Decision-Making Modeling and Optimization

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4795

Special Issue Editors

Business School, Sun Yat-Sen University, Shenzhen, China
Interests: large-scale group decision making; consensus; risk management; supply chain management
College of Management, Shenzhen University, Shenzhen, China
Interests: decision theory and methods; information management; large-scale group decision-making and consensus; big data decision; social network analysis; tourism management

E-Mail Website
Guest Editor
School of Business, Central South University, Changsha, China
Interests: complex large group decision-making theory and methods; big data intelligent decision-making methods; information systems and decision support systems; emergency management and decision-making; risk analysis and management

Special Issue Information

Dear Colleagues,

Axioms aims to promote research and applications of mathematics, mathematical logic, and mathematical physics. Decision-making modeling and optimization are the use of methodological models from fields such as mathematics and operations research to solve decision problems in economics, management, engineering, and other fields. In the past several decades, numerous decision-making methods have been proposed, such as AHP, ANP, TOPSIS, VIKOR, MULTIMOORA, DEMATEL, PROMETHEE, ELECTRE, etc. These classical methods have been extended to different decision contexts, such as (1) in terms of information expression forms, including interval numbers, fuzzy sets, intuitionistic fuzzy sets, hesitant fuzzy sets, linguistic terms, hesitant fuzzy linguistic terms, probabilistic linguistic term sets, etc.; (2) in terms of the decision-making environment, such as social network decision making, large-scale group decision making, and risk decision making; (3) considering decision-makers’ behaviors, there are prospect-theory-based decision making, regret-theory-based decision making, non-cooperative game-based decision making, etc.; (4) in practical applications, such as emergency decision making, investment evaluation, conflict management, etc. 

This Special Issue seeks to bring together the up-to-date developments in decision modeling and optimization in terms of methodology, algorithms, and applications. Any theoretical, empirical, and experimental work related to decision making and optimization, as well as related applications in economics, management, and technology, is welcome. Mathematical approaches to the challenges of decision modeling and optimization, as well as new techniques for disruptive improvements to traditional paradigms, are especially welcome. We invite researchers and experts worldwide to submit high-quality original research papers and critical survey articles. Topics to be discussed in this Special Issue include (but are not limited to) the following:

  • Decision making with complex forms of information expression, such as probabilities, fuzzy sets, rough sets, linguistic terms, preference relations, random variables, etc.;
  • Decision making with multi-objective optimization;
  • Decision making under social networks;
  • Decision making involving large-scale decision-makers;
  • Collaborative decision making and consensus decision making;
  • Decision support systems and business analytics;
  • Decision-making models with game theory;
  • Decision making considering risk attributes;
  • Decision making for practical applications in economics, management, engineering, healthcare, etc.;
  • Decision making related to the latest information technologies, such as big data, artificial intelligence, cloud computing, etc.

Dr. Zhijiao Du
Dr. Sumin Yu
Prof. Dr. Xuanhua Xu
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • decision making with complex forms of information expression, such as probabilities, fuzzy sets, rough sets, linguistic terms, preference relations, random variables, etc.
  • decision making with multi-objective optimization
  • decision making under social networks
  • decision making involving large-scale decision-makers
  • collaborative decision making and consensus decision making
  • decision support systems and business analytics
  • decision-making models with game theory
  • decision making considering risk attributes
  • decision making for practical applications in economics, management, engineering, healthcare, etc.
  • decision making related to the latest information technologies, such as big data, artificial intelligence, cloud computing, etc.

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Published Papers (3 papers)

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Research

15 pages, 603 KiB  
Article
Evidential-Reasoning-Type Multi-Attribute Large Group Decision-Making Method Based on Public Satisfaction
by Chenguang Cai, Yuejiao Wang, Pei Wang and Hao Zou
Axioms 2024, 13(4), 276; https://doi.org/10.3390/axioms13040276 - 20 Apr 2024
Cited by 1 | Viewed by 1086
Abstract
To address public participation-oriented, large group decision-making problems with uncertain attribute weights, we propose a multi-attribute decision-making method considering public satisfaction. Firstly, a large group is organized to provide their opinions in the form of linguistic variables. Public opinions can be categorized into [...] Read more.
To address public participation-oriented, large group decision-making problems with uncertain attribute weights, we propose a multi-attribute decision-making method considering public satisfaction. Firstly, a large group is organized to provide their opinions in the form of linguistic variables. Public opinions can be categorized into two types based on their content: one reflects the effectiveness of an alternative implementation and the other reflects the public expectations. Secondly, the two types of public opinions are sorted separately by linguistic variables. The evaluation of alternatives and the evaluation of expectations in different attributes are determined, both of which are expressed in the form of linguistic distributions. These two evaluations are then compared to determine the public satisfaction of the attributes in different alternatives. Thirdly, based on the deviation of public satisfaction in different attributes, a weight optimization model is constructed to determine the attribute weights. Fourthly, leveraging the interval credibility of attribute satisfaction for various alternatives, an evidential reasoning non-linear optimization model is established to obtain the comprehensive utility evaluation value for each alternative, which is used for ranking. Finally, a numerical example is employed to validate the feasibility and effectiveness of the proposed approach. According to the results of the numerical example, it can be concluded that the proposed approach can be effectively applied to large group decision-making problems that consider public satisfaction. Based on the comparison of methods, the proposed approach has certain advantages in reflecting public opinions and setting reference points, which can ensure the reliability of the decision results. Full article
(This article belongs to the Special Issue Decision-Making Modeling and Optimization)
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26 pages, 396 KiB  
Article
Efficiency and Vulnerability in Networks: A Game Theoretical Approach
by Conrado M. Manuel and Eduardo Ortega
Axioms 2023, 12(12), 1119; https://doi.org/10.3390/axioms12121119 - 13 Dec 2023
Viewed by 1196
Abstract
Defining measures of network efficiency and vulnerability is a pivotal aspect of modern networking paradigms. We approach this issue from a game theoretical perspective, considering networks where actors have social or economic interests modeled through a cooperative game. This allows us to define, [...] Read more.
Defining measures of network efficiency and vulnerability is a pivotal aspect of modern networking paradigms. We approach this issue from a game theoretical perspective, considering networks where actors have social or economic interests modeled through a cooperative game. This allows us to define, for each network, a family of efficiency measures and another of vulnerability measures, parameterized by the game. The proposed measures use the within groups’ and the between groups’ Myerson values. These values, respectively, measure the portion of the classical Myerson allocation corresponding to the productivity of players and the part related to intermediation costs. Additionally, they indicate the portion of total centrality in social networks attributed to communication or betweenness. In our proposal, the efficiency of a network is the proportion of total productivity (or centrality) that players can retain using the network topology. Intermediation costs (and betweenness centrality) can be seen as a weakness with a negative impact. Therefore, we suggest calculating vulnerability as the proportion of expenses players incur in intermediation payments. We explore the properties of these measures and tailor them to various structures and specific games, also analyzing their asymptotic behavior. Full article
(This article belongs to the Special Issue Decision-Making Modeling and Optimization)
30 pages, 4155 KiB  
Article
Development of an Intuitive GUI-Based Fuzzy Multi-Criteria Decision Model for Comprehensive Hospital Service Quality Evaluation and Indexing
by Ateekh Ur Rehman, Mustufa Haider Abidi, Yusuf Siraj Usmani, Syed Hammad Mian and Hisham Alkhalefah
Axioms 2023, 12(10), 921; https://doi.org/10.3390/axioms12100921 - 27 Sep 2023
Viewed by 1117
Abstract
Recently, hospital care and other services have become increasingly important for patient satisfaction. Better hospital care and assistance improve patients’ medical conditions, management trust, and financial success. In this regard, monitoring and measuring hospital service quality is necessary to improve patient satisfaction and [...] Read more.
Recently, hospital care and other services have become increasingly important for patient satisfaction. Better hospital care and assistance improve patients’ medical conditions, management trust, and financial success. In this regard, monitoring and measuring hospital service quality is necessary to improve patient satisfaction and wellness. However, the evaluation of healthcare service quality is a complex and critical task due to its intangible nature. Existing methodologies often struggle to effectively incorporate multiple criteria and address uncertainties inherent in healthcare evaluations. To address these challenges, this research work seeks to develop a comprehensive and robust approach for evaluating hospital service quality to improve decision making and resource allocation for service enhancement. This study aims to evaluate multi-faceted healthcare service quality by combining many criteria and uncertainties into a single index. The model is constructed methodically utilizing fuzzy logic and decision modeling. A dataset collected from diverse healthcare facilities covering various medical specialties and regions is employed to validate and refine the model. Numerous criteria, factors, and dimensions are examined and embedded into the development of the model. Fuzzy logic is used to capture and manage healthcare evaluations’ inherent vagueness and imprecision, yielding more accurate and comprehensive outcomes. The model’s outcome is the hospital service quality fuzzy index (HSQFI), an easy-to-understand single performance measure. A graphical user interface (GUI) is developed for collecting data, and then it shows the results in the form of barriers and recommendations. Based on the findings, recommendations in terms of barriers (service criteria) to enhance the hospital’s service quality have been made. This approach can be a tool for managers or other stakeholders to quickly realize the success of their service plans and pinpoint areas that may need improvement in the future. Full article
(This article belongs to the Special Issue Decision-Making Modeling and Optimization)
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