Mathematics

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13 pages, 624 KiB

Open AccessFeature PaperArticle

Decision Uncertainty from Strict Preferences in Sequential Search Scenarios with Multiple Criteria

by Debora Di Caprio, Yolanda Durán Durán and Francisco Javier Santos-Arteaga

Mathematics 2025, 13(9), 1368; https://doi.org/10.3390/math13091368 - 22 Apr 2025

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The standard expected utility model applied by economists and decision scientists assumes both that decision makers (DMs) are rational and that their information retrieval behavior and choices are determined by the observed and potential values of the multiple characteristics defining the alternatives. In [...] Read more.

The standard expected utility model applied by economists and decision scientists assumes both that decision makers (DMs) are rational and that their information retrieval behavior and choices are determined by the observed and potential values of the multiple characteristics defining the alternatives. In this regard, if DMs can formalize the information acquisition structures determined by the main postulates of expected utility theory, they should also be able to perform standard operations regarding the potential combinatorial outcomes that may be obtained when evaluating the alternatives. We define an information retrieval scenario where DMs account for the different combinatorial possibilities arising among the realizations of the characteristics defining the alternatives before evaluating them. We demonstrate the indifference that arises among risk-neutral DMs endowed with standard expected utilities within sequential information acquisition environments such as those defined by online search engines. We also illustrate the reticence of DMs to acquire information on new alternatives when increasing their aversion to risk or modifying the relative importance assigned to the different characteristics defining the alternatives. The main strategic consequences that follow from the enhanced information retrieval scenario proposed are also analyzed. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications, 2nd Edition)

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28 pages, 627 KiB

Open AccessFeature PaperArticle

Forecasting the Number of Passengers on Hungarian Railway Routes Using a Similarity and Fuzzy Arithmetic-Based Inference Method

by Marcell Fetter and Tamás Jónás

Mathematics 2025, 13(8), 1221; https://doi.org/10.3390/math13081221 - 8 Apr 2025

Viewed by 164

Abstract

In this study, we present a similarity and fuzzy arithmetic-based fuzzy inference method and show how effectively it can be used to forecast the number of passengers on a railway route. We introduce a novel fuzzy similarity measure that is derived from the [...] Read more.

In this study, we present a similarity and fuzzy arithmetic-based fuzzy inference method and show how effectively it can be used to forecast the number of passengers on a railway route. We introduce a novel fuzzy similarity measure that is derived from the so-called epsilon function, which may be viewed as an alternative to the exponential function. After demonstrating the most important properties of the new similarity measure, we construct a fuzzy inference method that is founded on arithmetic operations over triangular fuzzy numbers. This inference method utilizes the proposed similarity measure to derive weight values for the above-mentioned arithmetic operations. The motivation behind the proposed method is twofold. On the one hand, we aim to construct a method that is simple and easy to implement. On the other hand, we intend to ensure that this method meets the practical requirements for rail passenger forecasts. Using a real-life case study, we demonstrate how well our method can predict the expected number of passengers on a new railway route based on characteristics of this relation. With respect to the studied case, we may conclude that although the similarity and fuzzy arithmetic-based fuzzy inference system has only two adjustable parameters, it may be regarded as a viable alternative to Sugeno-type fuzzy inference systems with a much greater number of adjustable parameters tuned by various optimization techniques. 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, 5260 KiB

Open AccessArticle

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 536

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)

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23 pages, 1624 KiB

Open AccessArticle

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

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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

Open AccessArticle

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

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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

Open AccessArticle

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

Cited by 1 | Viewed by 1271

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

Open AccessEditor’s ChoiceArticle

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 818

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

Open AccessArticle

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 1086

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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