Multiple Criteria Hierarchy Process in Robust Ordinal Regression

作     者：Corrente, Salvatore Greco, Salvatore Slowinski, Roman 

作者机构：Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland Univ Catania Dept Econ & Business I-95129 Catania Italy 

出 版 物：《DECISION SUPPORT SYSTEMS》 (决策支持系统)

年 卷 期：2012年第53卷第3期

页      面：660-674页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Polish National Science Centre [NN519 441939] 

主　　题：Multiple Criteria Decision Aiding Hierarchy of criteria Multiple Criteria Hierarchy Process Robust Ordinal Regression Preference modeling 

摘      要：A great majority of methods designed for Multiple Criteria Decision Aiding (MCDA) assume that all evaluation criteria are considered at the same level, however, it is often the case that a practical application is imposing a hierarchical structure of criteria. The hierarchy helps decomposing complex decision making problems into smaller and manageable subtasks, and thus, it is very attractive for users. To handle the hierarchy of criteria in MCDA, we propose a methodology called Multiple Criteria Hierarchy Process (MCHP) which permits consideration of preference relations with respect to a subset of criteria at any level of the hierarchy. MCHP can be applied to any MCDA method. In this paper, we apply MCHP to Robust Ordinal Regression (ROR) being a family of MCDA methods that takes into account all sets of parameters of an assumed preference model, which are compatible with preference information elicited by a Decision Maker (DM). As a result of ROR, one gets necessary and possible preference relations in the set of alternatives, which hold for all compatible sets of parameters or for at least one compatible set of parameters, respectively. Applying MCHP to ROR one gets to know not only necessary and possible preference relations with respect to the whole set of criteria, but also necessary and possible preference relations related to subsets of criteria at different levels of the hierarchy. We also show how MCHP can be extended to handle group decision and interactions among criteria. (C) 2012 Elsevier B.V. All rights reserved.