{"title":"一个计算框架和Python模块,用于概率ELECTRE Tri- b多标准决策","authors":"Christian Ghiaus","doi":"10.1016/j.simpa.2025.100781","DOIUrl":null,"url":null,"abstract":"<div><div>ELECTRE Tri-B is a sorting and classification method for multiple-criteria decision-making (MCDM) in which alternatives are assigned to categories. The categories are completely ordered and defined by base (or reference) profiles. The <em>pELECTRE Tri</em> software implements a probabilistic extension of the ELECTRE Tri-B method designed to handle uncertainty in both the decision matrix values and the base profiles delimiting the categories. Its modular architecture enables step-by-step workflows from data input to results output, ensuring flexibility and transparency in the decision-making process. Implemented as a Python module, <em>pELECTRE Tri</em> requires no installation and can be executed locally or online. The software is supported by comprehensive documentation, including tutorials, how-to guides, theoretical explanations, and a user reference manual.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100781"},"PeriodicalIF":1.2000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"pELECTRE Tri: A computational framework and Python module for probabilistic ELECTRE Tri-B multiple-criteria decision-making\",\"authors\":\"Christian Ghiaus\",\"doi\":\"10.1016/j.simpa.2025.100781\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>ELECTRE Tri-B is a sorting and classification method for multiple-criteria decision-making (MCDM) in which alternatives are assigned to categories. The categories are completely ordered and defined by base (or reference) profiles. The <em>pELECTRE Tri</em> software implements a probabilistic extension of the ELECTRE Tri-B method designed to handle uncertainty in both the decision matrix values and the base profiles delimiting the categories. Its modular architecture enables step-by-step workflows from data input to results output, ensuring flexibility and transparency in the decision-making process. Implemented as a Python module, <em>pELECTRE Tri</em> requires no installation and can be executed locally or online. The software is supported by comprehensive documentation, including tutorials, how-to guides, theoretical explanations, and a user reference manual.</div></div>\",\"PeriodicalId\":29771,\"journal\":{\"name\":\"Software Impacts\",\"volume\":\"25 \",\"pages\":\"Article 100781\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2025-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Software Impacts\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2665963825000417\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Software Impacts","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2665963825000417","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
pELECTRE Tri: A computational framework and Python module for probabilistic ELECTRE Tri-B multiple-criteria decision-making
ELECTRE Tri-B is a sorting and classification method for multiple-criteria decision-making (MCDM) in which alternatives are assigned to categories. The categories are completely ordered and defined by base (or reference) profiles. The pELECTRE Tri software implements a probabilistic extension of the ELECTRE Tri-B method designed to handle uncertainty in both the decision matrix values and the base profiles delimiting the categories. Its modular architecture enables step-by-step workflows from data input to results output, ensuring flexibility and transparency in the decision-making process. Implemented as a Python module, pELECTRE Tri requires no installation and can be executed locally or online. The software is supported by comprehensive documentation, including tutorials, how-to guides, theoretical explanations, and a user reference manual.