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Decision-Making Method based on Mixed Integer Linear Programming and Rough Set: A Case Study of Diesel Engine Quality and Assembly Clearance Data

Author

Listed:
  • Wenbing Chang

    (School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
    These two authors contributed equally to this work.)

  • Xinglong Yuan

    (School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

  • Yalong Wu

    (Henan Diesel Engine Industry Co., Luoyang 471039, China)

  • Shenghan Zhou

    (School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
    These two authors contributed equally to this work.)

  • Jingsong Lei

    (School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

  • Yiyong Xiao

    (School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

Abstract

The purpose of this paper is to establish a decision-making system for assembly clearance parameters and machine quality level by analyzing the data of assembly clearance parameters of diesel engine. Accordingly, we present an extension of the rough set theory based on mixed-integer linear programming (MILP) for rough set-based classification (MILP-FRST). Traditional rough set theory has two shortcomings. First, it is sensitive to noise data, resulting in a low accuracy of decision systems based on rough sets. Second, in the classification problem based on rough sets, the attributes cannot be automatically determined. MILP-FRST has the advantages of MILP in resisting noisy data and has the ability to select attributes flexibly and automatically. In order to prove the validity and advantages of the proposed model, we used the machine quality data and assembly clearance data of 29 diesel engines of a certain type to validate the proposed model. Experiments show that the proposed decision-making method based on MILP-FRST model can accurately determine the quality level of the whole machine according to the assembly clearance parameters.

Suggested Citation

  • Wenbing Chang & Xinglong Yuan & Yalong Wu & Shenghan Zhou & Jingsong Lei & Yiyong Xiao, 2019. "Decision-Making Method based on Mixed Integer Linear Programming and Rough Set: A Case Study of Diesel Engine Quality and Assembly Clearance Data," Sustainability, MDPI, vol. 11(3), pages 1-21, January.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:3:p:620-:d:200646
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    References listed on IDEAS

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    1. Su, Chao-Ton & Hsu, Jyh-Hwa, 2006. "Precision parameter in the variable precision rough sets model: an application," Omega, Elsevier, vol. 34(2), pages 149-157, April.
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    Cited by:

    1. Miltiadis D. Lytras & Anna Visvizi, 2021. "Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making," Sustainability, MDPI, vol. 13(7), pages 1-3, March.

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