Multi-criteria Decision-making Framework Research Articles

Design of Hybrid Renewable Energy Systems: Integrating Multi‐Objective Optimization Into a Multi‐Criteria Decision‐Making Framework

ABSTRACTResearch into hybrid renewable energy systems (HRESs) fulfills the need for the development of sustainable and environmentally friendly energy systems to supply house‐holds. The design of HRESs is a challenging endeavor requiring the optimization of multiple objectives considered over multiple criteria. This paper presents a new multi‐criteria decision‐making framework (MCDM) to automate the design. The proposed framework initially uses a metaheuristic multi‐objective (MO) optimization algorithm to generate optimal candidate configurations and then objectively evaluates candidates to select the best configuration. A combination of the MO particle swarm optimization and a newly developed MO leaders‐and‐follower algorithms (MO‐LaF/PSO) is used to generate optimal configurations based on minimal levelized cost of energy (LCOE), renewable energy (RE) power abandonment, and CO2 emissions, while maintaining an acceptable level of reliability. The evaluation phase applies the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) ranking method that uses objective criteria weights calculated using MEREC (MEthod based on the Removal Effects of Criteria). This method is applied to a case‐study of an off‐grid Wind/PV/Diesel/Battery HRES. The results reveal that this newly proposed framework generates a unique top‐ranking configuration with an LCOE of 0.199 $/kWh, 0% wastage of RE, and 982 tons of CO2.

AHP-Driven Strategic Decision Framework for Selecting Optimal Radiopharmaceutical Facility Locations

Given the increasingly important role of radiopharmaceutical products in medical diagnosis and treatment, choosing a strategic location for a new radiopharmaceutical facility is an important decision for pharmaceutical companies. This research develops a multi-criteria decision-making framework for selecting optimal radiopharmaceutical facility locations. The framework begins by generating alternative locations and criteria by using a PESTEL analysis to assess the external environment. Five key criteria were then identified for evaluation: customer proximity, transportation accessibility, operational support, public acceptance, and legal acceptance, with each criterion further broken down into specific sub-criteria. The Analytical Hierarchy Process (AHP) method was used to weigh these criteria and sub-criteria based on expert and stakeholder input, and to evaluate the alternative locations. Results of the AHP analysis showed that proximity to customers and accessibility to transportation were the most important criteria, leading to the selection of Cikarang as the optimal location due to its proximity to hospitals, convenient transportation access, and compliance with regulations and permits. Sensitivity analysis confirmed the robustness of this choice against variations in criteria weights. This framework provides a structured approach for pharmaceutical companies in strategic decision-making regarding the siting of radiopharmaceutical facilities.

Assessing Housing Preferences and Living Conditions of Migrant Workers in the Fringe Areas of Bengaluru City, India

This study investigates the housing preferences and its impact on living conditions of migrant industrial workers in the fringe areas of Bengaluru, India, where rapid urbanization and economic expansion have led to a significant demand for affordable housing. Using the Analytic Hierarchy Process (AHP), a multi-criteria decision-making framework, this study analyzed key factors influencing housing choices, such as proximity to the workplace, affordability, and access to essential amenities. Data were collected from 400 respondents through a combination of surveys, complimented by field observations and expert consultations. The findings indicate a strong preference for affordable housing close to employment hubs, with “proximity to workplace” emerging as the most critical criterion with a priority weight of 43.36%, followed by “affordability” with a priority weight of 12.47%. However, field insights reveal a trade-off with housing quality and living conditions. Many migrant workers are confined to overcrowded and poorly ventilated rental units, often provided by employers, which compromises their health and well-being. Our results contribute to the understanding of urban housing challenges in rapidly growing economies and emphasize the importance of sustainable, health-oriented housing policies that can mitigate environmental impacts and improve the quality of life for low-income migrant populations.

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.

A Novel TOPSIS Framework for Multi-Criteria Decision Making with Random Hypergraphs: Enhancing Decision Processes

In today’s complex decision-making landscape, multi-criteria decision-making (MCDM) frameworks play a crucial role in managing conflicting criteria. Traditional MCDM methods often face challenges due to uncertainty and interdependencies among criteria. This paper presents a novel framework that combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with random hypergraphs to enhance decision processes. In TOPSIS, asymmetry in criterion interactions is typically managed by assigning different weights, while for independent criteria, Euclidean distance introduces geometric symmetry, treating all dimensions (criteria) equally when calculating an alternative’s distance from ideal or negative-ideal solutions. Although assigning weights can partially address asymmetry caused by interdependencies and uncertainties among criteria, it cannot fully account for uncertainty in data and criteria interactions. Our approach integrates random hypergraphs to better capture these relationships, offering a more refined representation of decision problems and improving the robustness of the decision-making process. In this method, we first capture criteria interactions in a random hypergraph. Using properties of the graph and input data, the algorithm then generates weights for interacted groups of criteria. These weights, termed “dynamic weights”, adapt in response to changes in criteria interactions and data, forming the basis for a generalized TOPSIS algorithm. A comparative study with illustrative examples highlights the advantages of this enhanced TOPSIS framework, showing how random hypergraphs expand its analytical capabilities. This research advances the theoretical foundation of MCDM frameworks while offering practical insights for practitioners seeking robust solutions in complex and uncertain decision environments.

Artificial intelligence-powered image analysis: A paradigm shift in infectious disease detection

The global burden of infectious diseases significantly affects mortality rates, with their varying symptoms making it challenging to assess and determine the severity of infections. Different countries face unique challenges related to these diseases. This study introduces innovative Artificial Intelligence (AI) based methodologies to enhance diagnostic accuracy through the analysis of medical imagery. It achieves this by developing a mathematical model capable of identifying potential infectious diseases from images, utilizing a Multi-Criteria Decision-Making (MCDM) framework. This cutting-edge approach combines Hypersoft Set (HSS) within a fuzzy context, pioneering in AI-driven diagnostic processes. The decision-making process might suggest actions such as isolation, quarantine in either domestic settings or specialized facilities, or admission to a hospital for further treatment. The use of visual aids in this research not only improves understanding but also highlights the effectiveness and significance of the proposed methods. The foundational theory and the results from this novel approach demonstrate its potential for widespread application in fields like machine learning, deep learning, and pattern recognition, indicating a significant stride in the fight against infectious diseases through advanced diagnostic techniques.

FHG-PR: a hybridized fuzzy-AHP and game theory model for assessing privacy risk on social media platforms

Social media platforms have become integral to our daily lives, enabling global connectivity and interaction. Current research provides general solutions for mitigating privacy risks, but it does not focus on particular platforms. However, the voluntary sharing of personal information on these platforms presents considerable privacy risks, highlighting the need for effective risk assessment mechanisms. This study presents a hybrid approach that uses the fuzzy Analytical Hierarchy Process (AHP) and game theory to estimate privacy risks on Meta and X. We used a fuzzy AHP to rank determinant variables based on surveys of social media users and professionals. These weighted criteria were then used in a Multi-Criteria Decision-Making (MCDM) framework using cooperative game theory to discover ways to lower privacy hazards. The model accurately analyzed the privacy threats on Meta and X, recommending alternate techniques for preventing breaches in data privacy. The cooperative game theory method allows stakeholders to collaborate on creating privacy-preserving measures. The findings emphasize the model’s ability to address platform-specific privacy threats, as well as its flexibility in other social media settings. The model’s potential for real-world application is demonstrated by its ability to provide practical risk reduction measures, which improve privacy protection for social media users.

Comparative Sensitivity Analysis in Composite Material Selection: Evaluating OAT and COMSAM Methods in Multi-criteria Decision-making

The decision-making process is critical across various fields, influencing essential aspects like performance, safety, and sustainability. In engineering and materials science, selecting suitable materials is a complex process involving multiple interdependent criteria, which impact the reliability and lifecycle of a design. Traditional sensitivity analysis methods, such as the one-at-a-time (OAT) approach, assess the effects of individual parameter changes but often fail to capture the cumulative impact of simultaneous modifications. It can limit the effectiveness of decision tools in real-world scenarios where multiple factors vary concurrently. This study utilizes the Comprehensive Sensitivity Analysis Method (COMSAM) to address limitations in traditional sensitivity analysis by modeling simultaneous parameter adjustments across multiple criteria. Specifically, the study aims to validate COMSAM’s effectiveness in the practical problem of composite material selection, comparing it with the traditional OAT approach within a multi-criteria decision-making (MCDM) framework. By integrating both sensitivity analysis techniques with the Characteristic Objects Method (COMET), this research ensures robust evaluation and mitigates vulnerabilities like the rank reversal paradox. The findings demonstrate that COMSAM provides enhanced insights into the impact of parameter interdependencies, offering a more resilient foundation for decision-making in high-stakes environments where material properties and performance are paramount.

Prioritization of monitoring compounds from SNTS identified organic micropollutants in contaminated groundwater using a machine learning optimized ToxPi model

Advanced suspect and non-target screening (SNTS) approach can identify a large number of potential hazardous micropollutants in groundwater, underscoring the need for pinpointing priority pollutants among detected chemicals. This present study therefore demonstrates a novel multi-criteria decision making (MCDM) framework utilizing machine learning (ML) algorithms coupled with toxicological prioritization index tool (i.e., ml_ToxPi) to rank 251 chemicals of interest in groundwater for subsequent targeted analysis. The MCDM framework integrated chemical analysis data (i.e., peak area and detection frequency), toxicity profiles (i.e., bioactivity ratio, human exposure metadata, and carcinogenicity metadata), as well as the environmental fate and transport information (i.e., octanol-water partition coefficient (log Kow), water solubility, biodegradation half-life, and soil adsorption coefficient (Koc)) for ranking the identified pollutants, and the random forest machine learning model was useful for systematically determining the weighting factors of each variable according to their variable importance scores (R2 = 0.808 and 0.778 for training and testing datasets, respectively, while RMSE = 0.042 in both cases). A total of 47 unique high priority compounds (i.e., ml_ToxPi score ≥ 0.55) were identified across the investigated sampling regions, which constituted diverse groups of compounds classified according to their chemical uses, such as alkylated polycyclic aromatic hydrocarbons (alkyl-PAHs), organophosphate flame retardants (OPFRs), parent PAHs, personal care products (PCPs), pesticides, pharmaceuticals, phenols, plasticizers, transformation product (TPs), and other industrial use chemicals. By incorporating relevant variables into the proposed ML-optimized ToxPi MCDM framework, the prioritization approach described here may be adopted in future SNTS assessment of environmental and biological media.

Application of Multi-criteria Decision-making Methods for Forensic Analysis of Mechanical Parts in Vehicle Accidents using q-Rung Orthopair Fuzzy Numbers

This study explores the application of a Multi-Criteria Decision-Making (MCDM) approach based on q-rung orthopair fuzzy numbers to identify the origin of mechanical parts found at vehicle accident scenes. The primary objective is to determine the most likely vehicle to which these parts belong by evaluating key criteria such as compatibility, damage level, serial number matching, and color compatibility. Q-rung orthopair fuzzy numbers offer an advanced method to handle the inherent uncertainty and vagueness associated with forensic evaluations, particularly in scenarios where data is incomplete or imprecise. The proposed methodology involves defining the criteria, assigning membership and non-membership degrees using q-rung orthopair fuzzy sets, and applying an aggregation process to effectively rank alternatives. This approach facilitates flexible decision-making by accommodating different levels of confidence and uncertainty, making it particularly suitable for forensic applications where evidence is often ambiguous. The findings demonstrate that integrating q-rung orthopair fuzzy numbers into the MCDM framework significantly enhances the accuracy and reliability of identifying vehicle components involved in accidents. The proposed methodology provides a systematic tool to support forensic investigations, aiding in the determination of liability and contributing to more robust outcomes in mechanical evidence analysis.

Carving a niche in building energy using modern vernacular house with passive wall materials - A multi-criteria decision-making framework

The building sector, one of the world's largest energy consumers, includes vernacular houses that offer good living conditions. However, increasing population and energy demands have caused vernacular houses to impact the ecosystem negatively, thus losing popularity. Passive design features can alleviate this problem but entail several factors and alternatives. This study aims to develop a fuzzy-based multi-criteria decision-making framework for selecting an optimal wall material for vernacular house that satisfies the technical, economic, environmental and social factors. Seven different wall materials, along with governing factors like room temperature, humidity, cooling and heating loads, cost, comfort index, CO2 emission, job creation and social acceptability, are considered. Four fuzzy-based decision-making tools are used, followed by sensitivity analysis to examine the results. A test cell of an established 3BHK vernacular house is made using design-builder and is simulated with Energy plus software. The error indices confirmed the design-builder model's accuracy, with NMBE and CV-RMSE values for room temperature and relative humidity both under 1 %, well within ASHRAE 14's threshold limits. From the results, cinder concrete is found as optimal passive wall material, resulting in lower cooling and heating loads and CO2 emission by 0.68 %, 9.64 %, and 2.27 %, respectively, than traditional vernacular house.

A global analysis of nearshore and submarine springs: spatial distribution, controlling factors, and probability of presence

Coastal springs act as bi-directionally preferential flow paths between coastal aquifers and oceans. While these springs can supply coastal ecosystems with nutrients, they also present vulnerabilities such as contamination and seawater intrusion. Despite their significance, substantial knowledge gaps exist regarding coastal springs due to their complex hydrogeological nature. This study provides a comprehensive global assessment of coastal springs, focusing on their distribution, controlling factors, and likelihood of occurrence. A global dataset of known coastal springs was compiled and analyzed, revealing 66 % of identified springs in the Mediterranean region, mainly linked to karst systems. In contrast, fewer springs were noted in Africa, South America, and South Asia. Key factors influencing spring occurrence were examined using geostatistical methods and integrated into a multi-criteria decision-making framework to develop a Coastal Spring Probability Index (CSPI) along coastline. High-potential areas for coastal springs were identified in regions characterized by significant carbonate and volcanic rock formations, wetter climates, and active tectonic margins, such as southern Europe, the Caribbean, tropical islands, and eastern Asia. Conversely, regions with dry climates and high water demand, such as North Africa and South America, exhibited a lower likelihood of spring presence. These findings will serve as a baseline for local-scale studies and aimed at improving coastal spring inventories and establishing monitoring networks. It will contribute to vulnerability assessment studies, thereby enhancing the management of coastal groundwater resources. The study emphasizes the importance of multidisciplinary approaches to understand coastal spring dynamics and advocates for a strategic planning in groundwater management and conservation. Ongoing efforts to inventory and monitor coastal springs, coupled with targeted conservation measures, are essential for ensuring the long-term sustainability of coastal water resources and ecosystems.

Decision Analysis Algorithm Using Frank Aggregation in the SWARA Framework with p,qRung Orthopair Fuzzy Information

The present study introduces an innovative approach to multi-criteria decision making (MCDM) aimed at handling decision analysis involving p,qrung orthopair fuzzy (p,qROF) data, where the criteria weights are completely unknown. To achieve this objective, we formulate generalized operational rules referred to as Frank operational rules, tailored for p,qROF numbers (p,qROFNs) utilizing the Frank t-norm and t-conorm. With these newly devised operations as a foundation, we create a variety of p,qROF aggregation operators (AOs) to effectively aggregate p,qROF information. Furthermore, we examine specific instances of these operators and rigorously establish their desirable properties, including idempotency, monotonicity, boundedness, and symmetry. Subsequently, we adapt the SWARA technique to the realm of p,qROF fuzzy data and this adapted technique becomes instrumental in determining criteria weights within the proposed MCDM framework centered around proposed AOs. We furnish a descriptive example to exemplify the practicality of the developed approach. Lastly, the effectiveness and soundness of our approach are underscored through both parameter analysis and a comparative evaluation.

Research on condition assessment of nuclear power systems based on fault severity and fault harmfulness

Obtaining real time quantitative condition assessment results is a focus in the field of nuclear power system PHM. The existing researches on the nuclear power system condition assessment based on the Multi-Criteria Decision-Making framework do not consider the faults perniciousness differences. This leads to the assessment results relying on the system status deviation degrees(severity), and the weighting and aggregation processes cannot effectively reflect the differences in the consequences and risks of different faults, which represent the faults harmfulness. A quantitative assessment method based on fault severity and fault harmfulness is proposed to address this issue. The novel method quantifies the fault severity based on the similarity principle while diagnosing system faults, and then analyzes the devices mainly affected by various faults to quantifies the harmfulness. Combining the system status deviation degrees(severity), the devices and parameters importance, and the differences in faults harmfulness, the novel method aggregates comprehensive system condition assessment results. Based on a widely recognized nuclear power system faults dataset, the novel method was compared with other methods. The conclusion is that the novel method considers the differences in the faults harmfulness, resulting in more reasonable assessment results and avoiding insufficient or excessive warnings.

A strategic multi-criteria decision-making framework for renewable energy source selection in Saudi Arabia using AHP-TOPSIS

The Kingdom of Saudi Arabia (KSA) is targeting 50 % of its energy generation from renewable energy sources (RES) by 2030. However, a comprehensive approach is needed to prioritize the available RES in the kingdom. This study employs a survey-based approach which gathered insights from several academic and industrial experts and then utilizes analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) methods to determine the optimal RES. In this case, hybrid RES (solar-wind, solar-geothermal, solar-biomass) is evaluated against single-source alternatives based on different criteria including cost-effectiveness, land conservation, low intermittency, resource potential, design simplicity, and social acceptance. The results showed that cost-effectiveness emerged as the predominant criterion with importance of 31 % followed by low intermittency and resource potential contributing with 24 % and 22 % in importance, respectively. Based on these findings, solar energy is recognized as the best RES, with a priority level of 23 % followed by the hybrid solar-wind and hybrid solar-geothermal, which attained priority levels of 20 and 16 %, respectively. Subsequently, a sensitivity analysis was carried out to assess the impact of varying criteria weights. The findings offer the decision maker useful insights into the priority of different RES in KSA.

A hybrid data-driven optimization and decision-making approach for a digital twin environment: Towards customizing production platforms

In the Industry 4.0 era, advanced technologies are transforming manufacturing processes and systems. Additionally, the increasing prevalence of big data and AI technologies have made decision-making using manufacturing data increasingly important. However, Small and Medium-sized Enterprises (SMEs) have encountered significant obstacles in adopting these technologies due to resource limitations and constraints. For SMEs, selecting an appropriate production strategy is challenging due to the complexity of manufacturing systems. As a response, this paper proposes a hybrid Simulation-Optimization with Multi-Criteria Decision-Making (SOMCDM) framework for SMEs to identify effective and customized production layouts. In the proposed approach, we model various production scenarios using a cellular manufacturing system. Surrogate models for different production layouts are created to basis functions using Multivariate Adaptive Regression Splines (MARS). Subsequently, the basis functions are used as fitness functions to identify optimal production parameters in a genetic algorithm. Then, optimized parameters are applied to production criteria and ranked using a multi-criteria decision-making technique. In a case study, the proposed framework is applied to select the best production platform among three scenarios for a company assembling complex products. The selected production platform improves overall manufacturing performance by 11.95% compared to the existing one. This study demonstrates the effectiveness of the proposed framework in identifying the best production platform for labor-intensive SMEs manufacturing high-mix, low-volume products using SOMCDM for a digital twin environment. The proposed framework is further detailed through a case study of a 3D printer assembly factory.

Evaluating Sustainable Materials for Automotive Applications Through Comprehensive MCDM Analysis Using CODAS, COPRAS, VIKOR, and ENTROPY

The automotive industry faces increasing pressure to incorporate sustainable materials in vehicle manufacturing to meet environmental, economic, and performance requirements. This research develops a comprehensive Multi-Criteria Decision-Making (MCDM) framework to evaluate and select sustainable materials for automotive body and instrument panels. The study integrates CODAS, COPRAS, VIKOR, and ENTROPY methods to systematically assess various material alternatives. By considering multiple criteria, including cost, environmental impact, and mechanical properties, the proposed framework provides a holistic evaluation to identify the most suitable materials. The findings contribute to advancing sustainable practices in the automotive industry, offering manufacturers a robust tool for informed decision-making. This approach supports the industry's transition towards environmentally responsible and efficient vehicle production, ensuring that the selected materials meet the stringent demands of modern automotive design.

A novel ML-MCDM-based decision support system for evaluating autonomous vehicle integration scenarios in Geneva’s public transportation

This paper proposes a novel decision-support system (DSS) to assist decision-makers in the ULTIMO project with integrating Autonomous Vehicles (AVs) in Geneva, Switzerland. Specifically, it aids in selecting the best scenario for incorporating AVs into Geneva’s public transportation system. The proposed DSS is architected on a combined integrated framework that includes a machine learning (ML) algorithm, random forest (RF) algorithm, and three novel multi-criteria decision-making (MCDM) algorithms: (1) Modified E-ARWEN (ME-ARWEN) for selecting the best scenario with high sensitivity; (2) Compromiser—Positive, Neutral, Negative (Compromiser-PNN) for extracting weights from stakeholders, considering their preferences and potential conflicts; and (3) Collective Weight Processor (CWP) for deriving weights from expert opinions. Besides the main objective, this article also aims to: (1) Address the gap in practical DSS software within AV-related studies by providing Python codes of the DSS; (2) Develop a highly sensitive and comprehensive MCDM framework to address the project’s needs; and (3) Employ Artificial Intelligence within the DSS to optimize outputs. By the application of the proposed DSS, four scenarios were evaluated: (1) Full integration of AVs; (2) Partial integration; (3) Pilot project in limited areas; and (4) Delayed integration. The analysis identified partial integration as the best scenario for integrating AVs. Furthermore, comprehensive analyses conducted to validate the DSS outputs demonstrated the reliability of the results.

Optimizing public investments: A Sustainable Economic, Environmental, and Social Investment Multi-criteria Decision Model (SEESIM)

One of the most important aspects for the economic and social growth of a country is high efficiency of public investments. This imperative has never been more critical, especially in the context of Europe and Italy, where the integration of environmental, economic, and social goals is essential to address the multifaceted challenges of the 21st century. This research introduces the Sustainable Economic, Environmental, and Social Investment Model (SEESIM), a novel multi-criteria decision-making framework designed to ensure the sustainability of projects as their validity over time, especially from a National Recovery and Resilience Plan perspective. Utilizing the Analytic Hierarchy Process (AHP), SEESIM integrates environmental, economic, and social criteria as useful tool to evaluate a project in the post-funding phase, ensuring a balanced approach to efficiency and sustainability. The model is adaptable to various regional contexts, providing a transparent and replicable methodology for assessing the impacts and costs of public investments. An experimental scenario demonstrates SEESIM’s utility in guiding investment decisions, highlighting its potential to significantly enhance the effectiveness of public resources in achieving long-term sustainability goals. SEESIM represents a pivotal advancement in sustainable development strategies, offering a comprehensive tool for integrating multi-dimensional sustainability criteria into public investment decisions.