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Decision-Making and Decision Support Systems: Methods and Applications

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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 March 2026 | Viewed by 7360

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Special Issue Editors

Dr. Georgia Dede

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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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Published Papers (6 papers)

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Research

16 pages, 1093 KB

Open AccessArticle

Supporting Equipment Allocation for Multiple Projects in ERP Systems—Functionality Extension in IFS Applications

by Mateusz Fijas, Katarzyna Grobler-Dębska and Edyta Kucharska

Appl. Sci. 2025, 15(17), 9801; https://doi.org/10.3390/app15179801 - 6 Sep 2025

Viewed by 437

Abstract

Many organizations execute multiple projects simultaneously, competing for limited resources, including specialized and expensive equipment. Managing such multi-project environments requires advanced planning and decision-making. An additional difficulty is taking into account the possibility and profitability of using internal and external resources. The construction industry is a particularly demanding example of this scenario, where simultaneously executed projects must share high-value equipment with limited availability. Project management planning with resource allocation is supported by various types of IT tools. ERP (enterprise resource planning) systems are particularly useful in this regard, as they use the organization’s transaction data directly, but only offer basic project support. Therefore, it is necessary to extend their functionality in order to fulfill the expected functional requirements of business users, with particular emphasis on the provision of a consistent, graphically supported interface. This article proposes an algorithm to support decision-making on equipment allocation in a multi-project environment, taking into account the use of own and third-party equipment. A case study is presented demonstrating the practical implementation of the proposed solution in the IFS Applications ERP system. The developed extension supports users through graphical and numerical presentation of machine workloads across multiple projects. Full article

(This article belongs to the Special Issue Decision-Making and Decision Support Systems: Methods and Applications)

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27 pages, 4588 KB

Open AccessArticle

Remote Sensing as a Sentinel for Safeguarding European Critical Infrastructure in the Face of Natural Disasters

by Miguel A. Belenguer-Plomer, Omar Barrilero, Paula Saameño, Inês Mendes, Michele Lazzarini, Sergio Albani, Naji El Beyrouthy, Mario Al Sayah, Nathan Rueche, Abla Mimi Edjossan-Sossou, Tommaso Monopoli, Edoardo Arnaudo and Gianfranco Caputo

Appl. Sci. 2025, 15(16), 8908; https://doi.org/10.3390/app15168908 - 13 Aug 2025

Viewed by 521

Abstract

Critical infrastructure, such as transport networks, energy facilities, and urban installations, is increasingly vulnerable to natural hazards and climate change. Remote sensing technologies, namely satellite imagery, offer solutions for monitoring, evaluating, and enhancing the resilience of these vital assets. This paper explores how applications based on synthetic aperture radar (SAR) and optical satellite imagery contribute to the protection of critical infrastructure by enabling near real-time monitoring and early detection of natural hazards for actionable insights across various European critical infrastructure sectors. Case studies demonstrate the integration of remote sensing data into geographic information systems (GISs) for promoting situational awareness, risk assessment, and predictive modeling of natural disasters. These include floods, landslides, wildfires, and earthquakes. Accordingly, this study underlines the role of remote sensing in supporting long-term infrastructure planning and climate adaptation strategies. The presented work supports the goals of the European Union (EU-HORIZON)-sponsored ATLANTIS project, which focuses on strengthening the resilience of critical EU infrastructures by providing authorities and civil protection services with effective tools for managing natural hazards. Full article

(This article belongs to the Special Issue Decision-Making and Decision Support Systems: Methods and Applications)

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13 pages, 963 KB

Open AccessArticle

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 - 8 Mar 2025

Viewed by 940

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, 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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20 pages, 2691 KB

Open AccessArticle

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 1172

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 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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26 pages, 994 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1461

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 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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13 pages, 317 KB

Open AccessArticle

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

Cited by 1 | Viewed by 896

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 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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