PL-MACONT-I: A Probabilistic Linguistic MACONT-I Method for Multi-Criterion Sorting

Abstract

Multi-criterion sorting (MCS) problems aim to assign alternatives to corresponding categories according to multiple criteria. Various MCS methods have been proposed. However, these methods need to preset reference profile of each category to arrange alternatives, which may be difficult for decision makers. To circumvent this issue, this study proposes an MCS method without setting reference profiles of categories. The mixed aggregation by comprehensive normalization technique (MACONT) method, as a recently-developed multi-criterion decision-making method, can obtain a multi-aspect and comprehensive solution for choice and ranking problems. To solve multi-criteria sorting problems, this study introduces an MACONT-I method in which the deck of card method (DCM) is applied to derive criteria weights. Furthermore, to enhance the applicability of the proposed method in decision-making with uncertain information, we extend the DCM and the proposed MACONT-I method to probabilistic linguistic context and propose a PL-MACONT-I method for multi-criterion sorting. An illustrative example is provided to demonstrate the practicality of the proposed method. Through sensitivity analysis and comparative analysis, the merits of the proposed method are emphasized.