Special lssues
Table of Content

Data-Driven Robust Group Decision-Making Optimization and Application

Submission Deadline: 30 December 2023 (closed)

Guest Editors

Prof. Shaojian Qu, Nanjing University of Information Science and Technology, China
Prof. Zhichao Zheng, National University of Singapore, Singapore
Prof. Ying Ji, Shanghai University, China
Prof. Qingguo Bai, Qufu Normal University, China

Summary

At present, with the parallel of global integration and reverse integration, as well as the rapid development of Internet technology and communication technology, the environment of governments, enterprises and other organizations is becoming more and more complex. Some major decision-making problems urgently need to make group decision-making with the help of group wisdom. As the basic decision-making form of human social activities, group decision-making can take into account various interests and overcome the shortcomings of individual knowledge, information and ability. It has been widely used in many fields, such as emergency decision-making of major emergencies, major strategic decision-making of the government, logistics and supply chain management decision-making and so on.


However, with the rapid development and deep integration of information technology, a new chapter of digital life has been opened and people have been replaced into the era of big data. Because big data has the characteristics of large volume, diversity, dynamics and low value density, dynamic and socialized group decision-making in the big data environment brings new challenges to the decision-making field, which is worthy of further exploration. Through the data-driven method, the consistent or compromise scheme is more effective than the traditional decision-making method. Therefore, applying data-driven technology to carry out more research and innovation on group decision-making has extensive theoretical and practical significance.


The aim of this Special Issue is to solicit the latest research and review articles on group decision-making driven by data-driven. We hope to combine the two studies, including new theoretical methods based on existing theories. We welcome new ideas to explore the future development direction of intelligent group decision-making. Welcome to provide original contributions of novel theories, methods and applications to the problems of data-driven group decision-making research.

The main topics of this special issue include, but are not limited to, the following:

Application of robust optimization method in uncertain group decision-making

Application of data-driven method in group decision-making

Application of machine learning method in group decision-making

Clustering method of preference data in group decision-making

Multistage dynamic group decision-making method

Large group emergency decision-making based on decision maker behavior data mining

Data collection and extraction in online reviews

Data mining for feature learning, classification, regression and clustering 


Keywords

Group decision-marking; Robust optimization; Data-driven; Large group emergency decision-making

Published Papers


  • Open Access

    ARTICLE

    Stroke Risk Assessment Decision-Making Using a Machine Learning Model: Logistic-AdaBoost

    Congjun Rao, Mengxi Li, Tingting Huang, Feiyu Li
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 699-724, 2024, DOI:10.32604/cmes.2023.044898
    (This article belongs to the Special Issue: Data-Driven Robust Group Decision-Making Optimization and Application)
    Abstract Stroke is a chronic cerebrovascular disease that carries a high risk. Stroke risk assessment is of great significance in preventing, reversing and reducing the spread and the health hazards caused by stroke. Aiming to objectively predict and identify strokes, this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost (Logistic-AB) based on machine learning. First, the categorical boosting (CatBoost) method is used to perform feature selection for all features of stroke, and 8 main features are selected to form a new index evaluation system to predict the risk of stroke. Second, the borderline… More >

  • Open Access

    ARTICLE

    Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

    Cheng Wang, Haoran Zhu, Congjun Rao
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 691-718, 2024, DOI:10.32604/cmes.2023.029258
    (This article belongs to the Special Issue: Data-Driven Robust Group Decision-Making Optimization and Application)
    Abstract Cardiovascular disease (CVD) has gradually become one of the main causes of harm to the life and health of residents. Exploring the influencing factors and risk assessment methods of CVD has become a general trend. In this paper, a machine learning-based decision-making mechanism for risk assessment of CVD is designed. In this mechanism, the logistics regression analysis method and factor analysis model are used to select age, obesity degree, blood pressure, blood fat, blood sugar, smoking status, drinking status, and exercise status as the main pathogenic factors of CVD, and an index system of risk More >

  • Open Access

    ARTICLE

    Mixed Integer Robust Programming Model for Multimodal Fresh Agricultural Products Terminal Distribution Network Design

    Feng Yang, Zhong Wu, Xiaoyan Teng
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 719-738, 2024, DOI:10.32604/cmes.2023.028699
    (This article belongs to the Special Issue: Data-Driven Robust Group Decision-Making Optimization and Application)
    Abstract The low efficiency and high cost of fresh agricultural product terminal distribution directly restrict the operation of the entire supply network. To reduce costs and optimize the distribution network, we construct a mixed integer programming model that comprehensively considers to minimize fixed, transportation, fresh-keeping, time, carbon emissions, and performance incentive costs. We analyzed the performance of traditional rider distribution and robot distribution modes in detail. In addition, the uncertainty of the actual market demand poses a huge threat to the stability of the terminal distribution network. In order to resist uncertain interference, we further extend More >

    Graphic Abstract

    Mixed Integer Robust Programming Model for Multimodal Fresh Agricultural Products Terminal Distribution Network Design

  • Open Access

    ARTICLE

    Novel Early-Warning Model for Customer Churn of Credit Card Based on GSAIBAS-CatBoost

    Yaling Xu, Congjun Rao, Xinping Xiao, Fuyan Hu
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2715-2742, 2023, DOI:10.32604/cmes.2023.029023
    (This article belongs to the Special Issue: Data-Driven Robust Group Decision-Making Optimization and Application)
    Abstract As the banking industry gradually steps into the digital era of Bank 4.0, business competition is becoming increasingly fierce, and banks are also facing the problem of massive customer churn. To better maintain their customer resources, it is crucial for banks to accurately predict customers with a tendency to churn. Aiming at the typical binary classification problem like customer churn, this paper establishes an early-warning model for credit card customer churn. That is a dual search algorithm named GSAIBAS by incorporating Golden Sine Algorithm (GSA) and an Improved Beetle Antennae Search (IBAS) is proposed to… More >

    Graphic Abstract

    Novel Early-Warning Model for Customer Churn of Credit Card Based on GSAIBAS-CatBoost

  • Open Access

    ARTICLE

    Distributionally Robust Newsvendor Model for Fresh Products under Cap-and-Offset Regulation

    Xuan Zhao, Jianteng Xu, Hongling Lu
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1813-1833, 2023, DOI:10.32604/cmes.2023.025828
    (This article belongs to the Special Issue: Data-Driven Robust Group Decision-Making Optimization and Application)
    Abstract The cap-and-offset regulation is a practical scheme to lessen carbon emissions. The retailer selling fresh products can adopt sustainable technologies to lessen greenhouse gas emissions. We aim to analyze the optimal joint strategies on order quantity and sustainable technology investment when the retailer faces stochastic market demand and can only acquire the mean and variance of distribution information. We construct a distributionally robust optimization model and use the Karush-Kuhn-Tucker (KKT) conditions to solve the analytic formula of optimal solutions. By comparing the models with and without investing in sustainable technologies, we examine the effect of More >

    Graphic Abstract

    Distributionally Robust Newsvendor Model for Fresh Products under Cap-and-Offset Regulation

  • Open Access

    ARTICLE

    Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

    Xuhui Zhu, Pingfan Xia, Qizhi He, Zhiwei Ni, Liping Ni
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 653-671, 2023, DOI:10.32604/cmes.2022.022985
    (This article belongs to the Special Issue: Data-Driven Robust Group Decision-Making Optimization and Application)
    Abstract Multiple classifier system exhibits strong classification capacity compared with single classifiers, but they require significant computational resources. Selective ensemble system aims to attain equivalent or better classification accuracy with fewer classifiers. However, current methods fail to identify precise solutions for constructing an ensemble classifier. In this study, we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm (ECDPB). Considering that extreme learning machines (ELMs) have rapid learning rates and good generalization ability, they can serve as the basic classifier for creating multiple candidates while using fewer computational resources. Meanwhile, we More >

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