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Mathematics
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Data-Driven Decision Making: Models, Methods and Applications, 2nd Edition
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Data-Driven Decision Making: Models, Methods and Applications, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 4909

Special Issue Editors

Prof. Dr. Yu-Wang Chen

E-Mail Website
Guest Editor
Alliance Manchester Business School, The University of Manchester, Manchester M15 6PB, UK
Interests: decision sciences; data analytics; risk analysis; optimisation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mi Zhou

E-Mail Website
Guest Editor
School of Management, Hefei University of Technology, Hefei 230009, China
Interests: decision analysis under uncertainty; group decision making; data analytics
Special Issues, Collections and Topics in MDPI journals
Tao Wen

E-Mail Website
Guest Editor Assistant
Alliance Manchester Business School, The University of Manchester, Manchester M15 6PB, UK
Interests: complex networks; decision making; data science

Special Issue Information

Dear Colleagues,

Data-driven decision making is becoming increasingly important in various fields of engineering and management and more widely recognized with the rapid development of data science, decision science and interpretable artificial intelligence. In real-world decision-making problems, data usually come from different sources in different formats and are often associated with various types of uncertainty, including randomness, incompleteness, inaccuracy, and inconsistency. In addition, subjective judgment and knowledge also play important roles in making informed decisions. In recent years, decision analysis in social network environments has also attracted wide interest.  

This Special Issue aims to provide a forum for the exchange of new findings and advances in the areas of data-driven decision making and decision analytics. The topics of interest include, but are not limited to:

  • Data-driven modelling and inference;
  • Decision making under uncertainty;
  • Decision analysis in social networks;
  • Group decision making;
  • Multiple criteria decision analysis;
  • Knowledge-based decision support;
  • Decision analytics and interpretable artificial intelligence;
  • Applications of data-driven decision making in engineering and management.

Prof. Dr. Yu-Wang Chen
Dr. Mi Zhou
Guest Editors

Tao Wen
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • decision making
  • decision support
  • data-driven modelling
  • group decision making
  • preference relations
  • knowledge representation
  • evidential reasoning
  • rule-based system
  • interpretable artificial intelligence
  • social network

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Published Papers (6 papers)

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Research

26 pages, 1517 KiB  
Article
Accelerating Consensus Reaching Through Top Persuaders: A Social Persuasion Model in Social Network Group Decision Making
by Bin Pan, Jingti Han, Bo Tian, Yunhan Liu and Shenbao Liang
Mathematics 2025, 13(3), 385; https://doi.org/10.3390/math13030385 - 24 Jan 2025
Viewed by 301
Abstract
In traditional group decision-making models, it is commonly assumed that all decision makers exert equal influence on one another. However, in real-world social networks, such as Twitter and Facebook, certain individuals—known as top persuaders—hold a disproportionately large influence over others. This study formulates [...] Read more.
In traditional group decision-making models, it is commonly assumed that all decision makers exert equal influence on one another. However, in real-world social networks, such as Twitter and Facebook, certain individuals—known as top persuaders—hold a disproportionately large influence over others. This study formulates the consensus-reaching problem in social network group decision making by introducing a novel framework for predicting top persuaders. Building on social network theories, we develop a social persuasion model that integrates social influence and social status to quantify individuals’ persuasive power more comprehensively. Subsequently, we propose a new CRP that leverages the influence of top persuaders. Our simulations and comparative analyses demonstrate that: (1) increasing the number of top persuaders substantially reduces the iterations required to achieve consensus; (2) establishing trust relationships between top persuaders and other individuals accelerates the consensus process; and (3) top persuaders retain a high and stable level of influence throughout the entire CRP rounds. Our research provides practical insights into identifying and strategically guiding top persuaders to enhance the efficiency in consensus reaching and reduce social management costs within social networked environments. Full article
(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications, 2nd Edition)
23 pages, 1624 KiB  
Article
Carbon Emissions and Sustainable Supply Chains: A Stackelberg Game Analysis of Multinational Firm Relationships
by Bo Tian, Meiqi Liu, Bin Pan, Guanghui Yuan and Fei Xie
Mathematics 2024, 12(24), 3983; https://doi.org/10.3390/math12243983 - 18 Dec 2024
Viewed by 657
Abstract
Against the backdrop of global climate change and sustainable development, carbon emissions within transnational closed-loop supply chains have become a critical area of research. This paper utilizes a Stackelberg game model to analyze the relationship between a single export manufacturer and an import [...] Read more.
Against the backdrop of global climate change and sustainable development, carbon emissions within transnational closed-loop supply chains have become a critical area of research. This paper utilizes a Stackelberg game model to analyze the relationship between a single export manufacturer and an import retailer. The study investigates the optimal solutions of the supply chain model—wholesale price, retail price, sales volume, and profit—across three consumer preference scenarios: no obvious preference, pure green preference, and pure new preference. Furthermore, this paper examines the impact of carbon emissions per unit of product on supply chain decision-making under two scenarios: with and without carbon trading. Carbon trading, which significantly increases unit costs, exerts a profound influence on the strategic decisions of both manufacturers and retailers. In addition, this paper incorporates carbon tariffs and taxes into its analysis, providing a theoretical foundation for governments and policymakers to promote sustainable production and consumption practices. The validity of the model is confirmed through numerical simulations, which reveal that under pure green and pure new preference scenarios, original equipment manufacturers (OEMs) are more inclined to invest in emissions reduction to minimize tariff costs. In contrast, under no obvious preference scenarios, OEMs are more likely to adjust product portfolios to evade carbon tariffs. This research advances the understanding of low-carbon production strategies in transnational supply chains, offering both theoretical insights and practical guidance for balancing economic and environmental objectives. Full article
(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications, 2nd Edition)
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26 pages, 588 KiB  
Article
Analyzing Decision-Making in Cognitive Agent Simulations Using Generalized Linear Mixed-Effects Models
by Shengkun Xie, Chong Gan and Anna T. Lawniczak
Mathematics 2024, 12(23), 3768; https://doi.org/10.3390/math12233768 - 29 Nov 2024
Viewed by 706
Abstract
Enhancing model interpretability remains an ongoing challenge in predictive modelling, especially when applied to simulation data from complex systems. Investigating the influence and effects of design factors within computer simulations of complex systems requires assessing variable importance through statistical models. These models are [...] Read more.
Enhancing model interpretability remains an ongoing challenge in predictive modelling, especially when applied to simulation data from complex systems. Investigating the influence and effects of design factors within computer simulations of complex systems requires assessing variable importance through statistical models. These models are crucial for capturing the relationships between factors and response variables. This study focuses on understanding functional patterns and their magnitudes of influence regarding designed factors affecting cognitive agent decision-making in a cellular automaton-based highway crossing simulation. We aim to identify the most influential design factors in the complex system simulation model to better understand the relationship between the decision outcomes and the designed factors. We apply Generalized Linear Mixed-Effects Models to explain the significant functional connections between designed factors and response variables, specifically quantifying variable importance. Our analysis demonstrates the practicality and effectiveness of the proposed models and methodologies for analyzing data from complex systems. The findings offer a deeper understanding of the connections between design factors and their resulting responses, facilitating a greater understanding of the underlying dynamics and contributing to the fields of applied mathematics, simulation modelling, and computation. Full article
(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications, 2nd Edition)
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43 pages, 1629 KiB  
Article
Decision-Making Model for Life Cycle Management of Aircraft Components
by Igor Kabashkin and Vitaly Susanin
Mathematics 2024, 12(22), 3549; https://doi.org/10.3390/math12223549 - 13 Nov 2024
Viewed by 884
Abstract
This paper presents a novel decision-making framework for the life cycle management of aircraft components, integrating advanced data analytics, artificial intelligence, and predictive maintenance strategies. The proposed model addresses the challenges of balancing safety, reliability, and cost-effectiveness in aircraft maintenance. By using real-time [...] Read more.
This paper presents a novel decision-making framework for the life cycle management of aircraft components, integrating advanced data analytics, artificial intelligence, and predictive maintenance strategies. The proposed model addresses the challenges of balancing safety, reliability, and cost-effectiveness in aircraft maintenance. By using real-time health monitoring systems, failure probability models, and economic analysis, the framework enables more informed and dynamic maintenance strategies. The model incorporates a comprehensive approach that combines reliability assessment, economic analysis, and continuous re-evaluation to optimize maintenance, replacement, and life extension decisions. The optimization method on the base of genetic algorithm (GA) is employed to minimize total life cycle costs while maintaining component reliability within acceptable thresholds. The framework’s effectiveness is demonstrated through case studies on three distinct aircraft components: mechanical, avionics, and engine. These studies showcase the model’s versatility in handling different failure patterns and maintenance requirements. This study introduces a data-driven decision-making framework for optimizing the life cycle management of aircraft components, focusing on reliability, cost-effectiveness, and safety. To achieve optimal maintenance scheduling and resource allocation, a GA is employed, allowing for an effective exploration of complex solution spaces and enabling dynamic decision-making based on real-time data inputs. The GA-based optimization approach minimizes total life cycle costs while maintaining component reliability, with the framework’s effectiveness demonstrated through case studies on key aircraft components. Key findings from the case study demonstrate significant cost reductions through optimization, with mechanical components showing a 10% more reduction in total life cycle costs, avionics components achieving a 14% more cost reduction, and engine components demonstrating a 7% more decrease in total costs. The research also presents an optimized dynamic maintenance schedule that adapts to real-time component health data, extending component lifespans and reducing unexpected failures. The framework effectively addresses key industry challenges such as no fault found events while minimizing unexpected failures and enhancing the overall reliability and safety of aircraft maintenance practices. Sensitivity analysis further demonstrates the model’s robustness, showing stable performance under varying failure rates, maintenance costs, and degradation rates. The study contributes a scalable approach to predictive maintenance, balancing safety, cost, and resource allocation in dynamic operational environments. Full article
(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications, 2nd Edition)
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23 pages, 743 KiB  
Article
Ship Selection and Inspection Scheduling in Inland Waterway Transport
by Xizi Qiao, Ying Yang, King-Wah Pang, Yong Jin and Shuaian Wang
Mathematics 2024, 12(15), 2327; https://doi.org/10.3390/math12152327 - 25 Jul 2024
Viewed by 690
Abstract
Inland waterway transport is considered a critical component of sustainable maritime transportation and is subject to strict legal regulations on fuel quality. However, crew members often prefer cheaper, inferior fuels for economic reasons, making government inspections crucial. To address this issue, we formulate [...] Read more.
Inland waterway transport is considered a critical component of sustainable maritime transportation and is subject to strict legal regulations on fuel quality. However, crew members often prefer cheaper, inferior fuels for economic reasons, making government inspections crucial. To address this issue, we formulate the ship selection and inspection scheduling problem into an integer programming model under a multi-inspector and multi-location scenario, alongside a more compact symmetry-eliminated model. The two models are developed based on ship itinerary information and inspection resources, aiming to maximize the total weight of the inspected ships. Driven by the unique property of the problem, a customized heuristic algorithm is also designed to solve the problem. Numerical experiments are conducted using the ships sailing on the Yangtze River as a case study. The results show that, from the perspective of the computation time, the compact model is 102.07 times faster than the original model. Compared with the optimal objectives value, the gap of the solution provided by our heuristic algorithm is 0.37% on average. Meanwhile, our algorithm is 877.19 times faster than the original model, demonstrating the outstanding performance of the proposed algorithm in solving efficiency. Full article
(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications, 2nd Edition)
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22 pages, 2485 KiB  
Article
Data-Analytics-Driven Selection of Die Material in Multi-Material Co-Extrusion of Ti-Mg Alloys
by Daniel Fernández, Álvaro Rodríguez-Prieto and Ana María Camacho
Mathematics 2024, 12(6), 813; https://doi.org/10.3390/math12060813 - 10 Mar 2024
Viewed by 985
Abstract
The selection of the most suitable material is one of the key decisions to be made during the design stage of a manufacturing process. Traditional approaches, such as Ashby maps based on material properties, are widely used in industry. However, in the production [...] Read more.
The selection of the most suitable material is one of the key decisions to be made during the design stage of a manufacturing process. Traditional approaches, such as Ashby maps based on material properties, are widely used in industry. However, in the production of multi-material components, the criteria for the selection can include antagonistic approaches. The aim of this work is to implement a methodology based on the results of process simulations for several materials and to classify them by applying an advanced data analytics method based on machine learning (ML)—in this case, the support vector regression (SVR) or multi-criteria decision-making (MCDM) methodology. Specifically, the multi-criteria optimization and compromise solution (VIKOR) was combined with entropy weighting methods. To achieve this, a finite element model (FEM) was built to evaluate the extrusion force and the die wear during the multi-material co-extrusion process of bimetallic Ti6Al4V-AZ31B billets. After applying SVR and VIKOR in combination with the entropy weighting methodology, a comparison was established based on material selection and the complexity of the methodology used. The results show that the material chosen in both methodologies is very similar, but the MCDM method is easier to implement because there is no need for evaluating the error of the prediction model, and the time required for data preprocessing is less than the time needed when applying SVR. This new methodology is proven to be effective as an alternative to traditional approaches and is aligned with the new trends in industry based on simulation and data analytics. Full article
(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications, 2nd Edition)
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