted calculations are utilized only to label and compare solutions in the final decision set. Finally, an engineering application is provided to verify the effectiveness of the proposed method, and the superiority of which is illustrated by comparative analysis.

Keywords

Collaborative decision-making / Conflict resolution / Generalized abductive learning / EWM based WK-means / Fuzzy comprehensive evaluation

References

[1] ZhouS., CaoY., ZhangZ., LiuY.. System design and simulation integration for complex mechatronic products based on SysML and modelica. J. Comput.-Aided Des. Comput. Graph., 2018, 30(4):728-738

[2] ZhangP., NieZ., DongY., ZhangZ., YuF., TanR.. Smart concept design based on recessive inheritance in complex electromechanical system. Adv. Eng. Inform., 2020, 43

[3] ChagraouiH., SoulaM.. Multidisciplinary design optimization of stiffened panels using collaborative optimization and artificial neural network. J. Mech. Eng. Sci., 2018, 232(20):3595-3611

[4] ZadehP.M., SayadiM., KosariA.. An efficient metamodel-based multi-objective multidisciplinary design optimization framework. Appl. Soft Comput., 2019, 74: 760-782

[5] TawhidM.A., SavsaniV.. Multi-objective sine-cosine algorithm (MO-SCA) for multi-objective engineering design problems. Neural Comput. Appl., 2019, 31: 915-929

[6] AfshariH., HareW., TesfamariamS.. Constrained multi-objective optimization algorithms: review and comparison with application in reinforced concrete structures. Appl. Soft Comput., 2019, 83

[7] Sayyadi ShahrakiN., ZahiriS.H.. An improved multi-objective learning automata and its application in VLSI circuit design. Memetic Comp., 2020, 12: 115-128

[8] LeiG., BramerdorferG., MaB., GuoY., ZhuJ.. Robust design optimization of electrical machines: multi-objective approach. IEEE Trans. Energy Convers., 2021, 36(1):390-401

[9] MaY., XiaoY., WangJ., ZhouL.. Multicriteria optimal Latin hypercube design-based surrogate-assisted design optimization for a permanent-magnet vernier machine. IEEE Trans. Magn., 2022, 58(2):1-5

[10] ChenQ., ZhangQ., GaoQ.Y., FengZ., TangQ., ZhangG.. Design and optimization of a space net capture system based on a multi-objective evolutionary algorithm. Acta Astronaut., 2020, 167: 286-295

[11] TawhidM.A., SavsaniV.. ε-constraint heat transfer search (ε-HTS) algorithm for solving multi-objective engineering design problems. J. Comput. Des. Eng., 2018, 5(1):104-119

[12] Z. Cui, J. Yue, C. Wu, Y. Su, F. Wu, A surrogate-assisted multi-objective optimization method for preliminary design of horizontal tail control system. 2022 34th Chinese Control and Decision Conference (CCDC) (2022). In press

[13] OlabanjiO., MpofuK.. Fusing multi-attribute decision models for decision making to achieve optimal product design. Found. Comput. Decision Sci., 2020, 45(4):305-337

[14] GargH.. Generalized intuitionistic fuzzy entropy-based approach for solving multi-attribute decision-making problems with unknown attribute weights. Proc. Natl. Acad. Sci. India Sect. A Phys. Sci., 2019, 89: 129-139

[15] YangG., YangJ., XuD., KhoveyniM.. A three-stage hybrid approach for weight assignment in MADM. Omega-Int. J. Manag. Sci., 2017, 71: 93-105

[16] ChinK.S., FuC., WangY.. A method of determining attribute weights in evidential reasoning approach based on incompatibility among attributes. Comput. Ind. Eng., 2015, 87: 150-162

[17] DuoT., GuoJ., WuF., ZhaiR.. Application of entropy-based multi-attribute decision-making method to structured selection of settlement. J. Vis. Commun. Image Represent., 2019, 58: 220-232

[18] IshizakaA., NguyenN.H.. Calibrated fuzzy AHP for current bank account selection. Expert Syst. Appl., 2013, 40(9):3775-3783

[19] JoshiD., KumarS.. Interval-valued intuitionistic hesitant fuzzy Choquet integral based TOPSIS method for multi-criteria group decision making. Eur. J. Oper. Res., 2016, 248(1):183-191

[20] LiangY., ZhengQ.. A decision support system for satellite layout integrating multi-objective optimization and multi-attribute decision making. J. Syst. Eng. Electron., 2019, 30(3):535-544

[21] LuJ., GuoH., YangP., LiC., YangL., ZhangZ.. Site selection of photovoltaic power station based on weighted least-square method and threshold normalization. 2018 International Conference on Power System Technology (POWERCON), 2018 179-184

[22] MehrabipourM., HajbabaieA.. A distributed gradient approach for system optimal dynamic traffic assignment. IEEE Trans. Intell. Transp. Syst., 2022, 23(10):17410-17424

[23] TakayamaN., AraiS.. Multi-objective deep inverse reinforcement learning for weight estimation of objectives. Artif. Life Robot., 2022, 27: 594-602

[24] ZhangM., LiY.. Multi-objective optimal reactive power dispatch of power systems by combining classification-based multi-objective evolutionary algorithm and integrated decision making. IEEE Access, 2020, 8: 38198-38209

[25] LiY., ChengY., MouQ., XianS.. Novel cross-entropy based on multi-attribute group decision-making with unknown experts’ weights under interval-valued intuitionistic fuzzy environment. Int. J. Comput. Intell. Syst., 2020, 13: 1295-1304 https://dx.doi.org/10.2991/ijcis.d.200817.001

[26] ZhangZ., HuX., LiuZ., ZhaoL.. Multi-attribute decision making: an innovative method based on the dynamic credibility of experts. Appl. Math. Comput., 2021, 393

[27] YuG., LiD., FeiW.. A novel method for heterogeneous multi-attribute group decision making with preference deviation. Comput. Ind. Eng., 2018, 124: 58-64

[28] ZhouZ.. Abductive learning: towards bridging machine learning and logical reasoning. Sci. China Inf. Sci., 2019, 62

[29] HuangY., DaiW., YangJ., CaiL., ChengS., HuangR., LiY., ZhouZ.. Semi-supervised abductive learning and its application to theft judicial sentencing. 2020 IEEE International Conference on Data Mining (ICDM), 2020 1070-1075

[30] DaiW., XuQ., YuY., ZhouZ.. Bridging machine learning and logical reasoning by abductive learning. Advances in Neural Information Processing Systems (NeurIPS), 2019 2815-2826

[31] ZhangP., ZhongS., ZhuR., JiaoM.. Evaluating technical condition of stone arch bridge based on entropy method-cloud model. J. Zhengzhou Univ. Eng. Sci., 2022, 43(1):69-75

[32] HuangJ.Z., NgM.K., RongH., LiZ.. Automated variable weighting in k-means type clustering. IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27(5):657-668

[33] ChengJ., DongM., QiB.. An OW-FCE model based on MDE algorithm for evaluating integrated navigation system. IEEE Access, 2019, 7: 178918-178929

[34] AltunkayaE.C., OzkolI.. Multi-parameter aerodynamic design of a horizontal tail using an optimization approach. Aerosp. Sci. Technol., 2022, 121

[35] NaJ., LiY., HuangY., GaoG., ChenQ.. Output feedback control of uncertain hydraulic servo systems. IEEE Trans. Ind. Electron., 2020, 67(1):490-500

[36] KalitaI., RoyM.. Deep neural network-based heterogeneous domain adaptation using ensemble decision making in land cover classification. IEEE Trans. Artif. Intell., 2020, 1(2):167-180

[37] VansonG., MarangéP., LevratE.. End-of-life decision making in circular economy using generalized colored stochastic Petri nets. Auton. Intell. Syst., 2022, 2(1

[38] WangH., FangZ., WangD., LiuS.. An integrated fuzzy QFD and grey decision-making approach for supply chain collaborative quality design of large complex products. Comput. Ind. Eng., 2020, 140

[39] HaoJ., LuoS., PanL.. Computer-aided intelligent design using deep multi-objective cooperative optimization algorithm. Future Gener. Comput. Syst., 2021, 124: 49-53

[40] WuY., ZhangT., LiuD., WangY.. Data-driven multi-attribute optimization decision-making for complex product design schemes. China Mech. Eng., 2020, 31 7):865-870

[41] WangR., Milisavljevic-SyedJ., GuoL., HuangY., WangG.. Knowledge-based design guidance system for cloud-based decision support in the design of complex engineered systems. ASME J. Mech. Des., 2021, 143(7

[42] XueX., LiX., WangL., GuoW., LinY.. Research on the design knowledge of complex products based on the cellular automata model. J. Mach. Des., 2018, 35 8):1-6

[43] JiangR., CiS., LiuD., ChengX., PanZ.. A hybrid multi-objective optimization method based on NSGA-II algorithm and entropy weighted TOPSIS for lightweight design of dump truck carriage. Mach., 2021, 9 8

Funding

the National Key Research and Development Program of China(2018YFB1700900)