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Applied Sciences
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Decision-Making and Decision Support Systems: Methods and Applications
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Decision-Making and Decision Support Systems: Methods and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 3408

Special Issue Editors

Dr. Georgia Dede

E-Mail Website
Guest Editor
Department of Informatics and Telematics, School of Digital Technology, Harokopio University, 17778 Athens, Greece
Interests: decision support systems; evaluation of systems and services; multicriteria analysis; operational research
Prof. Dr. Thomas Kamalakis

E-Mail Website
Guest Editor
Department of Informatics and Telematics, School of Digital Technology, Harokopio University, 17778 Athens, Greece
Interests: system technoeconomics and decision support; optical communications

Special Issue Information

Dear Colleagues,

This Special Issue seeks innovative research on the applications of decision-making (DM) and decision support systems (DSSs) across diverse domains of applied sciences. We request contributions that explore the development, implementation, and evaluation of DM and DSSs to address complex challenges and optimize decision-making processes. Decision-making is a cognitive process involving the selection of a course of action among several alternatives. It encompasses a wide range of approaches, including rational, intuitive, and behavioral models. Decision support systems are critical tools that assist in data-driven decision-making processes, integrating complex data analysis, modeling, and simulation to enhance the efficiency and effectiveness of decisions in diverse applied science fields. This Special Issue requests contributions that explore the theoretical foundations, design, implementation, and practical applications of DM methods and DSSs.

Topics of interest include, but are not limited to, the following:

  • Novel decision-making methodologies and algorithms;
  • Integration of advanced analytics, artificial intelligence and machine learning, and deep learning in decision support;
  • Human-centered design of DSSs;
  • Cognitive science;
  • Multi-criteria decision-making;
  • Evaluation of decision systems;
  • Evaluation of systems and services using decision methods;
  • Operational research and management science;
  • Intelligent systems;
  • Decision-making in risk reduction and incident mitigation;
  • Risk management with DSS;
  • Threat intelligence solutions for the anticipation of systemic risks;
  • Decision using human-explainable AI (XAI);
  • Big data and data analytics;
  • Technoeconomics and decision-making;
  • Decision-making under uncertainty;
  • Real-world applications of DM and DSSs in fields like healthcare, cybersecurity, cyber–physical–human security of critical infrastructures, cloud computing, environment, supply chain management, etc.

This Special Issue aims to foster a comprehensive understanding of the current trends, challenges, and future directions related to decision support systems, ultimately contributing to enhanced decision-making processes in applied sciences.

Dr. Georgia Dede
Prof. Dr. Thomas Kamalakis
Guest Editors

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. Applied Sciences 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 2400 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 systems
  • artificial intelligence
  • uncertain decisions
  • DSS evaluation
  • operational research
  • management science
  • risk management
  • cognitive science

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

13 pages, 963 KiB  
Article
Responsiveness to the Context: Information–Task–Situation Decisional Strategies and Electrophysiological Correlates
by Angelica Daffinà, Carlotta Acconito and Michela Balconi
Appl. Sci. 2025, 15(6), 2941; https://doi.org/10.3390/app15062941 (registering DOI) - 8 Mar 2025
Viewed by 217
Abstract
Decision-making, defined as a cognitive process involving the selection of a course of action among several alternatives, is pivotal in personal and professional life and is founded on responsiveness to the context of decisional strategies—in terms of pieces of contextual features collected, evaluated, [...] Read more.
Decision-making, defined as a cognitive process involving the selection of a course of action among several alternatives, is pivotal in personal and professional life and is founded on responsiveness to the context of decisional strategies—in terms of pieces of contextual features collected, evaluated, and integrated. This study explored the behavioral and electrophysiological (EEG) correlates of individual tendencies to rely on three distinct decisional strategies: Information (I-ds), Situation (S-ds), or Task (T-ds). A total of 51 individuals performed a decision-making task that required participants to face real-life decision-making situations, during which an unexpected event prompted them to appraise the situation and rely on different sources of contextual features to make the best decision and manage the problem. The behavioral data and EEG frequency bands (delta, theta, alpha, beta, and gamma) were collected during the decision-making task. The results evidenced a general predisposition to adopt a T-ds. In addition, EEG findings reported a higher increase in theta band power in the right frontal area (AF8) compared to the left temporoparietal site (TP9). Moreover, for the gamma band, higher activity was found in the T-ds compared to the I-ds in AF8. Overall, responsiveness to the context was closely linked to the assignment’s requirements. Additionally, adopting a T-ds requires high levels of multilevel attention control systems and a significant workload on human performance. Nevertheless, the T-ds remain the most employed type of responsiveness to the context approach, when compared to situational and contextual aspects. Full article
(This article belongs to the Special Issue Decision-Making and Decision Support Systems: Methods and Applications)
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Figure 1

Figure 1
<p>Behavioral results. The bar chart shows significant differences in Strategy, with higher scores in S-ds compared to I-ds and in T-ds compared to I-ds and S-ds. Bars represent ±1 Standard Error and stars (*) mark statistically significant comparisons.</p> Full article ">Figure 2
<p>EEG results: theta band. The bar chart shows significant differences for the theta band in Electrodes, with higher activity in AF8 compared to TP9. Bars represent ±1 Standard Error and stars (*) mark statistically significant comparisons. The more intense color in the rendering of the head (on the right) represents the increase in EEG power at specific EEG electrodes.</p> Full article ">Figure 3
<p>EEG results: gamma band. The bar chart shows significant differences for the gamma band in Strategy × Electrodes, with higher activity in T-ds compared to I-ds in AF8. Bars represent ±1 standard error and stars (*) mark statistically significant comparisons. The more intense color in the rendering of the head (below) represents the increase in EEG power at the specific EEG electrode for each strategy.</p> Full article ">
20 pages, 2691 KiB  
Article
Cloud PricingOps: A Decision Support Framework to Explore Pricing Policies of Cloud Services
by George Fragiadakis, Anargyros Tsadimas, Evangelia Filiopoulou, George Kousiouris, Christos Michalakelis and Mara Nikolaidou
Appl. Sci. 2024, 14(24), 11946; https://doi.org/10.3390/app142411946 - 20 Dec 2024
Viewed by 546
Abstract
To maximize the business value of the cloud, the cost of cloud solutions is explored alongside technical quality and performance. To enable this form of exploration engineering, finance and business teams collaborate in the context of FinOps, the operational framework that provides the [...] Read more.
To maximize the business value of the cloud, the cost of cloud solutions is explored alongside technical quality and performance. To enable this form of exploration engineering, finance and business teams collaborate in the context of FinOps, the operational framework that provides the required decision-making. Prominent providers, such as Google and Microsoft, provide FinOps to their customers, integrating cost factors when designing a cloud solution. However, different providers apply different pricing policies for their products, and these policies also change through time. Therefore, there are numerous efforts to explore price evolution through time for different cloud products applying different decision-making methods using different datasets. In an effort to establish a systematic approach to support decision-making on alternative pricing policies for cloud services and compare them across services and providers, the CloudPricingOps framework is proposed in this paper. It constitutes a decision support system that provides alternative decision-making methods, such as hedonic models, time series, and clustering and machine learning techniques, to deal with problems related to cloud product pricing policy analysis, comparison and prediction. It also constitutes a systematic method of discrete steps to integrate additional decision-making methods to deal with these problems and input datasets to be used regardless of the method or the cloud pricing problem that needs to be solved. Two discrete examples based on real data are also presented in this paper to demonstrate the usefulness of the CloudPricingOps framework for cloud engineering and business teams. Full article
(This article belongs to the Special Issue Decision-Making and Decision Support Systems: Methods and Applications)
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<p>CloudPricingOpS framework— conceptual view.</p> Full article ">Figure 2
<p>CloudPricingOps framework—system view.</p> Full article ">Figure 3
<p>CloudPricingOps framework’s provided functionality—use case diagram.</p> Full article ">Figure 4
<p>CloudPricingOps data ingestion activity diagram.</p> Full article ">Figure 5
<p>Integrate DM method’s activity diagram.</p> Full article ">Figure 6
<p>Comparison of coefficients across IaaS, CaaS, and PaaS datasets.</p> Full article ">Figure 7
<p>Clustering categories based on CPU-RAM and storage capacity with average costs.</p> Full article ">Figure 8
<p>Application resource requirements compared across services.</p> Full article ">
26 pages, 994 KiB  
Article
A Causal Inference Methodology to Support Research on Osteopenia for Breast Cancer Patients
by Niki Kiriakidou, Aristotelis Ballas, Cristina Meliá Hernando, Anna Miralles, Teta Stamati, Dimosthenis Anagnostopoulos and Christos Diou
Appl. Sci. 2024, 14(21), 9700; https://doi.org/10.3390/app14219700 - 24 Oct 2024
Viewed by 953
Abstract
Breast cancer is the most common cancer in the world. With a 5-year survival rate of over 90% for patients at the early disease stages, the management of side-effects of breast cancer treatment has become a pressing issue. Observational, real-world data such as [...] Read more.
Breast cancer is the most common cancer in the world. With a 5-year survival rate of over 90% for patients at the early disease stages, the management of side-effects of breast cancer treatment has become a pressing issue. Observational, real-world data such as electronic health records, insurance claims, or data from wearable devices have the potential to support research on the quality of life (QoL) of breast cancer patients (BCPs), but care must be taken to avoid errors introduced due to data quality and bias. This paper proposes a causal inference methodology for using observational data to support research on the QoL of BCPs, focusing on the osteopenia of patients undergoing treatment with aromatase inhibitors (AIs). We propose a machine learning-based pipeline to estimate the average and conditional average treatment effects (ATE and CATE). For evaluation, we develop a Structural Causal Model for the osteopenia of BCPs and rely on synthetically generated data to study the effectiveness of the proposed methodology under various data challenges. A set of studies were designed to estimate the effect of high-intensity exercise on bone mineral density loss using synthetic datasets of BCPs under AI treatment. Four observational study scenarios were evaluated, corresponding to synthetically generated data of 1000 BCPs with (a) no bias, (b) sampling bias, (c) hidden confounder bias, and (d) bias due to unobserved mediator. In all cases, evaluations were performed under both complete and missing data scenarios. In particular, machine learning-based models based on tree ensembles and neural networks achieved a lower estimation error by 23.8–51.3% and 32.4–89.3% for ATE and CATE, respectively, compared to direct estimation using sample averages. The proposed approach shows improved effectiveness in treatment effect estimation in the presence of missing values and sampling bias, compared to a “traditional” statistical analysis workflow. This suggests that the application of causal effect estimation methods for the study of BCPs’ quality of life using real-world data is promising and worth pursuing further. Full article
(This article belongs to the Special Issue Decision-Making and Decision Support Systems: Methods and Applications)
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Figure 1

Figure 1
<p>REBECCA data analysis workflow.</p> Full article ">Figure 2
<p>Causal DAG for osteopenia/osteoporosis. A causal directed acyclic graph (DAG) is a graphical tool used for visually representing the causal connections between a set of variables. In our case, the set of variables are relevant to the clinical study for osteopenia/osteoporosis, as a result of treating breast cancer with aromatase inhibitors, in the context of the REBECCA project.</p> Full article ">
13 pages, 317 KiB  
Article
Knapsack Balancing via Multiobjectivization
by Ignacy Kaliszewski and Janusz Miroforidis
Appl. Sci. 2024, 14(20), 9236; https://doi.org/10.3390/app14209236 - 11 Oct 2024
Viewed by 694
Abstract
In this paper, we address the aspect of knapsack balancing in the classic knapsack problem. Recognizing that excessive dispersion in the objective function or constraint coefficients of the optimal solution can be undesirable, we propose, when appropriate, to control this effect through problem [...] Read more.
In this paper, we address the aspect of knapsack balancing in the classic knapsack problem. Recognizing that excessive dispersion in the objective function or constraint coefficients of the optimal solution can be undesirable, we propose, when appropriate, to control this effect through problem multiobjectivization. By multiobjectivization, we mean the addition of one or more objective functions that aim to shift the original problem’s optimal solutions towards Pareto optimal solutions of the multiobjectivized problem, reducing the dispersion of the respective coefficients. We detail how the knapsack balance aspect can be incorporated into the standard knapsack problem model and demonstrate the functionality of this enriched model through illustrative examples. Full article
(This article belongs to the Special Issue Decision-Making and Decision Support Systems: Methods and Applications)
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Figure 1

Figure 1
<p>Properties of functions <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>1</mn> </msub> <mo>·</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 2
<p>Pareto optimal outcomes to problem <math display="inline"><semantics> <mrow> <mi mathvariant="script">L</mi> <mi>n</mi> <mrow> <mtext>-</mtext> </mrow> <mi mathvariant="script">S</mi> <mi>u</mi> <mi>m</mi> <mrow> <mtext>-</mtext> </mrow> <mi mathvariant="script">P</mi> <mi>r</mi> <mi>o</mi> <msup> <mi>d</mi> <mi>p</mi> </msup> <mspace width="4pt"/> <mi>KP</mi> </mrow> </semantics></math> stemming from <math display="inline"><semantics> <mrow> <mi>S</mi> <msubsup> <mi>P</mi> <mn>1</mn> <mi>p</mi> </msubsup> </mrow> </semantics></math>; horizontal axis: <math display="inline"><semantics> <mrow> <mi mathvariant="script">S</mi> <mi>u</mi> <mi>m</mi> </mrow> </semantics></math>, vertical axis: <math display="inline"><semantics> <mover accent="true"> <mrow> <mi mathvariant="script">P</mi> <mi>r</mi> <mi>o</mi> <msup> <mi>d</mi> <mi>p</mi> </msup> </mrow> <mo>˜</mo> </mover> </semantics></math>.</p> Full article ">Figure 3
<p>Pareto optimal outcomes to problem <math display="inline"><semantics> <mrow> <mi mathvariant="script">L</mi> <mi>n</mi> <mrow> <mtext>-</mtext> </mrow> <mi mathvariant="script">S</mi> <mi>u</mi> <mi>m</mi> <mrow> <mtext>-</mtext> </mrow> <mi mathvariant="script">P</mi> <mi>r</mi> <mi>o</mi> <msup> <mi>d</mi> <mi>p</mi> </msup> <mspace width="4pt"/> <mi>KP</mi> </mrow> </semantics></math> stemming from <math display="inline"><semantics> <mrow> <mi>S</mi> <msubsup> <mi>P</mi> <mn>1</mn> <mi>p</mi> </msubsup> </mrow> </semantics></math>; horizontal axis: <math display="inline"><semantics> <mrow> <mi mathvariant="script">S</mi> <mi>u</mi> <mi>m</mi> </mrow> </semantics></math>, vertical axis: <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>2</mn> </msub> <mrow> <mtext>-</mtext> <mi mathvariant="italic">DEV</mi> </mrow> </mrow> </semantics></math>.</p> Full article ">
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