Multi-criteria Decision-making Tool Research Articles

A multicriteria decision-making model for risk management in an integrated management system

PurposeThis paper proposes a multicriteria model for risk management to identify and assess risks associated with an integrated management system (IMS). The main benefit of the proposed model is its systemic and logical visualization, which may facilitate the understanding of this proposal’s practical application.Design/methodology/approachThe research design consists of four stages: (1) conduct a literature review to establish risk management models in IMS; (2) collect data concerning risk management models in IMS from a large multinational automotive company; (3) propose a multicriteria model to define and assess risks as well as prioritize mitigation actions and (4) apply the proposed multicriteria model to the data collected in case-based research to evaluate the practical viability of the model to contribute to methods traditionally used.FindingsThe results showed that the proposed risk management model contributes to more reliable decision-making in an IMS. The application of the proposed model identified 85 risks in the total processes of the IMS, 31 of which were classified as high risk; thus, priority actions to be taken were defined. The risk classification and prioritization facilitated the implementation of measures to mitigate or eliminate risks, as pointed out by the company managers.Research limitations/implicationsOne of the limitations is the fact that specific knowledge is required to maintain and update the multicriteria decision-making tool used in this study. Another one implies the approach to managing risks under the different ISO standards and sector-specific requirements, since this may require updates and customization of the proposed risk management model.Practical implicationsThe implementation of IMS in contemporary business environments can be supported by a robust risk management approach. In addition, it provides the leadership with a holistic view of multiple aspects related to a company and fosters continuous improvement.Social implicationsThe social implications of this study are assessed indirectly. This study contributes to the improvement of company management models.Originality/valueTraditionally, the methods used for risk management in IMS are usually applied independently of techniques such as failure mode and effect analysis. The model developed in this work enables to manage risks continuously to achieve a systemic view of organizational issues and greater transparency of the processes.

Development of a total quality management framework for the mobile network operations Centre

Telecommunication is one of the most fast-paced and rapidly changing sectors globally. This study develops a total quality management (TQM) framework for mobile network operation centres (NOC) to aid in network surveillance and fault management processes for a 5G transmission network. A systematic literature review was conducted using academic articles in journals and conference papers sourced from the EBSCOHost database. A fuzzy analytic hierarchical process (FAHP) was used as a multi-criteria decision-making tool to determine which criteria and factors are of the highest ranking for the development of the TQM framework. Data collection and control sheets were ranked higher, making them critical to the framework. This shows that, without good data, no problems can be identified, visualised, analysed, and solved and processes will not be improved. Process improvement is ranked last, showing it is of the lowest priority compared to the other criteria. This however, does not imply that in TQM it is of lower priority, as it is needed to keep meeting the needs of the customers. The framework includes how the TQM tools and techniques can be used in collecting, visualising, analysing, solving problems, and improving processes.

RESIDE: REPRESENTATIVE DATABASES FOR MULTI-CRITERIA DECISION TOOL

RESIDE is a software that allows designers and decision-makers to simultaneously consider issues related to energy consumption for environmental conditioning and water heating, construction costs and public policies from the initial planning stages of residential developments. However, its use is conditional on the existence of a database for the context analyzed. Understanding the growing importance of addressing the innate complexity in sustainability-oriented design activities, this article presents a procedure to identify representative databases for the state of Minas Gerais, in order to enable the use of the RESIDE multi-criteria decision tool.

Ecohydrological risk assessment model for the Pra River Basin using GIS and multi-criteria decision making

ABSTRACT The Pra River Basin in Ghana is a major source of ecological, cultural, and economic benefits. However, it has been experiencing degradation, and logistical constraints have limited the implementation of Integrated Water Resources Management. This study presents a GIS-based ecohydrological risk model that identifies ecohydrological units with the highest degradation risk. First, the PRB was divided into 25 eco-hydrological units. Second, seven drivers of degradation were identified and mapped. The drivers were reclassified into five categories with risk scores of 1–5, with 5 indicating higher risk scores. The variables were ranked using the Analytical Hierarchy Process, the degradation risk model was calculated using the weighted sum method, and the multi-criteria decision tool was used to identify the ecohydrological units with the highest degradation risk. Of the 25 ecohydrological units delineated, the Buama, Lower Offin, Ayensu, and Oda ecohydrological units were found to be at the highest risk of degradation due to high artisanal small-scale gold mining, high population density, low forest/vegetation cover, and high encroachment in waterways. The results can be used as a guide to prioritize management intervention, starting with the areas classified as high risk and moving down to those classified as moderate risk.

Managing transit-oriented development: A comparative analysis of expert groups and multi-criteria decision making methods

A key challenge for transport managers and planners in sustainable development is evaluating transit facilities' performance. While Multi-Criteria Decision-Making (MCDM) tools are often used, they can be influenced by experts' subjective biases. This study applies MCDM to assess Mass Rapid Transit (MRT) stations in Jakarta, Indonesia, focusing on Transit-Oriented Development (TOD). The primary goal is to compare stakeholder perspectives and MCDM methods, complemented by a sensitivity analysis and validation with real-world smart card data. The findings reveal significant differences in criteria weighting between Indonesian and non-Indonesian experts, and between academic and non-academic experts, especially in transit connectivity and land use diversity. The study also shows variations in station rankings across different MCDM methods. Sensitivity analysis identifies transit connectivity as the most critical criterion. Simple Additive Weighting (SAW) with linear normalisation aligns well with actual usage data and shows robustness in sensitivity analysis, making it the most reliable method for TOD evaluation. The study highlights the need for continuous TOD performance monitoring and the regular collection of real-world data on ridership and TOD indicators.

Logistics supply of the eco-industrial parks’ residents

This paper provides a comprehensive analysis of the logistics supply for residents of eco-industrial parks and examines its influence on the competitiveness and sustainability of these parks. Eco-industrial parks, as integral components of circular economy systems, facilitate the efficient use of resources, reduction of environmental impact, and optimization of supply chain operations. The purpose of the paper is to assess the logistics systems within eco-industrial parks, identify the challenges and opportunities for enhancing operational efficiency, and explore how these strategies can be adapted to the Ukrainian context, particularly during the ongoing war. The research draws upon international case studies, focusing on the integration of advanced technologies and green logistics practices in China, Europe, and other regions. The methodology used in this paper combines inductive analysis of literature, comparative analysis, and empirical research based on existing models of logistics management within EIPs. A key part of the analysis involves the evaluation of institutional pressures and their role in promoting sustainable supply chain management. The paper examines how these practices, when applied in a coordinated and efficient manner, help to reduce costs and carbon emissions, while also improving resource-sharing between companies located within eco-industrial parks. The results show that implementing circular supply chain management significantly enhances the performance of companies operating within EIPs. Firms that adopt renewable energy sources and optimize their transportation systems using electric vehicles report a 13-14% reduction in operational costs and carbon emissions. The integration of technologies such as Geographic Information Systems and Multi-Criteria Decision-Making tools has also proven beneficial in the site selection and planning of eco-industrial parks, allowing for the optimization of logistics and resource allocation. In Ukraine, which is currently facing critical challenges due to war, adopting these strategies can strengthen industrial clusters, reduce dependence on external resources, and enhance resilience against economic shocks. The scientific novelty of this research lies in the proposed structure of the logistics supply for eco-industrial parks’ residents. The practical implications of this paper include the application of green logistics technologies, the development of self-sustaining energy networks, and the implementation of closed-loop supply chains that promote resource efficiency. Future research directions should focus on the development of logistics optimization models for Ukrainian eco-industrial parks, particularly in war-torn areas, and the assessment of their long-term economic and environmental impact. Additionally, the paper advocates for the expanded use of renewable energy sources and Geographic Information Systems-based planning tools to further improve logistics processes and reduce the ecological footprint of industrial activities. The results of this paper underscore the importance of inter-firm cooperation and the strategic use of logistics to foster both economic and environmental resilience in eco-industrial settings.

Selection of Marketing Staff Using Simple Additive Weight and VIKOR Algorithm

In today’s rapidly evolving technological landscape, decision-making processes within organizations are increasingly relying on advanced computational methods to enhance efficiency and accuracy. This is particularly relevant in human resource management, where selecting suitable candidates for key positions is critical. Traditional methods of staff recruitment often rely on subjective assessments, which may lead to biases and inconsistencies. To address these challenges, this study proposes the use of the Simple Additive Weighting (SAW) and VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) algorithms as multi-criteria decision-making tools for selecting marketing staff. The SAW method offers a straightforward approach by assigning weighted scores to various criteria. In contrast, the VIKOR method provides a ranking system that considers ideal and compromise solutions for candidate selection. Integrating these two algorithms makes the selection process more objective and data-driven, reducing the risk of human error and improving overall decision quality. This paper outlines implementing the combined SAW-VIKOR model in the marketing staff recruitment process, highlighting its potential to optimize candidate evaluation and selection. The results demonstrate that utilizing these algorithms enhances the decision-making process, leading to better alignment of selected staff with organizational goals. This approach is valuable for organizations looking to leverage technology in their recruitment strategies.

Parameter selection for long short-term memory networks with multi-criteria decision-making tools: an application for G7 countries stock market forecasting

Parameter selection for long short-term memory networks with multi-criteria decision-making tools: an application for G7 countries stock market forecasting

Simulation Framework for Substation Siting Integrating Load, Land Use, Neighborhood, and Cost Analysis

This study presents a comprehensive framework for substation siting to address power demand and urbanization challenges. It integrates economic, social, and environmental dimensions to ensure reliable power supply systems. Using simulation techniques, the paper evaluates substation placement by analyzing load distribution, land use, neighborhood satisfaction, and construction costs. The Analytical Hierarchy Process (AHP) assesses load distribution, while a hierarchical clustering algorithm maximizes land use efficiency. Neighborhood satisfaction is measured using hierarchical analysis and fuzzy logic. Construction costs are optimized via a genetic algorithm. These simulations formulate a multi-criteria decision-making tool, proposing an optimized siting strategy balancing technical, socio-economic, and environmental considerations. The framework enhances the accuracy of substation siting studies and provides practical implementation guidance, offering new insights and refined methodologies for modern power system planning and development.

Creating a fuzzy DEMATEL-ISM-MICMAC -fuzzy BWM model for the organization's sustainable competitive advantage, incorporating green marketing, social responsibility, brand equity and green brand image

This study aimed to create a model that would enable the achievement of a sustainable competitive advantage. The research approach is combining four multi-criteria decision-making tools including Fuzzy DEMATEL, ISM, MICMAC Analysis and Fuzzy BWM to evaluate factors in four key areas included corporate social responsibility, green marketing, brand image, and brand equity in Unilever Corporation. These factors collectively contribute to enhancing competitiveness. The process begins with the fuzzy DEMATEL method, which helps to uncover the cause-and-effect relationships among various components. Following this, the ISM approach is utilized to construct a hierarchical model that clarifies how these components influence and depend on one another. This step is crucial for gaining insights into their collective impact on Unilever's sustainability goals. Next, MICMAC analysis categorizes these factors based on their levels of influence and dependence, pinpointing the most critical elements that drive competitive advantage. Finally, the fuzzy BWM technique is applied to prioritize these key factors, leading to the aimed at bolstering Unilever's long-term competitiveness by fostering a green and sustainable brand image that resonates with consumer values. The key results reveal that the highest priority was given to achieving sustainable development goals. The criterion "Promotion of customer awareness" secured the second position Finally, ensuring the quality of products and services was also recognized as a third key focus area. Identifying these key factors and their interrelationships will provides decision-makers and stakeholders in Unilever company with actionable insights for precise planning and strategic prioritization.

Spatial prioritization of groundwater resources in mountain region of Nepal: a GIS and AHP integrated framework

ABSTRACT Climate change and anthropogenic activities pose major challenges to water resource management in water-scarce mountain regions. Managing and monitoring groundwater effectively is crucial in mitigating these challenges. Understanding groundwater resources and their distribution is necessary for effective management. The present study covers Doramba rural municipality of Nepal. It employed the analytic hierarchy process (AHP) as a multi-criteria decision-making (MCDM) tool integrated with remote sensing (RS) data and geospatial techniques to identify groundwater potential zones (GPZs). The parameters for analysis include drainage density, slope, rainfall, lineament density, land use/land cover (LULC), soil, geology, curvature, roughness, topographic wetness index, and topographic position index. These 11 thematic layers of the study area were prioritized using Satty's AHP method for the delineation of GPZs. The results classified the study area into very high, high, moderate, low, and very low groundwater potential classes, covering 26.71, 27.98, 46.61, 34.71, and 4.89 km2, respectively. The delineated map accuracy was confirmed with a 70.7% prediction rate based on the spatial distribution of 56 springs in the study area. The study's results can be applied to the sustainable management of groundwater resources in the study region and other areas with similar hydrogeological conditions.

Dynamic grey relational analysis-based supplier selection in a health-care unit

Purpose In the present-day highly customer-conscious service environment, supply chain management has become a critical component of health-care industry, helping in fulfilling patient expectation, optimizing inventory and automating departmental activities. Supplier selection is one of the crucial elements of health-care supplier chain, establishing mutually beneficial relationships with the reliable suppliers that provide the most value of money. Health-care supplier selection with feasible sets of alternatives and conflicting criteria can be treated as a multi-criteria decision making (MCDM) problem. Among the MCDM methods, grey relational analysis (GRA) appears as a potent tool due to its simple computational steps and ability to deal with imprecise data. The purpose of this paper is to explore the applicability of a newly developed MCDM tool for solving a health-care supplier selection problem. Design/methodology/approach In GRA, the distinguishing coefficient (ξ) plays a contributive role in final ranking of the alternative suppliers and almost all the past researchers have considered its value as 0.5. In this paper, a newly developed MCDM tool, i.e. dynamic GRA (DGRA), is adopted to evaluate the relative performance of 25 leading pharmaceutical suppliers for a health-care unit based on nine important financial metrics. Instead of static value of ξ, DGRA treats it as a dynamic variable dependent on grey relational variator and ranks the health-care suppliers using their computed rank product scores. Findings Based on rank product scores and developed exponential curve, DGRA classifies all the suppliers into reliable, moderately reliable and unreliable clusters, helping the health-care unit in identifying the best performing suppliers for subsequent order allocation. Among the reliable suppliers, alternatives A2 and A11 occupy the top two positions having almost the same performance with respect to the considered financial metrics. Originality/value Application of DGRA along with determination of the most reliable suppliers would help in effectively adopting multi-sourcing strategy to increase resilience while diversifying the supply portfolio, thereby enabling the health-care unit to minimize chances of sudden disruption in the supply chain. It can act as an intelligent decision-making framework aiding in solving health-care supplier selection problems considering perceived risks and dynamic input data.

Optimizing Selection of Sustainable Material-extraction Sites with GIS-based Decision-support Systems (AHP/MACBETH): In-depth Case Study from Settat, Morocco

In today’s world, sustainability is crucial, particularly in managing natural resources for sustainable development. Often, environmental impacts of new activities are overlooked, worsening the effects on already vulnerable environments. Our study uses two multi-criteria decision-making tools, MACBETH and AHP, integrated with GIS technology, to evaluate quarry sites in Settat Province, Morocco. This methodology classifies sites by suitability, based on criteria aligned with sustainable development goals.We found a high consistency between MACBETH and AHP, with less than 4% divergence in criteria weighting, confirming the robustness of our integrated approach in making location-based decisions for quarries. The resulting maps clearly delineate zones from unsuitable to highly suitable for quarrying. Beyond assessing current quarrying practices, our findings offer strategic insights for future site planning and establishment, showcasing adaptability and potential for replication. This research provides a practical model for improving material extraction practices, aligning with sustainable development needs, and serving as a valuable tool for policymakers and industry stakeholders.

Multifaceted multi-criteria decision making framework to prioritise the electric two-wheelers based on standard and regional driving cycles

The primary objective of the carried-out research work is to utilise the multi-criteria decision-making (MCDM) techniques to rank the top-selling electric two-wheeler (E2W) models based on the different performance features stated by original equipment manufacturers and estimated through standard and regional driving cycle (DC) analysis. First and foremost, the vehicle structural, powertrain, performance features, and cost component-related data of eight top-selling E2W models (M1–M8) are gathered from the official websites of OEMs and local dealers. Then, performance features like energy consumption per km (EC/km), driving range (DR), well-to-wheel (WTW) CO2 emission, total cost of ownership (TCO), and total cost of ownership per km (TCO/km) are estimated under standard and regional DCs through the developed energy consumption and TCO models. Further, the ten different MCDM tools are utilised for prioritising the E2W models under each DC with the attributes’ weights estimated from the entropy methods. To enhance the trustworthiness of ranking results, sensitivity analysis is done using six distinct combinations of attribute weights. Moreover, the M7 E2W is identified as the best E2W by utilising Borda Count to integrate all the ranking results. Overall, this study provides an in-depth understanding for users about how to select E2W with minimal effort.

Delineation of the Groundwater Potential Zones using Integrated Geospatial Techniques and Multi-criteria Decision Approach in the Cretaceous Lower Benue and Niger Delta Basins of Abia State, Southern Nigeria

Delineation of the groundwater potential zones in the Cretaceous Lower Benue and Niger Delta basins of Abia state, Nigeria with geographic information system (GIS) / remote sensing (RS) multi-criteria decision approach (MCDA) considered the significant hydrogeomorphological factors affecting the availability of groundwater resources. The factors (geology, rainfall, slope, drainage density, lineament density, soil type, and land use land cover) were ranked using the analytical hierarchical process, a multi-criteria decision-making tool. The integration of the factors into a geographic information system (GIS) and weighted overlay analysis were then carried out using ArcGIS software. The result indicates 31.5% of zones of high to very high groundwater occurrence are in the southern section, and 41.6% of poor to very poor zones in the northern section. While the moderate zones make up the 26.8% of the study area. The study demonstrates that the groundwater occurrence in the study area is primarily controlled by the interactive impact of geology, rainfall, and slope factors, thus revealing the causes of the incessant random borehole failures in the moderate zones. Thus, the groundwater resources can be sustainably exploited, developed, and managed with the help of the map produced from the resulting demarcation of groundwater potential zones in the study.

Delineation of the Groundwater Potential Zones using Integrated Geospatial Techniques and Multi-criteria Decision Approach in the Cretaceous Lower Benue and Niger Delta Basins of Abia State, Southern Nigeria

Delineation of the groundwater potential zones in the Cretaceous Lower Benue and Niger Delta basins of Abia state, Nigeria with geographic information system (GIS) / remote sensing (RS) multi-criteria decision approach (MCDA) considered the significant hydrogeomorphological factors affecting the availability of groundwater resources. The factors (geology, rainfall, slope, drainage density, lineament density, soil type, and land use land cover) were ranked using the analytical hierarchical process, a multi-criteria decision-making tool. The integration of the factors into a geographic information system (GIS) and weighted overlay analysis were then carried out using ArcGIS software. The result indicates 31.5% of zones of high to very high groundwater occurrence are in the southern section, and 41.6% of poor to very poor zones in the northern section. While the moderate zones make up the 26.8% of the study area. The study demonstrates that the groundwater occurrence in the study area is primarily controlled by the interactive impact of geology, rainfall, and slope factors, thus revealing the causes of the incessant random borehole failures in the moderate zones. Thus, the groundwater resources can be sustainably exploited, developed, and managed with the help of the map produced from the resulting demarcation of groundwater potential zones in the study.

An Agile Approach for Adopting Sustainable Energy Solutions with Advanced Computational Techniques

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policymakers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision-Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale, but the findings also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

Multicriteria decision tools for selection of sustainable integrated retrofits: application to the seismic and energy upgrade of a masonry building

The design of sustainable integrated retrofits is a new frontier in civil engineering that aims to minimize economic and environmental impacts of interventions on existing buildings. The enhanced methodology presented in this paper combines seismic and energy performance metrics with economic and environmental indicators associated with each integrated retrofit. The purpose is to identify the optimal retrofit through histograms, radar charts, proficiency ratios and iso-class plots, as the one corresponding to the higher seismic and energy performances and the minimum economic and environmental impacts. Such a methodology can be applied to either the installation phase of the retrofit or its entire life cycle, making it possible to compare these two scenarios in a long-term perspective. The methodology, generally valid for any building materials or structural typologies, is here applied to a two-story natural-stone masonry building, damaged by the 2016 Central Italy earthquake sequence.

Identifying and Prioritizing Economic Opportunities for Medical Tourism Management: An Analytic Hierarchy process

Background: As a growing industry, medical tourism includes many economic opportunities. This industry is the largest and most diverse industry in the world, with a foreign currency income which is equal to the automotive and military weapons industries. The aim of the current research is to identify and prioritize economic opportunities related to medical tourism management, using the Analytical Hierarchy Process (AHP) as a multi-criteria decision-making tool. Methods: This was a descriptive-survey research with an applied and cross-sectional approach. A survey method was used to collect data, so this study can be classified as field research. The statistical population of the research included 25 people with experience and expertise selected through purposive sampling in the fields of management, tourism, and medicine. The method of collecting data was in the form of library and field studies. The tool for collecting data was a questionnaire of paired comparisons and a check sheet. Data analysis was done with AHP method and Expert Choice 11 software. After collecting the paired comparison questionnaire and calculating the geometric mean, the inconsistency rate of the questionnaires was less than 0.1, which showed that the data had the required validity. Results: First, using library studies, economic opportunities related to medical tourism were identified. These opportunities included cash generation, technological advancements, job creation, diversification of tourism industry, development of health care infrastructure, knowledge transfer and cooperation, services and ancillary industries, and regional development and diversification of economic activity. Opportunities were prioritized using the AHP method. Cash generation with relative importance of 0.191 was ranked first, job creation with relative importance of 0.162 was second, and technological advances was ranked third with relative importance of 0.131. Diversification to economic activities with a relative importance of 0.45 was ranked ninth which was the lowest. Conclusion: Strategic marketing, delivering quality healthcare services, competitive pricing, specialized medical care, patient-centered experience, collaboration and partnership, continuous improvement, and strengthening medical tourism infrastructure are the pillars through which medical tourism management can not only generate sustainable revenue but also provide invaluable healthcare services to patients from across the globe.

Generalization of Fermatean Fuzzy Set and Implementation of Fermatean Fuzzy PROMETHEE II Method for Decision Making via PROMETHEE GAIA

The Fermatean fuzzy set, in contrast to other generalizations of fuzzy sets like PFS and IFS, has a wide range of acceptance for both MF and NMF. In light of this, the Fermatean fuzzy set performs as an efficient, flexible, and comprehensive representation in situations that lack certainty. Here, the weaker forms of Fermatean fuzzy sets are introduced, and their traits are analyzed. Decomposition and continuity of the Fermatean fuzzy α-open set are also accustomed. With the goal of safeguarding our green environment, hiring the best supplier is of the utmost significance in the construction industry. Using outranking techniques, Visual PROMETHEE Academic Edition 1.4 is a live multi-criteria decision aid software program. It runs virtual analysis through GAIA and applies selected criteria to contrast parameters. It also saves them for possible export and editing. In this article, the PROMETHEE II method is applied for Fermatean fuzzy numbers with FF(α,β)-level for selecting the optimal green supplier for a construction company. Because of its ability to handle vagueness, the FF PROMETHEE II method emerges as a valuable tool in Multi-criteria decision making. Furthermore, this study assesses the efficacy of the proposed technique by comparing its results with those obtained through other established methods.