Search Results (332)

This paper aims to study a distribution decision support system (DSS) conceptual framework for textile logistics, combining the operational requirements of logistics enterprises in textile markets to optimize vehicle surplus tonnage usage and distribution flexibility, using the integrated computer-aided manufacturing definition (IDEF) method and developing a comprehensive conceptual framework for textile logistics distribution decisions, complemented by an in-depth analysis of its underlying database structure. Further, this paper constructs the model base and proposes two vehicle-loading models and their solving algorithms, including one model with constraints on the maximum loading rate and the other with constraints on the smallest vehicle numbers, with these algorithms implemented by linear programming in operational research and performed by programming techniques. This paper also constructs the method base and designs some methods, such as the method of vehicle surplus tonnage utilization, the method of vehicle-loading priority order selection, and the simultaneous loading method of multi-freight cargo and multiple vehicles; these methods are implemented by the database principle and technological or programming techniques. We use a test distribution DSS conceptual framework to run the data example and obtain a good test result. The findings indicate that the DSS conceptual framework can integrate the model and method bases and can also solve the hard problems of the use of surplus tonnage vehicles and simultaneous loading. Full article

A decision support system (DSS) is a software application designed to determine suitable actions for specific organizational situations. Its main component is a data repository analyzed to produce decisions. This paper describes the data organization (Data Model) as a multi-dimensional OLAP cube with amendments for decision-making support. We present DSS functionality as building (slicing) hyper-cubes into decision sub-cubes. The system’s adjustment and evolution involve changing the granularity of these sub-cubes. We discuss the merging and splitting of hyper-cubes, arguing that this functionality is adequate for creating complex, real-time DSSs for various incidents, such as cybersecurity incidents. Full article

The material selection process can be viewed as a multi-criteria decision-making (MCDM) problem with multiple objectives, which are often conflicting and of different importance. The selection of the most suitable biomaterial is considered as a very complex, important, and responsible task that is influenced by many factors. In this paper, a procedure for biomaterial selection based on MCDM is proposed by using a developed decision support system (DSS) named MCDM Solver. Within the framework of the developed DSS, the complete procedure for selecting the criteria weights was developed. Also, in addition to the adapted standard MCDM methods such as TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and VIKOR (VIšekriterijumsko KOmpromisno Rangiranje), an extended WASPAS (Weighted Aggregated Sum Product Assessment) method was developed, enabling its application for considering target-based criteria in solving biomaterial selection problems. The proposed MCDM Solver enables a structured decision-making process helping decision-makers rank biomaterials with respect to multiple conflicting criteria and make rational and justifiable decisions. For the validation of the developed DSS, two case studies, i.e., the selection of a plate for internal bone fixation and a hip prosthesis, were presented. Finally, lists of potential biomaterials (alternatives) in the considered case studies were ranked based on the selected criteria, where the best-ranked one presents the most suitable choice for the specific biomedical application. Full article

Despite navigation advancements in enhanced sensor utilization and increased focus on maritime training and education, most marine accidents still involve collisions with high human involvement. Furthermore, navigators’ knowledge and application of the most often misunderstood Rule 10 Traffic Separation Schemes (TSS) according to the Convention on the International Regulations for Preventing Collisions at Sea (COLREG) represents the first focus in this study. To provide insight into the level of understanding and knowledge regarding COLREG Rule 10, a customized, worldwide survey has been created and disseminated among marine industry professionals. The survey results reveal a notable knowledge gap in Rule 10, where we initially assumed that more than half of the respondents know COLREG regulations well. According to the probability calculation and chi-square test results, all three categories (OOW, Master, and others) have significant rule misunderstanding. In response to the COLREG misunderstanding, together with the increasing density of maritime traffic, the implementation of Decision Support Systems (DSS) in navigation has become crucial for ensuring compliance with regulatory frameworks and enhancing navigational safety in general. This study presents a structural approach to vessel prioritization and decision-making within a DSS framework, focusing on the classification and response of the own vessel (OV) to bow-crossing scenarios within the TSS. Through the real-time integration of AIS navigational status data, the proposed DSS Architecture offers a structured, rule-compliant architecture to enhance navigational safety and the decision-making process within the TSS. Furthermore, implementing a Fall-Back Strategy (FBS) represents the key innovation factor, which ensures system resilience by directing operator response if opposing vessels disobey COLREG rules. Based on the vessel’s dynamic context and COLREG hierarchy, the proposed DSS Architecture identifies and informs the navigator regarding stand-on or give-way obligations among vessels. Full article

Wildfires are becoming an increasingly severe threat to European forests, driven by climate change, land use changes, and socio-economic factors. Integrated solutions for wildfire prevention, early detection, emergency management, and ecological restoration are urgently needed to enhance forest resilience. The Horizon 2020 SILVANUS project developed a comprehensive multi-sectoral platform combining technological innovation, stakeholder engagement, and sustainable forest management strategies. This report analyses the Slovak Republic’s participation in SILVANUS, applying a seven-criterion fit–gap framework (governance, legal, interoperability, staff capacity, ecological suitability, financial feasibility, and stakeholder acceptance) to evaluate the platform’s alignment with national conditions. Notable contributions include stakeholder-supported functional requirements for wildfire prevention, climate-sensitive forest models for long-term adaptation planning, IoT- and UAV-based early fire detection technologies, and decision support systems (DSS) for emergency response and forest-restoration activities. The Slovak pilot sites, particularly in the Podpoľanie region, served as important testbeds for the validation of these tools under real-world conditions. All SILVANUS modules scored ≥12/14 in the fit–gap assessment; early deployment reduced high-risk fuel polygons by 23%, increased stand-level structural diversity by 12%, and raised the national Sustainable Forest Management index by four points. Integrating SILVANUS outcomes into national forestry practices would enable better wildfire risk assessment, improved resilience planning, and more effective public engagement in wildfire management. Opportunities for adoption include capacity-building initiatives, technological deployments in fire-prone areas, and the incorporation of DSS outputs into strategic forest planning. Potential challenges, such as technological investment costs, inter-agency coordination, and public acceptance, are also discussed. Overall, the Slovak Republic’s engagement with SILVANUS demonstrates the value of participatory, technology-driven approaches to sustainable wildfire management and offers a replicable model for other European regions facing similar challenges. Full article

Accurately predicting the success of startup financing is critical for strategic business planning and informed investor decision-making. Traditional financing prediction models typically focus on a company’s financial indicators to explore the impact of factors such as resource allocation and strategic choices on financing success, yet they often overlook the important role of social media as an external source of information in influencing financing performance. To address this gap, this paper focuses on the role of social media sentiment in predicting startup financing success and proposes a decision support system (DSS) framework that integrates multi-source data. Specifically, this study combines financial data from the Crunchbase platform with company-related social media news data from Twitter. The BERTweet model is used to perform sentiment analysis on the social media texts, extracting sentiment features such as polarity and intensity to capture public attitudes and expectations toward the company. Subsequently, financial indicators, social media numerical features, and sentiment features are combined to construct a decision support system for predicting financing success using a deep neural network (DNN). Experimental results show that the decision support system incorporating social media data significantly outperforms traditional decision support systems in prediction accuracy, with sentiment features further enhancing the model’s ability to identify a company’s financing performance. Our study provides strong support for understanding the profound influence of public sentiment, offering practical guidance for startups to optimize financing strategies and for investors to make informed decisions. Full article

Iraq has faced escalating water scarcity over the past two decades, driven by climate change, upstream water withdrawals, and prolonged economic instability. These factors have caused deterioration in irrigation systems, inefficient water distribution, and growing social unrest. As per capita water availability falls below critical levels, Iraq is entering a period of acute water stress. This escalating water scarcity directly impacts water and food security, public health, and economic stability. This study aims to develop a general framework combining decision support systems (DSSs) with Integrated Comprehensive Water Management Strategies (ICWMSs) to support water planning, allocation, and response to ongoing water scarcity and reductions in Iraq. Implementing such a system is essential for Iraq to alleviate its continuing severe situation and adequately tackle its worsening water scarcity that has intensified over the years. This integrated approach is fundamental for enhancing planning efficiency, improving operational performance and monitoring, optimizing water allocation, and guiding informed policy decisions under scarcity and uncertainty. The current study highlights various international case studies that show that DSSs integrate real-time data, artificial intelligence, and advanced modeling to provide actionable policies for water management. Implementing such a framework is crucial for Iraq to mitigate this critical situation and effectively address the escalating water scarcity. Furthermore, Iraq’s water management system requires modifications considering present and expected future challenges. This study analyzes the inflows of the Tigris and Euphrates rivers from 1933 to 2022, revealing significant reductions in water flow: a 31% decrease in the Tigris and a 49.5% decline in the Euphrates by 2021. This study highlights the future 7–20% water deficit between 2020 and 2035. Furthermore, this study introduces a flexible, tool-based framework supported by a DSS with the DPSIR model (Driving Forces, Pressures, State, Impacts, and Responses) designed to address and reduce the gap between water availability and increasing demand. This approach proposes a multi-hazard risk matrix to identify and prioritize strategic risks facing Iraq’s water sector. This matrix links each hazard with appropriate DSS-based response measures and supports scenario planning under the ICWMS framework. The proposed framework integrates hydro-meteorological data analysis with hydrological simulation models and long-term investment strategies. It also emphasizes the development of institutional frameworks, the promotion of water diplomacy, and the establishment of transboundary water allocation and operational policy agreements. Efforts to enhance national security and regional stability among riparian countries complement these actions to tackle water scarcity effectively. Simultaneously, this framework offers a practical guideline for water managers to adopt the best management policies without bias or discrimination between stakeholders. By addressing the combined impacts of anthropogenic and climate change, the proposed framework aims to ensure rational water allocation, enhance resilience, and secure Iraq’s water strategies, ensuring sustainability for future generations. Full article

This paper proposes Linguistic Intuitionistic Fuzzy (LIF) aggregation operators, LIF-energies, LIF-correlation, and LIF-correlation coefficients. Supporting theorems are also proven for the proposed functions, which are utilized in the Linguistic Intuitionistic Fuzzy–Vlse Kriterijumska Optimizacija Kompromisno Resenje (LIF-VIKOR) method within Decision Support Systems (DSS). Additionally, numerical examples are presented to validate the method. The sensitivity analysis of weighting vectors is conducted, and the consistency of final rankings affirms the robustness of the proposed approaches. Arithmetic operations, specifically subtraction and division, are applied to LIF numbers (LIFNs) within the LIF-VIKOR algorithm. Furthermore, a function called the Linguistic Median Membership (LMM) function is introduced to convert LIFN values into crisp numbers. In the LIF-VIKOR algorithm, the proposed correlation coefficient is used for ranking alternatives, while the entropy method is applied to compute weights. Sensitivity analysis is performed to ensure the consistency of the proposed method. Finally, an Artificial Neural Network (ANN) is integrated into the VIKOR algorithm to enhance computational efficiency, reducing the time and manpower required to solve the model. Full article

This study introduces a comprehensive framework for assessing and enhancing the quality of hourly temperature data collected from a six-station agrometeorological network in the Arta plain, Epirus, Greece, spanning the period 2015–2023. By combining traditional quality control (QC) techniques with advanced methods—Dynamic Time Warping (DTW), Fuzzy Logic, and XGBoost machine learning—the framework effectively identifies anomalies and reconstructs missing or erroneous temperature values. The DTW–Fuzzy Logic approach reliably detected spatial inconsistencies, while the machine learning reconstruction achieved low root mean squared error (RMSE) values (0.40–0.66 °C), ensuring the high fidelity of the corrected dataset. A Data Quality Index (DQI) was developed to quantify improvements in both completeness and accuracy, providing a transparent and standardized metric for end users. The enhanced temperature data significantly improve the reliability of inputs for applications such as evapotranspiration (ET) estimation and agricultural decision support systems (DSS). Designed to be scalable and automated, the proposed framework ensures robust Internal Consistency across the network—even when stations are intermittently offline—yielding direct benefits for irrigation water management, as well as broader agrometeorological applications. Full article

The present paper describes a novel and user-friendly Decision Support System (e-DSS) designed to assist technicians in the preliminary design stage of a building renovation process based on the solutions developed in the innovation project e-SAFE, funded by the EU under the H2020 program. The e-DSS is engineered to rapidly assess key performance indicators, including energy performance before and after renovation, reduction in CO₂ emission for space heating, space cooling, and DHW preparation, seismic upgrade feasibility, expected costs, and payback time. To demonstrate its capabilities, the e-DSS was applied to an existing public housing building in Catania, southern Italy. The predicted thermal energy needs for space heating and cooling were compared to the results from detailed simulations using a professional-grade software tool, for both as-built condition and a proposed renovation generated by the e-DSS itself. The discrepancies identified through this comparison will inform the refinement of the e-DSS algorithms to increase their accuracy and reliability. More generally, this paper recommends suitable algorithms that can be effectively employed in the development of simplified decision-making tools specifically tailored for building professionals operating in the early phase of building renovation projects. Full article

The container loading problem (CLP) has a broad spectrum of applications in industry and has been studied for over 60 years due to its high complexity. This paper addresses a realistic single-container loading scenario with practical constraints, including orientation limitations, maximum stacking weight, static stability, overall container weight limit, and fractional loading for multiple drop-off points (multidrop). We propose an open-source decision support system (DSS) implemented on a widely used platform (MS Excel®), which employs a heuristic algorithm to find efficient loading solutions under these constraints. The DSS uses a multi-start randomized constructive algorithm based on a maximal residual space representation. The constructive phase builds the loading pattern in vertical layers (columns or walls), while respecting all practical constraints. The performance of the proposed heuristic is validated through extensive computational experiments on classical benchmark instances, comparing its results against the recent state-of-the-art methods. We also analyze the impact of multi-drop constraints on utilization metrics. The DSS features an interactive interface for creating/loading instances, visualizing step-by-step packing patterns, and displaying key statistics, thus providing a user-friendly decision tool for practitioners. Full article

Artificial neural networks (ANNs) are increasingly effective in addressing complex scientific and technological challenges. However, challenges persist in synthesizing neural network models and defining their structural parameters. This study investigates the use of parallel evolutionary algorithms on distributed computing systems (DCSs) to optimize energy consumption and computational time. New mathematical models for DCS performance and reliability are proposed, based on a mass service system framework, along with a multi-criteria optimization model designed for resource-intensive computational problems. This model employs a multi-criteria GA to generate a diverse set of Pareto-optimal solutions. Additionally, a decision-support system is developed, incorporating the multi-criteria GA, allowing for customization of the genetic algorithm (GA) and the construction of specialized ANNs for specific problem domains. The application of the decision-support system (DSS) demonstrated performance of 1220.745 TFLOPS and an availability factor of 99.03%. These findings highlight the potential of the proposed DCS framework to enhance computational efficiency in relevant applications. Full article

Transport networks are crucial for economic growth, yet their sustainability is increasingly threatened by natural hazards. Recent floods in Italy have highlighted the vulnerability of rail and road infrastructure, causing severe damage and economic losses. The Gargano Promontory in northern Apulia has experienced frequent hydrogeological disruptions over the past decade, significantly affecting bridges and the railway network managed by Ferrovie del Gargano s.r.l. (FdG). However, structural interventions are complex, time-consuming, costly, and involve certain risks. To enhance sustainability and comply with railway safety regulations, FdG has adopted non-structural measures to improve hydrogeological risk classification and management. Despite the prevalence of flood events, the existing literature often overlooks crucial technical aspects, which this study addresses. The HYD.RAIL (HYDraulic Risk Assessment for Infrastructure and Lane) research project aims to improve transport infrastructure resilience by refining hydraulic risk assessments and introducing new classification parameters. HYD.RAIL employs a multicriteria approach, integrating parameters defined in collaboration with railway professionals. This paper presents the initial framework, offering a methodology to identify, classify, and manage hydrogeological risks in transportation infrastructure. Compared to standard methods, which lack detailed risk classification, HYD.RAIL enables more precise flood risk mapping. For example, high-risk points were reduced from 37 to 6 locations on Line 1 and from 134 to 50 on Line 2 using HYD.RAIL. This approach enhances flood risk management efficiency, providing railway operators with a more accurate understanding of infrastructure vulnerabilities. Full article

Effective and sustainable water reserve management faces increasing challenges due to climate-induced variability, data fragmentation, and the limitations of traditional, static modeling systems. This study introduces a conceptual framework designed to address these challenges by integrating digital twins, IoT-driven real-time monitoring, game engine simulations, and AI-driven decision support systems (AI-DSS). The methodology involves constructing a digital twin ecosystem using IoT sensors, GIS layers, remote-sensing imagery, and game engines. This ecosystem simulates water dynamics and assesses policy interventions in real time. AI components, including machine-learning models and retrieval-augmented generation (RAG) chatbots, are embedded to synthesize real-time data into actionable insights. The framework enables the continuous assessment of hydrological dynamics, predictive risk analysis, and immersive, scenario-based decision-making to support long-term water sustainability. Simulated scenarios demonstrate accurate flood forecasting under variable rainfall intensities, early drought detection based on soil moisture and flow data, and real-time water-quality alerts. Digital elevation models from UAV photogrammetry enhance terrain realism, and AI models support dynamic predictions. Results show how the framework supports proactive mitigation planning, climate adaptation, and stakeholder communication in pursuit of resilient and sustainable water governance. By enabling early intervention, efficient resource allocation, and participatory decision-making, the proposed system fosters long-term, sustainable water security and environmental resilience. This conceptual framework suggests a pathway toward more transparent, data-informed, and resilient decision-making processes in water reserves management, particularly in regions facing climatic uncertainty and infrastructure limitations, aligning with global sustainability goals and adaptive water governance strategies. Full article

This study examines the key obstacles that hinder the mass adoption of additive manufacturing (AM) processes for fabrication and processing of metal parts. To address these challenges, the necessity of integrating an intelligent decision support system (DSS) into the workflow of AM process engineers is demonstrated. The advantages of applying a two-level ontological approach to the creation of semantic information to develop an ontology-based DSS are pointed out. A key feature of this approach is that the ontological models are clearly separated from data and knowledge bases formed on this basis. An ensemble of ontological models is presented, which is the basis for the intelligent DSS being developed. The ensemble includes ontologies for equipment and materials reference databases, a library of laser processing technological operation protocols, knowledge base of settings used for laser processing and for mathematical model database. The ensemble of ontological models is implemented via the IACPaaS cloud platform. Ontologies, databases and knowledge base, as well as DSS, are part of the laser-based AM knowledge portal, which was created and is being developed on the platform. Knowledge and experience obtained by various technologists and accumulated within the portal will allow one to lessen a number of extensive trial-and-error experiments to find suitable processing settings. In the long term, the deployment of this portal is expected to reduce the qualification requirements for AM process engineers. Full article