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Linguistic Approaches for Multiple Criteria Decision Making and Applications

Submission Deadline: 30 June 2023 (closed)

Guest Editors

Prof. Huchang Liao, Sichuan University, China
Dr. Xingli Wu, Sichuan University, China
Dr. Abbas Mardani, University of South Florida, United States
Prof. Zeshui Xu, Sichuan University, China
Prof. Edmundas Kazimieras Zavadskas, Vilnius Gediminas Technical University, Lithuania

Summary

Artificial intelligence is an intelligent tool that assists human agents in decision making. An agent’s behavior shall be driven by an underlying preference model to clearly reflect the user’s preferences. Language is the most common and intuitive form of human expression. The acquisition of preference information requires not only a modeling language and suitable representations, but also automatic learning, discovery and modeling methods.

 

Linguistic approach deals with linguistic variables whose values of words or sentences are in natural or artificial language, rather than specific numbers. It enhances the feasibility, flexibility, and credibility of assessments, thus advancing decision analysis to a new research area – computing with words. To date, various models of linguistic expressions, such as probabilistic linguistic term sets, have been proposed to portray different categories of linguistic evaluation information. Real world decision-making problems usually involve selecting, ranking, or sorting a finite set of alternatives evaluated on a finite set of criteria. Multiple criteria decision making (MCDM) provides rich techniques to solve such problems, designed to recommend decisions that are consistent with the value systems of decision-makers. There are three well-established theories for modeling value systems: 1) multiple criteria value/utility theory, 2) outranking relations, and 3) decision rules, and two ways of preference elicitation: 1) aggregation approaches with direct preference elicitation, where decision makers are required to provide the values of parameters in a default preference model; 2) disaggregation approaches with indirect preference elicitation based on holistic judgments on reference alternatives. The theory and methods of MCDM based on linguistic approaches have gained much attention of researchers in the past, and have made great progress in research. However, in the context of big data, decision-making problems tend to be complex. For example, online reviews are an example of evaluation information that is large in scale and presents an unstructured form. How to deal with complex MCDM problems under linguistic settings still needs further research.

 

This special issue aims at encouraging researchers and practitioners to address challenges associated with decision making methodologies inlinguistic contexts. We are looking for papers with a focus on MCDM methods considering complex situations, including large-scale and unstructured linguistic evaluations, large-scale alternatives and large-scale decision makers. In particular, new approaches of decision making in data-driven topics are especially welcome. Potential topics include but are not limited to methods and applications in:

 

n  Natural language processing and computing with words;

n  Large-scale group decision making with linguistic approaches;

n  Multiple criteria decision making with linguistic approaches;

n  Preference disaggregation analysis with linguistic approaches;

n  Data-driven decision making with linguistic approaches;

n  Decision support system with linguistic approaches.



Keywords

Computing with words; multiple criteria decision making; linguistic approach; big data; preference model

Published Papers


  • Open Access

    ARTICLE

    Agricultural Investment Project Decisions Based on an Interactive Preference Disaggregation Model Considering Inconsistency

    Xingli Wu, Huchang Liao, Shuxian Sun, Zhengjun Wan
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3125-3146, 2024, DOI:10.32604/cmes.2023.047031
    (This article belongs to this Special Issue: Linguistic Approaches for Multiple Criteria Decision Making and Applications)
    Abstract Agricultural investment project selection is a complex multi-criteria decision-making problem, as agricultural projects are easily influenced by various risk factors, and the evaluation information provided by decision-makers usually involves uncertainty and inconsistency. Existing literature primarily employed direct preference elicitation methods to address such issues, necessitating a great cognitive effort on the part of decision-makers during evaluation, specifically, determining the weights of criteria. In this study, we propose an indirect preference elicitation method, known as a preference disaggregation method, to learn decision-maker preference models from decision examples. To enhance evaluation ease, decision-makers merely need to compare pairs of alternatives with which… More >

  • Open Access

    ARTICLE

    An Evidence-Based CoCoSo Framework with Double Hierarchy Linguistic Data for Viable Selection of Hydrogen Storage Methods

    Raghunathan Krishankumar, Dhruva Sundararajan, K. S. Ravichandran, Edmundas Kazimieras Zavadskas
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2845-2872, 2024, DOI:10.32604/cmes.2023.029438
    (This article belongs to this Special Issue: Linguistic Approaches for Multiple Criteria Decision Making and Applications)
    Abstract Hydrogen is the new age alternative energy source to combat energy demand and climate change. Storage of hydrogen is vital for a nation’s growth. Works of literature provide different methods for storing the produced hydrogen, and the rational selection of a viable method is crucial for promoting sustainability and green practices. Typically, hydrogen storage is associated with diverse sustainable and circular economy (SCE) criteria. As a result, the authors consider the situation a multi-criteria decision-making (MCDM) problem. Studies infer that previous models for hydrogen storage method (HSM) selection (i) do not consider preferences in the natural language form; (ii) weights… More >

  • Open Access

    ARTICLE

    A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

    Rongrong Ren, Luyang Su, Xinyu Meng, Jianfang Wang, Meng Zhao
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 429-458, 2024, DOI:10.32604/cmes.2023.027310
    (This article belongs to this Special Issue: Linguistic Approaches for Multiple Criteria Decision Making and Applications)
    Abstract With the development of big data and social computing, large-scale group decision making (LGDM) is now merging with social networks. Using social network analysis (SNA), this study proposes an LGDM consensus model that considers the trust relationship among decision makers (DMs). In the process of consensus measurement: the social network is constructed according to the social relationship among DMs, and the Louvain method is introduced to classify social networks to form subgroups. In this study, the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights. In the process of consensus improvement: A… More >

  • Open Access

    ARTICLE

    Multidimensional Quality Evaluation of Graduate Thesis: Based on the Probabilistic Linguistic MABAC Method

    Yuyan Luo, Xiaoxu Zhang, Tao Tong, Yong Qin, Zheng Yang
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 2049-2076, 2023, DOI:10.32604/cmes.2023.025413
    (This article belongs to this Special Issue: Linguistic Approaches for Multiple Criteria Decision Making and Applications)
    Abstract Graduate education is the main way to train high-level innovative talents, the basic layout to cope with the global talent competition, and the important cornerstone for implementing the innovation-driven development strategy and building an innovation-driven country. Therefore, graduate education is of great remarkably to the development of national education. As an important manifestation of graduate education, the quality of a graduate thesis should receive more attention. It is conducive to promoting the quality of graduates by supervising and examining the quality of the graduate thesis. For this purpose, this work is based on text mining, expert interviews, and questionnaire surveys… More >

  • Open Access

    ARTICLE

    A Multi-Attribute Decision-Making Method Using Belief-Based Probabilistic Linguistic Term Sets and Its Application in Emergency Decision-Making

    Runze Liu, Liguo Fei, Jianing Mi
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 2039-2067, 2023, DOI:10.32604/cmes.2023.024927
    (This article belongs to this Special Issue: Linguistic Approaches for Multiple Criteria Decision Making and Applications)
    Abstract Probabilistic linguistic term sets (PLTSs) are an effective tool for expressing subjective human cognition that offer advantages in the field of multi-attribute decision-making (MADM). However, studies have found that PLTSs have lost their ability to accurately capture the views of decision-makers (DMs) in certain circumstances, such as when the DM hesitates between multiple linguistic terms or the decision information is incomplete, thus affecting their role in the decision-making process. Belief function theory is a leading stream of thought in uncertainty processing that is suitable for dealing with the limitations of PLTS. Therefore, the purpose of this study is to extend… More >

    Graphic Abstract

    A Multi-Attribute Decision-Making Method Using Belief-Based Probabilistic Linguistic Term Sets and Its Application in Emergency Decision-Making

  • Open Access

    ARTICLE

    Non-Cooperative Behavior Management in Large-Scale Group Decision-Making Considering the Altruistic Behaviors of Experts and Its Application in Emergency Alternative Selection

    Mingjun Jiang
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 487-515, 2023, DOI:10.32604/cmes.2023.024014
    (This article belongs to this Special Issue: Linguistic Approaches for Multiple Criteria Decision Making and Applications)
    Abstract Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns, leading to non-cooperative behaviors during the consensus-reaching process. Many studies on non-cooperative behavior management assumed that the maximum degree of cooperation of experts is to totally accept the revisions suggested by the moderator, which restricted individuals with altruistic behaviors to make more contributions in the agreement-reaching process. In addition, when grouping a large group into subgroups by clustering methods, existing studies were based on the similarity of evaluation values or trust relationships among experts separately but did not consider them simultaneously. In this study, we introduce a… More >

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