Search Results (165)

This research determines how to integrate factors related to evacuation in emergency preparedness using techniques for Multicriteria Decision-Making (MCDM). A distinctive MCDM technique that incorporates human behavior into evacuation models enhances decision-making and safety during emergencies, especially in vulnerable populations. For this purpose, a hybrid combination of MCDM methods—CRiteria Importance Through Intercriteria Correlation (CRITIC), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Weighted Aggregated Sum Product Assessment (WASPAS)—is used to rank the vulnerability of Chilean regions by considering various factors. First, the related factors are ranked by CRITIC, and the result is that the “psychosocial problem” factor has the highest priority and weight. Then, according to the hybrid methods and CRITIC, all regions of Chile are ranked first with TOPSIS, WASPAS, and a combination of them to determine which one has the highest priority. The results show that the Santiago Metropolitan Region has the highest priority for vulnerability in all three methods. Full article

The utilization of hydrogen energy presents new opportunities for renewable energy integration, and the hybrid electricity–hydrogen synergy system exhibits significant potential for renewable energy accommodation and multi-scenario applications. To comprehensively explore the potential of such systems, this study proposes a two-stage design methodology that integrates HOMER simulation with multi-criteria decision-making (MCDM). Using Baotou, Inner Mongolia as a case study, HOMER is employed for simulation and optimization, and a comprehensive evaluation index system encompassing energy, economic, and environmental dimensions is established to assess the potential Cases and identify the optimal one. This study proposes an innovative weighting model combining CRITIC, Grey-DEMATEL, and Huber loss function. The model effectively resolves conventional methods’ deficiencies in balancing subjective–objective factors. Furthermore, an enhanced GRA-VIKOR model is developed to overcome the inherent constraints of conventional VIKOR approaches, particularly their excessive dependence on indicator weights and decision-maker preferences. The experimental results reveal that systems with 50% wind power integration demonstrate the optimal comprehensive development potential, while the developed MCDM framework successfully confines indicator weight deviations within the range of 0.016–0.019. Full article

Multiple criteria decision-making (MCDM) is a significant area of decision-making theory that certainly warrants attention. It might be difficult to accurately convey the necessary decision facts when navigating decision-making problems since we frequently run into complicated issues and unpredictable situations. To address this, introducing the novel idea of the bipolar pqr-spherical fuzzy set (${\mathrm{B}}_{\mathrm{pqr}}$SFS), a hybrid structure of the bipolar fuzzy set (BFS) and the pqr-spherical fuzzy set (pqr-SFS), is the main goal of this work. The fundamental (set-theoretic and algebraic) operations on ${\mathrm{B}}_{\mathrm{pqr}}$SFSs are explained as well as their relations to several known models. A distance measure, such as Euclidean distance, among ${\mathrm{B}}_{\mathrm{pqr}}$SFNs, is provided. Afterward, we expand the fundamental aggregation operators to the pqr-spherical fuzzy (${\mathrm{B}}_{\mathrm{pqr}}$SF) environment by developing bipolar pqr-spherical fuzzy-weighted averaging and bipolar pqr-spherical fuzzy-weighted geometric operators for aggregating ${\mathrm{B}}_{\mathrm{pqr}}$SFNs. According to the aforementioned distance measure and operators, an MCDM approach is established consisting of two algorithms, namely, the TOPSIS method and the method using the proposed operators in the ${\mathrm{B}}_{\mathrm{pqr}}$SF context. Moreover, a numerical example is provided in order to ensure that the presented model is applicable. By using the two algorithms, a comparative analysis of the proposed method with other existing ones is given in order to verify the feasibility of the suggested decision-making procedure. Full article

Vietnam has a coastline of over 3260 km and an exclusive economic zone extending 200 nautical miles, providing favorable conditions for the development of wave energy. Exploring and harnessing this endless energy source to maximize the use of the available resources is essential for sustainable economic development. According to research conducted by the Institute of Marine and Island Research, the total global exploitable wave energy capacity is 212 TWh per year, accounting for nearly 1% of the global total and 90% of Vietnam’s annual electricity consumption needs. However, selecting the optimal location to construct wave energy production plants requires the consideration of various criteria, including efficiency potential, economic and social, technological, transport and environment factors. In this research, the authors propose a hybrid MCDM model including a fuzzy analytic hierarchy process (FAHP) and Interactive Multi-Criteria Decision-Making method (TODIM) under a fuzzy environment for wave power plant location selection in Vietnam. A real-world application of the approach is given to showcase the effectiveness of the proposed method, where three potential locations are assessed based on 14 criteria. The research results propose priority locations for project implementation, while providing a scientific basis for policymakers and investors in the decision-making process. This study contributes to promoting the development of renewable energy and efficiently utilizing Vietnam’s marine resources. Full article

Direct sunlight causes glare and reduces indoor daylight quality, making shading systems essential. This study proposes and validates a perforated shading screen (PSS) to enhance daylighting and energy efficiency. A hybrid approach integrating parametric modeling, machine learning, multi-criteria decision-making (MCDM), and genetic algorithm (GA) is used to optimize the design incorporating architects’ preferences. The Analytic Network Process (ANP) is used to assign weights to performance metrics while accounting for interdependencies. The study evaluates PSS performance in three hot climate regions—Cairo, Riyadh, and Kuching—on both south and west elevations, comparing it to traditional fins. Results show that PSS consistently outperforms fins, significantly improving daylight and energy performance. The Useful Daylight Illuminance (UDI) increased by up to 105.32%, Continuous Daylight Autonomy (CDA) by up to 11.87%, while Annual Solar Exposure (ASE), Solar Gain (SG), and Energy Use Intensity (EUI) were reduced by up to 100%, 88.07%, and 45.2%, respectively. To validate the findings, the optimal PSS design from a selected case study was 3D-printed and experimentally tested. Results confirmed enhanced daylight distribution and reduced glare, improving occupant comfort. The proposed PSS offers an effective shading solution adaptable to various climates, balancing daylighting needs and energy efficiency. Full article

This study addresses the challenge of optimizing deep learning models for IoT network monitoring, focusing on achieving a symmetrical balance between scalability and computational efficiency, which is essential for real-time anomaly detection in dynamic networks. We propose two novel hybrid optimization methods—Hybrid Grey Wolf Optimization with Particle Swarm Optimization (HGWOPSO) and Hybrid World Cup Optimization with Harris Hawks Optimization (HWCOAHHO)—designed to symmetrically balance global exploration and local exploitation, thereby enhancing model training and adaptation in IoT environments. These methods leverage complementary search behaviors, where symmetry between global and local search processes enhances convergence speed and detection accuracy. The proposed approaches are validated using real-world IoT datasets, demonstrating significant improvements in anomaly detection accuracy, scalability, and adaptability compared to state-of-the-art techniques. Specifically, HGWOPSO combines the symmetrical hierarchy-driven leadership of Grey Wolves with the velocity updates of Particle Swarm Optimization, while HWCOAHHO synergizes the dynamic exploration strategies of Harris Hawks with the competition-driven optimization of the World Cup algorithm, ensuring balanced search and decision-making processes. Performance evaluation using benchmark functions and real-world IoT network data highlights superior accuracy, precision, recall, and F1 score compared to traditional methods. To further enhance decision-making, a Multi-Criteria Decision-Making (MCDM) framework incorporating the Analytic Hierarchy Process (AHP) and TOPSIS is employed to symmetrically evaluate and rank the proposed methods. Results indicate that HWCOAHHO achieves the most optimal balance between accuracy and precision, followed closely by HGWOPSO, while traditional methods like FFNNs and MLPs show lower effectiveness in real-time anomaly detection. The symmetry-driven approach of these hybrid algorithms ensures robust, adaptive, and scalable monitoring solutions for IoT networks characterized by dynamic traffic patterns and evolving anomalies, thus ensuring real-time network stability and data integrity. The findings have substantial implications for smart cities, industrial automation, and healthcare IoT applications, where symmetrical optimization between detection performance and computational efficiency is crucial for ensuring optimal and reliable network monitoring. This work lays the groundwork for further research on hybrid optimization techniques and deep learning, emphasizing the role of symmetry in enhancing the efficiency and resilience of IoT network monitoring systems. Full article

Biodiversity is essential for the health and stability of our planet, contributing to ecosystem services like pollination, nutrient cycling, and climate regulation. However, it faces significant threats from human activities, including habitat destruction and pollution. Transportation infrastructure, if not carefully managed, can fragment habitats and disrupt wildlife migration, exacerbating biodiversity loss. Thus, incorporating environmental and biodiversity considerations into transport planning is crucial for promoting long-term sustainability. Accordingly, the goal of this paper is to define a framework for evaluating and ranking intermodal transport routes based on their impact on the environment and biodiversity. The study employs a Geographic Information System (GIS)-based Multi-Criteria Decision-Making (MCDM) model, combining input from interactive GIS maps and stakeholders with a novel hybrid approach. The MCDM part of the model combines fuzzy Delphi and fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods for obtaining the criteria weights and the Axial Distance-based Aggregated Measurement (ADAM) method for obtaining the final ranking of the routes. This methodology application on several Trans-European Transport Network (TEN-T) routes revealed that the Hamburg/Bremerhaven–Wurzburg–Verona route had the least environmental and biodiversity impact. The study identified the Rotterdam–Milano route as the optimal choice, balancing sustainability, ecological preservation, and transport efficiency. The route minimizes ecological disruption, protects biodiversity, and aligns with European Union strategies to reduce environmental impact in infrastructure projects. The study established a framework for evaluating intermodal transport routes based on environmental and biodiversity impacts, balancing efficiency with ecological responsibility. It makes significant contributions by integrating biodiversity criteria into transport planning and introducing a novel combination of GIS and MCDM techniques for route assessment. Full article

Biodiversity, from genes to entire ecosystems, is crucial for a healthy planet. However, human activities, including business practices, are causing rapid biodiversity loss. This study focuses on selecting and integrating biodiversity protection practices into the supply chain, offering a chance to make positive changes for the environment and future generations. A new hybrid grey multi-criteria decision-making (MCDM) model is proposed in this paper, which combines the grey Best–Worst Method (BWM) for obtaining criteria weights and the grey Axial Distance-based Aggregated Measurement (ADAM) method for ranking alternatives (practices). The applicability of the proposed model for solving the defined problem was demonstrated by ranking nine practices according to seven criteria. The most effective supply chain management practices in the context of biodiversity conservation were supply chain policies (with a score of 0.044), biodiversity goal setting, monitoring, reporting, and transparency (0.039), and education and awareness raising (0.037). These practices are the best because they combine clear frameworks, measurable goals, and long-term cultural change for effective biodiversity conservation. The lowest ranked practice is compliance with legislation (0.006) since it represents a baseline, reactive approach rather than a proactive or innovative strategy for biodiversity conservation. This study provides a comprehensive framework and hybrid MCDM model that enhances theoretical knowledge and can serve as a basis for developing a practical tool for integrating, assessing, and prioritizing biodiversity-focused practices in supply chains. The main novelties of this paper are the extension of the ADAM method in the grey environment, the development of a new hybrid MCDM model that combines the grey BWM and grey ADAM method, the identification of biodiversity-oriented business strategies in supply chains and the criteria for their evaluation, and a framework for practice evaluation and selection. Full article

Decision making in city logistics (CL) is complex due to the numerous concepts and alternatives, as well as the intricate relationships between measures and effects. This study introduces a novel approach to evaluating urban freight transport (UFT) by combining multi-criteria decision making (MCDM) and social cost–benefit analysis (SCBA). This combination aims to improve decision making for sustainable CL concepts, particularly in reducing externalities in last-mile delivery. The model assesses various CL initiatives and urban consolidation center (UCC) concepts for their impact on UFT externalities. It uses the MCDM for ex ante scenarios assessment and prioritization. Input data were collected through a survey of experts from various sectors, and the Analytic Hierarchy Process (AHP) was applied in the case study of Novi Sad, Serbia. The prioritization highlighted the significance of implementing restrictive regulatory measures, alternative transport modes, and operational optimization within UCC concepts. By estimating capital, operational, and external costs, SCBA was applied to the prioritized UCC concepts, which were then further evaluated using the SCBA outputs. Sensitivity analysis was employed to assess the robustness of the proposed model. This paper offers valuable insights into the potential use of existing tools within a hybrid model to enhance decision making in CL. Full article

Background: AI-driven mental health solutions offer transformative potential for improving mental healthcare outcomes, but identifying the most effective approaches remains a challenge. This study addresses this gap by evaluating and prioritizing AI-driven mental health alternatives based on key criteria, including feasibility of implementation, cost-effectiveness, scalability, ethical compliance, user satisfaction, and impact on clinical outcomes. Methods: A fuzzy multi-criteria decision-making (MCDM) model, consisting of fuzzy TOPSIS and fuzzy ARAS, was employed to rank the alternatives, while a hybridization of the two methods was used to address discrepancies between the methods, each emphasizing distinct evaluative aspect. Results: Fuzzy TOPSIS, focusing on closeness to the ideal solution, ranked personalization of care (A5) as the top alternative with a closeness coefficient of 0.50, followed by user engagement (A2) at 0.45. Fuzzy ARAS, which evaluates cumulative performance, also ranked A5 the highest, with an overall performance rating of Si = 0.90 and utility degree Qi = 0.92. Combining both methods provided a balanced assessment, with A5 retaining its top position due to high scores in user satisfaction and clinical outcomes. Conclusions: This result underscores the importance of personalization and engagement in optimizing AI-driven mental health solutions, suggesting that tailored, user-focused approaches are pivotal for maximizing treatment success and user adherence. Full article

This research aims to assess the integration of different fuel cell (FC) options with battery and waste heat recovery systems through a mathematical modelling process to determine the most feasible retrofit solutions for a marine electricity generation plant. This paper distinguishes itself from existing literature by incorporating future cost projection scenarios involving variables such as carbon tax, fuel, and equipment prices. It assesses the environmental impact by including upstream emissions integrated with the Energy Efficiency Existing Ship Index (EEXI) and the Carbon Intensity Indicator (CII) calculations. Real-time data have been collected from a Kamsarmax vessel to build a hybrid marine power distribution plant model for simulating six system designs. A Multi-Criteria Decision Making (MCDM) methodology ranks the scenarios depending on environmental benefits, economic performance, and system space requirements. The findings demonstrate that the hybrid configurations, including solid oxide (SOFC) and proton exchange (PEMFC) FCs, achieve a deduction in equivalent CO₂ of the plant up to 91.79% and decrease the EEXI and the average CII by 10.24% and 6.53%, respectively. Although SOFC-included configurations show slightly better economic performance and require less fuel capacity, the overall performance of PEMFC designs are ranked higher in MCDM analysis due to the higher power density. Full article

In recent years, shared e-scooters have become increasingly popular as a mode of transportation in urban areas. Shared e-scooters have emerged as a convenient and sustainable transportation option in urban areas, providing users with a flexible and efficient way to travel short distances within a city. Many service providers and local municipalities are interested in implementing shared e-scooter operational models. However, determining which operating model to prefer and what the service areas will be is a significant problem. We aimed to solve the implementation of three different operational models, the site selection problem of station locations, and service areas for Erzurum, the metropolitan city in this study. As shared e-scooter is quite a new transportation mode; information collected to assess the operational models’ sustainability performance may be indeterminate and vague. In this study, the Geographic Information System (GIS)-based hybrid multi-criteria decision-making (MCDM) method is proposed for the solution of implementation, site selection, and service areas problems of three different shared e-scooter operational models. To this end, a four-step scientific and strategic solution approach is developed: (i) the identification and detailed explanation of 5 main and 24 sub-criteria, (ii) the weighting of criteria through the Analytical Hierarchical Process (AHP), Multi-Influencing Factor (MIF), and Best–Worst Method (BWM) in order to increase the sensitivity and robustness of the study, (iii) obtaining a suitability map for the solution of implementation, site selection, and service areas problems of operational models, and (iv) assigning shared e-scooter stations and analyzing their performance levels with COmplex PRoportional ASsessment (COPRAS). The results show that, in Erzurum, the central three districts are the most suitable for service areas. The paper’s solution methodology can help service providers and policymakers invest in sustainable shared e-scooter operational models, even in situations of high uncertainty. Full article

The furniture-manufacturing industry is pressured to improve quality, productivity, and profitability, particularly within increasingly volatile market conditions. This study is focused on the development of methods for optimizing production processes in a furniture-manufacturing company through the application of an integrated risk management framework. By integrating Failure Mode and Effects Analysis (FMEA) with advanced multi-criteria decision-making (MCDM) techniques, specifically fuzzy AHP, fuzzy TOPSIS, and fuzzy WINGS, a hybrid model is developed to identify, prioritize, and address critical failure points while accounting for complex interdependencies. Significant failure modes, such as order inaccuracies and delivery delays, are revealed as key findings and are found to notably affect productivity and customer satisfaction. The proposed model’s ability to capture cascading effects and a nuanced prioritization enables a more precise risk assessment, thereby supporting resilience and process efficiency in the furniture-manufacturing sector. This approach is shown to not only optimize production but also provide a foundation for applying such hybrid models in other industries to manage sector-specific interdependencies effectively. Full article

Within the realm of graphic design sustainability, selecting appropriate solutions has become a crucial strategic decision for organizations aiming to optimize their operations. This paper presents a novel hybrid multi-criteria decision-making (MCDM) approach, integrating a fuzzy analytical hierarchy process (FAHP) and fuzzy measurement alternatives and ranking according to compromise solution (FMARCOS). Evaluation criteria for graphic design sustainability are determined through consultation with experts, with their judgments expressed using linguistic terms based on fuzzy numbers. Criteria weights are calculated using FAHP, and the ranking and selection of the optimal potential solution are determined using FMARCOS. Subsequently, sensitivity analysis of the criteria weights is conducted to validate the results. Findings indicate that the integrated FAHP and FMARCOS model provides a robust and adaptable assessment framework for graphic design sustainability, enabling companies to navigate complexities strategically and effectively. The key contribution of this research is its emphasis on a systematic and objective model, offering practical insights relevant to the industry. It also serves as a valuable benchmark for future research in similar fields. Full article

Industrial pressure relief valves must function reliably and effectively to protect pressurized systems from excessive pressure conditions. These valves are essential safety devices that act as cushions to protect piping systems, equipment, and vessels from the risks of high pressure, which can cause damage or even explosions. The objectives of this study were to minimize valve failures, decrease the number of rejected valves in the production line, and enhance the overall quality of pressure relief valves. This work introduces an integrated quality improvement methodology known as the hybrid multi-criteria decision-making (MCDM)—failure mode and effects analysis (FMEA) approach. This approach is based on prioritizing crucial factors for any failure modes in the industrial setting. The presented case study demonstrates the application of a hybrid approach for identifying the fundamental causes of industrial pressure relief valve failure modes and malfunctions. This investigation highlights the applicability of FMEA as a methodology for determining causes and executing remedial actions to keep failures from happening again. FMEA helps uncover the underlying causes of industrial pressure relief valve failures, while the integration of the hybrid MCDM methodology enables the application of four integrated MCDM methods to identify crucial factors. The adopted model addresses the shortcomings of the conventional FMEA by accurately analyzing the relationships between the risk factors and by utilizing several MCDM methods to rank failure modes. Following the application of the adopted methodology, it was discovered that the high-risk failure modes for the pressure relief valve included misalignment of wire, normal wear/aging, rejection of machined parts, mismatch of mating parts, and corrosion. Therefore, risk managers should prioritize developing improvement strategies for these five failure modes. Similarly, failures comprising debris, delayed valve opening, internal leakage, premature valve opening, and burr foreign particles were determined as second essential groups for improvement. Full article