多准则决策中数据规范化方法的发展:MARCOS方法的应用

IF 1.9 Q3 ENGINEERING, MANUFACTURING

Manufacturing Review Pub Date : 2022-01-01 DOI:10.1051/mfreview/2022019

D. Trung

{"title":"多准则决策中数据规范化方法的发展:MARCOS方法的应用","authors":"D. Trung","doi":"10.1051/mfreview/2022019","DOIUrl":null,"url":null,"abstract":"The purpose of the data normalization is to transfer the quantities with different dimensions to the same dimensionless form. The multi-criteria decision-making (MCDM) methods that require identifying the weight for each criterion, so the data normalization should be performed. In this study, five distinct data normalization methods were used in combination with a multi-criteria decision-making method (MARCOS method). All five of these data normalization methods were performed in combining with the MARCOS method and applied in three different cases. The number of solutions and the criteria in each case were different. Two different weighting methods were also used in each situation. After defining the most suitable data normalization methods in combining with the MARCOS method, this study proposed two new data normalization methods. The results show that solution rank is likely stable. The works in the future were mentioned in the last section of this article as well.","PeriodicalId":51873,"journal":{"name":"Manufacturing Review","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Development of data normalization methods for multi-criteria decision making: applying for MARCOS method\",\"authors\":\"D. Trung\",\"doi\":\"10.1051/mfreview/2022019\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The purpose of the data normalization is to transfer the quantities with different dimensions to the same dimensionless form. The multi-criteria decision-making (MCDM) methods that require identifying the weight for each criterion, so the data normalization should be performed. In this study, five distinct data normalization methods were used in combination with a multi-criteria decision-making method (MARCOS method). All five of these data normalization methods were performed in combining with the MARCOS method and applied in three different cases. The number of solutions and the criteria in each case were different. Two different weighting methods were also used in each situation. After defining the most suitable data normalization methods in combining with the MARCOS method, this study proposed two new data normalization methods. The results show that solution rank is likely stable. The works in the future were mentioned in the last section of this article as well.\",\"PeriodicalId\":51873,\"journal\":{\"name\":\"Manufacturing Review\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Manufacturing Review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1051/mfreview/2022019\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Manufacturing Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1051/mfreview/2022019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}

引用次数: 16

摘要

数据归一化的目的是将不同维数的量转化为相同的无量纲形式。多标准决策(MCDM)方法需要确定每个标准的权重，因此需要进行数据归一化处理。在本研究中，五种不同的数据归一化方法与多准则决策方法(MARCOS方法)相结合。所有这五种数据归一化方法都与MARCOS方法结合使用，并应用于三种不同的情况。每种情况下的解决方案的数量和标准是不同的。在每种情况下，还使用了两种不同的加权方法。本研究结合MARCOS方法确定了最适合的数据归一化方法后，提出了两种新的数据归一化方法。结果表明，解阶可能是稳定的。本文的最后一节也提到了今后的工作。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Development of data normalization methods for multi-criteria decision making: applying for MARCOS method

The purpose of the data normalization is to transfer the quantities with different dimensions to the same dimensionless form. The multi-criteria decision-making (MCDM) methods that require identifying the weight for each criterion, so the data normalization should be performed. In this study, five distinct data normalization methods were used in combination with a multi-criteria decision-making method (MARCOS method). All five of these data normalization methods were performed in combining with the MARCOS method and applied in three different cases. The number of solutions and the criteria in each case were different. Two different weighting methods were also used in each situation. After defining the most suitable data normalization methods in combining with the MARCOS method, this study proposed two new data normalization methods. The results show that solution rank is likely stable. The works in the future were mentioned in the last section of this article as well.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Manufacturing Review ENGINEERING, MANUFACTURING-

CiteScore

5.40

自引率

12.00%

发文量

审稿时长

8 weeks

期刊介绍： The aim of the journal is to stimulate and record an international forum for disseminating knowledge on the advances, developments and applications of manufacturing engineering, technology and applied sciences with a focus on critical reviews of developments in manufacturing and emerging trends in this field. The journal intends to establish a specific focus on reviews of developments of key core topics and on the emerging technologies concerning manufacturing engineering, technology and applied sciences, the aim of which is to provide readers with rapid and easy access to definitive and authoritative knowledge and research-backed opinions on future developments. The scope includes, but is not limited to critical reviews and outstanding original research papers on the advances, developments and applications of: Materials for advanced manufacturing (Metals, Polymers, Glass, Ceramics, Composites, Nano-materials, etc.) and recycling, Material processing methods and technology (Machining, Forming/Shaping, Casting, Powder Metallurgy, Laser technology, Joining, etc.), Additive/rapid manufacturing methods and technology, Tooling and surface-engineering technology (fabrication, coating, heat treatment, etc.), Micro-manufacturing methods and technology, Nano-manufacturing methods and technology, Advanced metrology, instrumentation, quality assurance, testing and inspection, Mechatronics for manufacturing automation, Manufacturing machinery and manufacturing systems, Process chain integration and manufacturing platforms, Sustainable manufacturing and Life-cycle analysis, Industry case studies involving applications of the state-of-the-art manufacturing methods, technology and systems. Content will include invited reviews, original research articles, and invited special topic contributions.