{"title":"Considering regret psychology and non-cooperative competition among alternatives for heterogeneous multi-attribute group decision making","authors":"Yang Huang , Meiqiang Wang","doi":"10.1016/j.cie.2025.111132","DOIUrl":null,"url":null,"abstract":"<div><div>In reality, to select the best one from a set of alternatives, there are widespread situations that require several experts to assess the attribute values of each alternative with heterogeneous information. The purpose of this paper is to solve the heterogeneous multi-attribute group decision making (HMAGDM) problems with attribute values involving real numbers, intervals, intuitionistic fuzzy sets, interval type-2 fuzzy sets, and interval-valued hesitant fuzzy sets from the perspective that alternatives have regret psychology with respect to the assessment information and that there is a non-cooperative competitive relationship among alternatives. Therefore, a method based on regret theory and interval data envelopment analysis (DEA) game cross-efficiency model is proposed for HMAGDM. The method adopts a process of aggregation followed by exploration. In the aggregation phase, a regret theory-based expert weight determination model is constructed to derive the weights of experts, and the individual decision matrices provided by individual experts are further aggregated into a collective decision matrix. Then, the heterogeneous information in the collective decision matrix is uniformly converted into intervals, which are represented as variables with unknown parameters. According to these variables, a regret theory-based interval DEA game cross-efficiency model is proposed to calculate the comprehensive values of alternatives and thus rank alternatives. The feasibility and effectiveness of the proposed method are illustrated by a supplier selection example.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"204 ","pages":"Article 111132"},"PeriodicalIF":6.7000,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Industrial Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0360835225002785","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
In reality, to select the best one from a set of alternatives, there are widespread situations that require several experts to assess the attribute values of each alternative with heterogeneous information. The purpose of this paper is to solve the heterogeneous multi-attribute group decision making (HMAGDM) problems with attribute values involving real numbers, intervals, intuitionistic fuzzy sets, interval type-2 fuzzy sets, and interval-valued hesitant fuzzy sets from the perspective that alternatives have regret psychology with respect to the assessment information and that there is a non-cooperative competitive relationship among alternatives. Therefore, a method based on regret theory and interval data envelopment analysis (DEA) game cross-efficiency model is proposed for HMAGDM. The method adopts a process of aggregation followed by exploration. In the aggregation phase, a regret theory-based expert weight determination model is constructed to derive the weights of experts, and the individual decision matrices provided by individual experts are further aggregated into a collective decision matrix. Then, the heterogeneous information in the collective decision matrix is uniformly converted into intervals, which are represented as variables with unknown parameters. According to these variables, a regret theory-based interval DEA game cross-efficiency model is proposed to calculate the comprehensive values of alternatives and thus rank alternatives. The feasibility and effectiveness of the proposed method are illustrated by a supplier selection example.
期刊介绍:
Computers & Industrial Engineering (CAIE) is dedicated to researchers, educators, and practitioners in industrial engineering and related fields. Pioneering the integration of computers in research, education, and practice, industrial engineering has evolved to make computers and electronic communication integral to its domain. CAIE publishes original contributions focusing on the development of novel computerized methodologies to address industrial engineering problems. It also highlights the applications of these methodologies to issues within the broader industrial engineering and associated communities. The journal actively encourages submissions that push the boundaries of fundamental theories and concepts in industrial engineering techniques.