Multi-criteria Decision Research Articles

PARAMETRIC OPTIMIZATION OF ABRASIVE WATER JET MACHINING PROCESS USING COPRAS AND VIKOR METHODS

Abrasive Waterjet Machining (AWJM) is one of the widely used non-traditional machining process to cut the hard materials like aluminium metal matrix composites (AMMCs) and the materials which are very difficult to machine using conventional machining due to their high hardness and tendency to cause tool wear. Present work two popular multi criteria decision making (MCDM) techniques Complex Proportional Assessment (COPRAS) AND VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) Methods have been used to optimize the AWJM machining process parameters for the machining of AMMCs. Both equal weight and standard deviation methods are used to find the weight of three response parameters like material removal rate, surface roughness and kerf width. From the results it is observed that there is a strong correlation between the weight calculation and the MCDM techniques which shows the robustness of the techniques. It is also noticed that machining with highest traverse speed and with out silicon carbide (SiC) is giving good response paramètres. However machining with low traverse speed is giving worst response parameters.

SELECTING WELDING SAFETY GOGGLES BY MULTI-CRITERIA DECISION-MAKING APPROACHES— A COMPREHENSIVE EVALUATION

This paper presents a comprehensive application of the Multi-Criteria Decision-Making (MCDM) approach to evaluate and rank different models of welding goggles. The study uses both the Mean Weight and Entropy Weight methods in conjunction with five different MCDM techniques—TOPSIS, CODAS, COPRAS, SAW, and MOORA. These methods provide an insightful comparison and robust ranking of various goggle models. The analysis consistently highlighted 'ArcOne The Fly Safety Goggles' and '3M Speedglas 100 Series' as superior performers across multiple criteria.

A Climate Adaptation Asset Risk Management Approach for Resilient Roadway Infrastructure

As climate change intensifies, roadway infrastructure is increasingly at risk from extreme weather events including floods, hurricanes, and wildfires. This paper presents a system-of-systems performance-based asset risk management approach, designed to integrate various elements for effective investment prioritization and infrastructure resilience. Central to this approach are an Asset Inventory Database and a Risk Registry Database, supported by a Common Reference Location System (GIS). These components are the foundation for analytical modules to assess vulnerability and resilience based on exposure, sensitivity, and adaptive capacity. The approach includes an actionable framework to support a proactive data-driven performance-based management process for prioritizing investments. The project prioritization process consists of four steps: identifying risk factors, integrating climate data, conducting advanced risk assessments, and project prioritization. The goal is to prioritize resource allocation and develop climate-adaptive risk mitigation management strategies. Key performance indicators (KPIs) are recommended for setting goals, monitoring the outcomes of these strategies, and measuring their benefits. A Climate Impact Vulnerability Score (CIVS) is proposed to assess the susceptibility of infrastructure assets to environmental conditions. The approach also leverages artificial intelligence (AI) tools to analyze roadway infrastructure vulnerabilities and climate risk exposure. A case study applied to bridges using k-means clustering and multi-criteria decision analysis (MCDA) demonstrates the potential of advanced analytical methods in improving decision-making. This research concludes that the approach will contribute to enhancing resource allocation, supporting strategic decisions, aligning goals with budgets prioritizing investments, and strengthening the resilience and sustainability of roadway infrastructure.

Comparative Analysis of Common Structural Systems for Pedestrian Bridges in Kuwait

Introduction Pedestrian bridges are crucial urban infrastructure, providing safe passage over roads, railways, and waterways. Different structural systems like trusses, steel girders, and reinforced concrete beams are used, but more research is needed to determine the best design for specific regional contexts. Methods This research paper presents a comparison of various structural systems for pedestrian bridges, with a focus on a practical case study in Kuwait. The study evaluates five common pedestrian bridge designs: steel, concrete, and three truss arrangements. The designs were assessed based on multiple criteria, such as cost, construction time, structural weight, carbon emissions, and vibration performance. Using a decision-making matrix (DMM) and engineering judgment, Truss 3 (X-bracings without verticals) was identified as the optimal design. It features a unique bracing system that enhances its properties. Truss 3 was found to be the lightest design at 561.6 kN, with moderate CO2 emissions of 64.2 tons and a cost of 4288.5 KD per span. Detailed design and safety checks were conducted using ETABS software. The final design was detailed and presented using Autodesk Revit. Results The findings highlight the importance of using integrated frameworks and multi-criteria decision analysis (MCDA) to select suitable structural systems that meet technical and regional sustainability goals. Conclusion This research aims to provide a robust solution tailored to the unique conditions of Kuwait, ensuring safety, efficiency, and sustainability for pedestrian bridge infrastructure.

Attention-guided deep learning for effective customer loyalty management and multi-criteria decision analysis

Attention-guided deep learning for effective customer loyalty management and multi-criteria decision analysis

REDUCE—A Tool Supporting Inconsistencies Reduction in the Decision-Making Process

This paper presents REDUCE, a free online tool designed to support decision-making processes by addressing inconsistency in multiplicative pairwise comparison (PC) matrices, a key element of many multi-criteria decision-making (MCDM) methods, including the analytic hierarchy process (AHP). AHP relies on pairwise comparisons to assign weights to decision criteria or alternatives, but human-generated PC matrices often exhibit inconsistencies. Consistency is evaluated using Saaty’s consistency ratio ( CR ), where a value below 0.10 is considered acceptable. Higher inconsistency levels necessitate matrix corrections, which are challenging if the original expert is unavailable or revision constraints exist. REDUCE autonomously reduces inconsistency in PC matrices using two different algorithms that require no expert intervention. The tool accommodates different PC matrices, enabling users to specify the desired CR threshold (e.g., CR≤0.10) and select the algorithm for adjustment. It ensures the resulting matrix is consistent while preserving the original preference structure to the greatest extent possible. Additionally, REDUCE calculates weights for the compared entities, making it a valuable tool for applications of AHP and related methodologies. Quantitative evaluations demonstrate that REDUCE can improve matrices with high inconsistency (e.g., CR=0.25) to acceptable levels (e.g., CR=0.08) while retaining up to 95% of the original preference integrity, depending on the chosen algorithm. By addressing the accessibility gap for small and medium enterprises (SMEs) that lack resources for costly decision-making software or expert consultants, REDUCE facilitates broader adoption of MCDM tools. This work highlights the potential of REDUCE to enhance decision-making reliability and accessibility in resource-constrained environments.

A Declarative Modeling Framework for Intuitive Multiple Criteria Decision Analysis in a Visual Semantic Urban Planning Environment

The magnitude and multiformity of recorded and readily available urban data from independent sensors and mobile user devices have paved new possibilities for city environment planning and evolution. In previous works, a visual semantic decision support system for urban planning was presented, and the capability of machine learning approaches, ranging from random forests to graph-based convolutional neural networks, to infer preferable directions for future development was explored, extrapolating upon previous opted locations and selected alternatives for publicly commercial services. In this work, the anterior decision-making process leading to establishment choices is addressed with the same sets of criteria and samples within the same environment. A Declarative Modeling shell is proposed, and Multiple Criteria Decision Analysis processes are adopted to encompass the DM’s/DMs’ rationale, solely relying on methodologies close to human intuition. To this end, outranking representatives from the PROMETHEE family as well as weighted sum approaches are employed, fueled by the interpretation of the declarative description of decision parameters on behalf of the DM(s), exploring the ability to achieve classifications in a straight synthetic manner, i.e., in the absence of previous knowledge, thus exhibiting the potential of decision analysis methodologies, enhanced by Declarative Modeling, to be used as powerful intuitive tools in similar paradigm contexts.

Analysing heterogeneity in factors affecting adoption of ride-hailing services: a stepwise LCCA-MCDM modelling approach

AbstractThe study investigates the latent heterogeneity in travel behaviour among urban travellers, including ride-hailing service (RHS) users and non-users, by incorporating attitudes so as to reinforce conventional user-segmentation approaches. Simultaneously, prioritisation of ride-hailing specific attributes was carried out to assess how RHS will operate in a sustainable way. The study initially examines latent heterogeneity in travellers through a Latent Class Cluster Analysis (LCCA) model. Subsequently, it prioritises key RHS-specific attributes for each cluster using three established Multi Criteria Decision Making (MCDM) techniques. Three clusters were identified based on individuals’ attitudes and covariates (socio-demographics, travel habits, and built environment attributes). The largest cluster is the Tech-savvy ride-hailing-ready individuals (48%) with higher technological literacy, showing maximum acceptance towards ride-hailing. The second largest cluster comprises the Traditional active-mobility individuals (28%) who display the least proclivity towards RHS, probably due to their technological inhibition coupled with greater attachment to traditional travel alternatives. Lastly, the PV-loving multimodal individuals (24%) are primarily vehicle owners but prefer RHS for occasional trips. The final ranking obtained from the analysis has revealed that travel time, reliability, and flexibility are the motivators, while travel cost and waiting time are the deterrents, as perceived by the users, that influence RHS in the Indian context.

Analysis of the Effects of the Covid-19 Process on the Financial Performance of Businesses in the Defense Industry By the TOPSIS, EDAS, CoCoSo Method: ISE Sample

The defense industry has recently received attention due to its significant advancements. This study examines the impact of the COVID-19 pandemic on the financial performance of companies operating in the Turkish defense sector. The financial data from 2019 to 2023 of defense industry firms listed on Borsa Istanbul, namely ASELS, OTKAR, PAPIL, SDTTR, NETAŞ, and KATMER, were analyzed. The primary objective of this research is to evaluate the financial performance of these companies during the pandemic by comparing it with the pre-pandemic period. In this context, eight financial ratios related to liquidity, turnover, financial structure, and profitability were utilized to assess financial performance. The criteria weights were determined using the Entropy method, and the performance rankings of the companies were established through Multi-Criteria Decision Making (MCDM) techniques, including TOPSIS, EDAS, and CoCoSo. This study offers a novel perspective by examining the effects of the COVID-19 pandemic on Turkish defense industry firms and uncovering their financial performance in comparison to previous periods. According to the results of the entropy method, the most significant criteria for evaluating the financial performance of companies listed on Borsa Istanbul (BİST) are Active Profitability Ratio (APR) and Equity Profitability Ratio (EPR). In the year 2020, which was significantly impacted by the pandemic, companies with strong financial performance included ASELS, PAPIL, SDTTR, and NETAŞ. Conversely, KATMER was identified as one of the companies with weaker financial performance during the same period.

Selection of photovoltaic panels for floating systems: an analysis based on Entropy, CRITIC and TOPSIS

ABSTRACT In the context of sustainable energy use, multiple criteria are involved in the decision to select the best energy generation projects, as well as its installation location. However, despite the widespread use of decision-making techniques, there is a noticeable gap due to the lack of a systematic process for selecting the best projects for energy transition. This paper evaluates alternatives of photovoltaic panels for energy generation in floating systems and proposes a procedure to select the best project using a multiple-criteria decision analysis. The Entropy method was used to determine the weight of eight criteria, including cost, number of cells, efficiency, area, panel weight, and power characteristics, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to make the selection. A sensitivity analysis was conducted considering different weighing methods. Among the 20 photovoltaic panels analysed, the method proved to be effective in determining the most successful one for installation in floating systems. The chosen panel maintained the best performance in all scenarios tested. This paper provides a systematic approach for selecting the most suitable photovoltaic panel for floating energy systems, contributing to researchers to refine decision-making methodologies and practitioners to optimise project implementation in sustainable energy initiatives.

A Novel Framework for Belief and Plausibility Measures in Intuitionistic Fuzzy Sets: Belief and Plausibility Distance, Similarity, and TOPSIS for Multicriteria Decision Making

Dempster–Shafer Theory (DST) relies significantly on belief and plausibility measures to handle ambiguity and uncertainty; however, DST has been extended to fuzzy sets (FSs) and intuitionistic fuzzy sets (IFSs) with only a few extensions focusing on belief and plausibility intuitionistic fuzzy distance (BP-distance) and similarity (BP-similarity) until now. In this work, we propose a novel framework for the belief and plausibility of intuitionistic fuzzy sets (BP-IFSs) and their BP-distance and BP-similarity measures. We modified steps 4 and 5 of the classical TOPSIS method, utilizing both distance and similarity measures to rank the alternatives that satisfy all necessary axioms of distance and similarity. We present numerical examples involving pattern recognition, linguistic variables, and clustering to illustrate the efficiency of these measures, and we develop belief and plausibility TOPSIS (BP-TOPSIS) using the proposed criteria and apply it to complex multicriteria decision-making (MCDM) challenges. The results demonstrate the practicality and effectiveness of our approach.

Assessing Urban Community Resilience to Flood Using a Hybrid Multi-Criteria Decision-Making Approach: A Case of Guwahati City, Assam, India

Assessing Urban Community Resilience to Flood Using a Hybrid Multi-Criteria Decision-Making Approach: A Case of Guwahati City, Assam, India

Skin Cancer Detection using CNN (Convolution Neural Network) with AI Medical Assistant

Skin cancer is a very common disease, and with numerous morphological forms and characteristics that are similar between malignant and benign skin lesions, makes the diagnosis pretty difficult. Recent trends in deep learning, with CNNs being the first to enter this domain, have highly facilitated the classification and detection of skin cancer, even in its nascent stages, from images. A plethora of such research works proposed novel architectures for CNNs that may possibly improve the accuracy in the diagnosis of the lesion given as against those belonging to seborrheic keratosis, nevus, or melanoma. The best model, according to the works on these authors, was the multi-layer deep learning model known as SkinLesNet, which performed better than established models such as ResNet50 and VGG16 in all datasets. More extensive studies present a DCNN model that addresses the issue of class imbalance and is stronger than transfer learning models, such as DenseNet and MobileNet. This model achieved strong accuracy on several instances: 98.5% with HAM10000 and 97.1% with ISIC-2019 data. Optimizations in model selection methods: In addition, there existed optimizations in model selection. This was through the application of the multiple-criteria decision making approaches. For instance, the method RAPS can be taken as an example. Through the application of this method, a very significant increase in accuracy was achieved. For optimizing the use of feature selection using Inception V3 in determining diagnosis for oral cavity squamous cell carcinoma, BPSO showed an accuracy of 96.3%. Such breakthroughs underpin therefore the capability of deep learning and AI techniques in raising diagnostic accuracy while cutting costs and making a chance for early intervention of skin and other cancers.

Assessing and Prioritizing Perceived Service Quality in Healthcare Settings in Nepal: A Multi-Criteria Decision Analysis Using Analytical Hierarchy Process (AHP)

This study uses the Analytical Hierarchy Process (AHP) to compare and assess different private healthcare facilities in Nepal according to perceived service quality. For methodology, first, the criteria for the study were established and rated. Then sensitivity analysis was carried out to evaluate the decision using AHP to rank healthcare according to the characteristics of service quality and the relative positions. The data were collected from eleven hospitals in Kathmandu, Nepal. SERVQUAL dimensions were used in order to comprehend the Perceived Service Quality. The analytical procedure was carried out using Expert Choice software. The study shows that Private Teaching Hospitals have the highest Perceived Service Quality among patients, followed by Public Teaching Hospitals and that patients value tangibility over other service aspects. The study provides insights into the characteristics of service quality in Nepalese hospitals. The findings help healthcare managers to understand the factors that patients consider important to improve service quality.

Intelligent Methods for Estimating the Flood Susceptibility in the Danube Delta, Romania

Floods, along with other natural and anthropogenic disasters, profoundly disrupt both society and the environment. Populations residing in deltaic regions worldwide are particularly vulnerable to these threats. A prime example is the Danube Delta (DD), located in the Romanian sector of the Black Sea. This research paper aims to identify areas within the DD that are highly or very highly susceptible to flooding. To accomplish this, we employed a combination of multicriteria decision-making (AHP) and artificial intelligence (AI) techniques, including deep learning neural networks (DLNNs), support vector machines (SVMs), and multilayer perceptron (MLP). The input data comprised previously flooded regions alongside eight geographical factors. All models identified high or very high flood potential of over 65% of the studied area. The models’ performance was assessed using receiver operating characteristic (ROC) analysis, demonstrating excellent outcomes evaluated by the area under the curve (AUC) exceeding 0.908. This study is significant as it lays the groundwork for implementing measures against flood impacts in the DD.

Topology Optimization, Part Orientation, and Symmetry Operations as Elements of a Framework for Design and Production Planning Process in Additive Manufacturing L-PBF Technology

This paper investigates the possibility of the application of different optimization techniques in the design and production planning phase in the metal additive manufacturing process, specifically laser powder bed fusion (L-PBF) additive technology. This technology has a significant market share and belongs to the group of mature additive technology for the production of end-use metal parts. In the application of this technology, there is a space for additional cost/time reduction by simultaneously optimizing topology structure and part orientations. Simultaneous optimization reduces the production time and, indirectly, the cost of parts production, which is the goal of effective process planning. The novelty in this paper is the comparison of the part orientation solutions defined by the software algorithm and the experienced operator, where the optimal result was selected from the aspect of time and production costs. A feature recognition method together with symmetry operations in the part orientation process were also examined. A framework for the optimal additive manufacturing planning process has been proposed. This framework consists of design and production planning phases, within which there are several other activities: the redesign of the part, topological optimization, the creation of alternative build orientations (ABOs), and, as a final step, the selection of the optimal build orientation (OBO) using the multi-criteria decision method (MCDM). The results obtained after the MCDM hybrid method application clearly indicated that simultaneous topology optimization and part orientation has significant influence on the cost and time of the additive manufacturing process. The paper also proposed a further research direction that should take into consideration the mechanical as well as geometric, dimensioning and tolerances (GDT) characteristics of the part during the process of ABOs and OBO, as well as the uses of symmetry in these fields.

More robust offshore wind energy planning through model ensembling

This research performs an ex-ante assessment of the 19 high potential areas for offshore wind energy (HPA-OWE) allocated in four maritime spatial planning subdivisions of Spain. A 39 geo-statistical criteria pool was developed and categorized into five planning tiers (coexistence, socio-ecological, spatial-efficiency, energy-equity, technical/technological). An ensemble of three multi-criteria decision analysis (MCDA) techniques coupled with a Monte Carlo method based on a large, uniform number of randomly distributed criteria weights is applied for more robust priority rankings of HPA-OWE. The co-existence tier indicates that HPA-OWE should be prioritized in the North Atlantic and in the Levantine–Balearic planning subdivision. The application of machine learning on the MCDA results identified criteria that most influence the rank of each HPA-OWE at planning subdivision. The outcomes highlight the need to include place-based data to better take into account spatial inequalities in coastal regions and re-balance them with socio-economic and energetically privileged coastal territories.

Study on the Function and Planning of Urban River Ecological Corridor

This study investigates the functions and planning principles of urban river ecological corridors, highlighting their vital role in urban ecosystems. Urban rivers, which include both waterways and riparian zones, provide essential ecosystem services such as habitat provision, temperature regulation, pollutant filtration, and flood mitigation. However, rapid urbanization has led to the degradation of these corridors, resulting in habitat fragmentation, reduced biodiversity, and compromised ecological integrity. The paper reviews global efforts and strategies for urban river restoration, emphasizing the significance of ecological methods and public participation in the planning process. A case study of the Yuhangtang River in Hangzhou, China, exemplifies various restoration approaches, including traditional flood control, ecological restoration, and landscape design, all assessed using a multi-criteria decision-making (MCDM) framework. The findings indicate that employing the MCDM tool can facilitate planning that integrates the functions of river ecological corridors. Additionally, plans that achieve a balance of ecological, economic, social, and aesthetic benefits are more likely to gain public acceptance.

Study on the Function and Planning of Urban River Ecological Corridor

This study investigates the functions and planning principles of urban river ecological corridors, highlighting their vital role in urban ecosystems. Urban rivers, which include both waterways and riparian zones, provide essential ecosystem services such as habitat provision, temperature regulation, pollutant filtration, and flood mitigation. However, rapid urbanization has led to the degradation of these corridors, resulting in habitat fragmentation, reduced biodiversity, and compromised ecological integrity. The paper reviews global efforts and strategies for urban river restoration, emphasizing the significance of ecological methods and public participation in the planning process. A case study of the Yuhangtang River in Hangzhou, China, exemplifies various restoration approaches, including traditional flood control, ecological restoration, and landscape design, all assessed using a multi-criteria decision-making (MCDM) framework. The findings indicate that employing the MCDM tool can facilitate planning that integrates the functions of river ecological corridors. Additionally, plans that achieve a balance of ecological, economic, social, and aesthetic benefits are more likely to gain public acceptance.

Artificial Intelligence and Information Governance: Strengthening Global Security, through Compliance Frameworks, and Data Security

This study examines the dual role of artificial intelligence (AI) in advancing and challenging global information governance and data security. By leveraging methodologies such as Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), Structural Equation Modeling (SEM), and Multi-Criteria Decision Analysis (MCDA), the study investigates AI-specific vulnerabilities, governance gaps, and the effectiveness of compliance frameworks. Data from the MITRE ATT&CK Framework, AI Incident Database, Global Cybersecurity Index (GCI), and National Vulnerability Database (NVD) form the empirical foundation for this analysis. Key findings reveal that AI-driven data breaches exhibit the highest regulatory scores (0.72) and dependency levels (0.81), underscoring the critical need for robust compliance frameworks in high-risk AI environments. PCA identifies regulatory gaps (45.3% variance) and AI technology type (30.2% variance) as significant factors influencing security outcomes. SEM highlights governance strength as a primary determinant of security effectiveness (coefficient = 0.68, p < 0.001), while MCDA underscores the importance of adaptability in governance frameworks for addressing AI-specific threats. The study recommends adopting quantum-resistant encryption, enhancing international cooperation, and integrating AI automation with human oversight to fortify governance structures. These insights provide actionable strategies for policymakers, industry leaders, and researchers to navigate the complexities of AI governance and align technological advancements with ethical and security imperatives in a rapidly evolving digital landscape.