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Decision-Making and Decision Support Systems: Methods and Applications: 2nd Edition

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 November 2026 | Viewed by 582

Editors


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Guest Editor
Department of Informatics & Telematics, Harokopio University, Omirou 9, 17778 Tavros, Greece
Interests: decision support systems; evaluation of systems and services; multicriteria analysis; operational research
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Informatics and Telematics, Harokopio University of Athens, 17671 Athens, Greece
Interests: system technoeconomics and decision support; optical communications
Special Issues, Collections and Topics in MDPI journals

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 simulations 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;
  • Decisions 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 that are 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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 (1 paper)

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Research

27 pages, 3658 KB  
Article
An Integrated INF-DEMATEL-MABAC Framework for Enhanced FMEA: Prioritizing Scaffold-Related Fall Risks in Demolition Projects
by Chi-Tung Lai and Sheau-Farn Max Liang
Appl. Sci. 2026, 16(11), 5400; https://doi.org/10.3390/app16115400 - 28 May 2026
Viewed by 300
Abstract
Scaffold-related falls remain a major safety concern in demolition projects, where temporary access systems are frequently erected, modified, used, and dismantled under changing structural and site conditions. These characteristics complicate risk prioritization because scaffold failures may involve interacting human, technical, organizational, and environmental [...] Read more.
Scaffold-related falls remain a major safety concern in demolition projects, where temporary access systems are frequently erected, modified, used, and dismantled under changing structural and site conditions. These characteristics complicate risk prioritization because scaffold failures may involve interacting human, technical, organizational, and environmental factors. This study develops an expert-based risk prioritization framework for scaffold-related fall risks in demolition projects by integrating Failure Mode and Effects Analysis (FMEA), interval neutrosophic fuzzy (INF) theory, Decision-Making Trial and Evaluation Laboratory (DEMATEL), and Multi-Attributive Border Approximation Area Comparison (MABAC). Using the 4M1E perspective, namely Man, Machine, Material, Method, and Environment, 37 demolition-specific failure modes were identified through literature review and expert elicitation. Ten experts evaluated these failure modes using the SODE criteria, namely Severity, Occurrence, Detection difficulty, and Expected Cost impact. INF theory was used to represent uncertainty, hesitation, and judgmental variation in expert assessments. INF-DEMATEL was applied to examine interrelationships among the SODE criteria and derive interdependence-aware criterion weights, while INF-MABAC was used to rank the failure modes according to their distance from the Border Approximation Area. The framework was illustrated through an empirical application in Taiwan’s demolition industry. The results identified Severity as the most influential criterion. The highest-priority failure modes were insufficient safety awareness, improper scaffold-to-structure anchoring, and inadequate scaffold maintenance and inspection governance. Comparison with risk priority number (RPN)-based methods and sensitivity analyses using expert exclusion and Severity-weight variation showed that the ranking was generally consistent and reasonably stable under the tested conditions. The proposed framework provides a structured, uncertainty-aware decision-support procedure for identifying prevention priorities in demolition scaffold operations. Full article
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