Multi-criteria Decision Analysis Methods Research Articles

Elevating performance and interpretability of in silico classifiers for drug proarrhythmia risk evaluations using multi-biomarker approach with ranking algorithm.

Using electrophysiological simulations and machine learning to predict drug proarrhythmia risk has gained popularity due to its effectiveness. The leading in silico drug assessment system mainly uses a single biomarker (qNet) to predict proarrhythmia risk, offering good performance and straightforward interpretation. Other advanced classifiers incorporating additional physiological biomarkers provide better predictive capabilities but are less intuitive. Thus, a method that accommodates multiple biomarkers while maintaining interpretability is needed. We enhance the current best ordinal logistic regression (OLR) model by adding more physiological biomarkers to overcome its limitations. We also introduce a general torsade metric score (TMS) for multi-biomarker approaches to facilitate easier interpretation. Additionally, a novel ranking algorithm based on a simple multi-criteria decision analysis method is employed to evaluate various classifiers against standard proarrhythmia risk criteria efficiently. Our proposed method demonstrates that using multiple well-known biomarkers yields better performance than using qNet alone. Some accepted multi-biomarker OLR models do not incorporate qNet yet outperform those that do. Moreover, some ill-performing biomarkers when utilized individually can show improved performance in combination with other biomarkers. The proposed approach offers an effective way of utilizing multiple biomarkers, including well-known ones, providing practical alternatives for proarrhythmia risk assessment. The interpretability of the accepted models is straightforward, thanks to the TMS thresholds for multi-biomarker OLR models that allow direct evaluation of the classification prediction of individual drugs.

Multi-criteria decision analysis for monitoring and evaluating soil erosion risk in forest fire-affected areas.

Wildfires lead to socio-economic and environmental impacts. These impacts include hydrological instability, which can cause severe damage, especially where infrastructures are present. Post-rehabilitation measures can be useful in reducing or preventing erosion or hydrogeological risks. Decision-makers are called on to prioritize post-fire intervention areas and allocate public funds for this purpose. This work focuses on the assessment of erosion and hydrological risk potential in forested slope areas affected by wildfire using a Multi-Criteria Decision Analysis (MCDA) approach integrated with a GIS environment on a regional scale. Expert perception was considered using the pairwise comparison method as part of the Analytical Hierarchy Process (AHP). This allows expert stakeholders to rank relevant criteria, providing a quantitative metric (weight) for qualitative data. Two MCDA methods are used and compared: Weighted Linear Combination (WLC) and Ordered Weighted Averaging (OWA). Fire frequency, slope (gradient and length), and proximity to infrastructures were found to be the most important factors by the stakeholders. The WLC method provides evidence classified into high and moderate suitability class areas characterized by high values for fire frequency or slope gradient. Conversely, the OWA method, ranging from low to high risks, makes it possible to adapt the method and obtain a range of suitability maps. Novelties of the MCDA-GIS combined methodology adopted in this work are its application on a regional scale and the combination of vulnerability and driving-force factors (namely presence of grey infrastructures, fire frequency). The MCDA-GIS methodology can be suitable for public administrations in that it allows for mapping a regional area more quickly and thus facilitates sector planning.

The Application of Machine Learning and Deep Learning with a Multi-Criteria Decision Analysis for Pedestrian Modeling: A Systematic Literature Review (1999–2023)

Among transportation researchers, pedestrian issues are highly significant, and various solutions have been proposed to address these challenges. These approaches include Multi-Criteria Decision Analysis (MCDA) and machine learning (ML) techniques, often categorized into two primary types. While previous studies have addressed diverse methods and transportation issues, this research integrates pedestrian modeling with MCDA and ML approaches. This paper examines how MCDA and ML can be combined to enhance decision-making in pedestrian dynamics. Drawing on a review of 1574 papers published from 1999 to 2023, this study identifies prevalent themes and methodologies in MCDA, ML, and pedestrian modeling. The MCDA methods are categorized into weighting and ranking techniques, with an emphasis on their application to complex transportation challenges involving both qualitative and quantitative criteria. The findings suggest that hybrid MCDA algorithms can effectively evaluate ML performance, addressing the limitations of traditional methods. By synthesizing the insights from the existing literature, this review outlines key methodologies and provides a roadmap for future research in integrating MCDA and ML in pedestrian dynamics. This research aims to deepen the understanding of how informed decision-making can enhance urban environments and improve pedestrian safety.

Analyzing customer preferences for hydrogen cars: a characteristic objects method approach

As hydrogen vehicles gain popularity, car manufacturers are introducing numerous models, presenting customers with the challenge of choosing the most suitable option. To address this, Multi-Criteria Decision Analysis methods are often used to evaluate and select the best alternative. This study applies the Characteristic Objects Method (COMET) to address the practical problem of selecting the most appropriate hydrogen car for decision-makers. Using data provided by manufacturers, we evaluate ten hydrogen vehicles and create six decision models based on the preferences of three decision-makers, utilizing both the recently proposed Triad Support and Expected Solution Point-COMET algorithms. The models provide insights into how customer preferences can be extracted and represented in decision models. Moreover, we analyze local weights derived from the models to understand customer expectations for hydrogen cars better. The results of our study highlight the effectiveness of the COMET approach in capturing and comparing decision-maker preferences, offering a valuable methodology framework for future applications in similar multi-criteria decision-making problems.

Governance of responsible research and innovation: A social welfare, psychologically grounded multicriteria decision analysis approach

Our article deals with the governance of responsible research and innovation (RRI) and aims to set out a first psychologically grounded decision-theoretic method for the governance of RRI. We approach the governance of RRI as a multicriteria group decision analysis problem of delivering social welfare in an innovation ecosystem. Following such a methodological approach, we develop a psychologically grounded multicriteria group decision analysis method that integrates in its value function the main psychological effects captured in the value function of prospect theory as the main theory of individual decision-making under risk. The method first applies a psychologically motivated multicriteria decision analysis function that measures the welfare delivered to all stakeholders involved in a research and innovation consortium. The method then applies a social welfare function on the welfare measurements of stakeholders to propose a social welfare solution that emerges as an RRI-compliant solution for the consortium. The results are a first psychologically grounded multicriteria group decision analysis method and its first application to the governance of RRI. The implications of our results are theoretical but also practical, as our method contributes not only to the established field of multicriteria decision analysis by setting out new method but also to the field of RRI by delivering a psychologically grounded decision-theoretic general method for the governance of RRI.

Fuzzy RANCOM: A novel approach for modeling uncertainty in decision-making processes

Multi-Criteria Decision Analysis (MCDA) methods are essential for addressing real-world challenges in the dynamic field of decision support technology. As technology advances, the expanding variety of multi-criteria evaluation methods highlights the critical importance of method selection for reliable results. These methods can be applied to scenarios involving both precise data and uncertainties. Expert knowledge is relevant in navigating real-world decision problems, particularly in uncertain environments where judgment inaccuracies can create challenges. Determining criteria weights is a pivotal step, as it significantly influences final outcomes and provides a nuanced understanding of each factor's impact on the evaluation.This paper addresses the pressing need for methods that account for uncertainty and effectively reduce the impact of inaccuracies in expert judgment. To this end, we introduce the Fuzzy RANking COMparison (RANCOM) method, a novel fuzzy subjective weighting approach that extends the advantages of the original RANCOM method to manage uncertainty. Using Triangular Fuzzy Numbers (TFNs), the proposed extension maintains simplicity, rapid execution, and flexibility in establishing criteria relationships. Key contributions of this paper include the development of the Fuzzy RANCOM method and a comparative analysis with the Fuzzy Analytical Hierarchy Process (AHP) method. Through multiple simulations, the study examines the operational dynamics of these methods, offering a valuable tool for decision-makers, especially in contexts where expert judgments may be prone to inaccuracies. The development of the Fuzzy RANCOM method provides a robust solution, equipping decision-makers with an effective tool to determine criteria weights while minimizing the impact of potential judgment errors.

Multi-Criteria Decision Analysis for healthcare decisions: a bibliometric study

Healthcare has a direct impact on human life and involves risk factors, uncertainties and preferences. The complexity and implications of actions in this area and the demand for rational and transparent decision processes has resulted in the widespread use of Multi-Criteria Decision Analysis (MCDA) methods. As such, this study aimed to conduct a bibliometric analysis of the literature on the use of MCDA in healthcare, as well as analyzing the reasons presented in the articles for selecting MCDA methods, when applicable. The study analyzed 195 articles in the Scopus and PubMed databases between 1991 and 2019. The results were presented in graphs that focused on the main objectives of the study, highlighting the fact that only 28% of the articles presented justifications for the methods chosen. Many of the justifications were not well founded. It was concluded that care is needed to ensure appropriate selection of multi-criteria methods.

Assessing raw material efficiency and waste management for Sustainable Development: A VIKOR and TOPSIS Multi-Criteria Decision Analysis

Abstract This study explores the integration of sustainability in decision-making processes within a steel manufacturing company in Poland. As global clients increasingly demand sustainable practices, companies must adapt their operations to meet these expectations. We applied Multi-Criteria Decision Analysis (MCDA) methods, specifically TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) and VIKOR (in Serbian: VlseKriterijuska Optimizacija I Komoromisno Resenje), along with three weighting techniques, namely Entropy, Standard Deviation, and CRITIC (Criteria Importance Through Inter-criteria Correlation), to evaluate the sustainability of four products. Nine criteria were considered, including material type, corrosion protection, surface treatment, and various manufacturing processes. Our findings reveal that the MCDA framework effectively ranks products from most to least sustainable, highlighting the importance of raw material efficiency and waste management. This research demonstrates the practical application of MCDA methods in assessing sustainability within the steel industry, providing a basis for future studies to extend this framework to other manufacturing sectors and regions. Overall, this approach supports informed decision-making, aligning with broader sustainability goals while satisfying the demands of business partners and clients.

Risk Estimation of Surface Water Pollution in Vam Co Tay River Based on Remote Sensing Data and Multi-Criteria Decision Analysis Methods

Risk Estimation of Surface Water Pollution in Vam Co Tay River Based on Remote Sensing Data and Multi-Criteria Decision Analysis Methods

Multi-centre modified Delphi exercise to identify candidate items for classifying early-stage symptomatic knee osteoarthritis

To generate a list of candidate items potentially useful for discriminating individuals with Early-stage Symptomatic Knee Osteoarthritis (EsSKOA) from those with other conditions and from established osteoarthritis (OA), and to reduce this list based on expert consensus. We conducted a three-round online international modified Delphi exercise with OA clinicians and researchers ("OA experts"). In Round 1, participants reviewed 84 candidate items and nominated additional item(s) potentially useful for EsSKOA classification; those nominated by ≥3 participants were added. In Round 2, participants rated perceived usefulness of 108 items (1 [not at all useful] to 9 [extremely useful]). In Round 3, participants could revise their ratings after reviewing Round 2 group median and quartiles. Following Round 3, we retained items with a median usefulness score >5 and ≥33.3% of participants categorised the item as useful (7 to 9), overall and in subgroup analysis by clinician field. There were 128 participants in Round 1 and 113 (88%) completed all rounds. We retained 77 items that spanned multiple domains (demographics, symptoms, physical exam, performance-based measures, imaging, laboratory investigations, and gross inspection/arthroscopy). Highly rated items included (median usefulness score): prior knee joint injury (8), diagnosis of OA in a different joint (7), and activity-related knee pain (7). The interquartile range was most often 3. We identified 77 items that OA experts consider potentially useful for EsSKOA classification. The results highlight experts' uncertainty around item usefulness. Next, candidate items will be further assessed and reduced using data-driven and multicriteria decision analysis methods.

Toward robust decision-making under multiple evaluation scenarios with a novel fuzzy ranking approach: green supplier selection study case

In the evolving field of decision-making, the continuous advancement of technologies and methodologies drives the pursuit of more reliable tools. Decision support systems (DSS) provide information to make informed choices and multi-criteria decision analysis (MCDA) methods are an important component of defining decision models. Despite their usefulness, there are still challenges in making robust decisions in dynamic environments due to the varying performance of different MCDA methods. It creates space for the development of techniques to aggregate conflicting results. This paper introduces a fuzzy ranking approach for aggregating results from multi-criteria assessments, specifically addressing the limitations of current result aggregation techniques. Unlike conventional methods, the proposed approach represents rankings as fuzzy sets, providing detailed insights into the robustness of decision problems. The study uses green supplier selection as a case study, examining the performance of the introduced approach and the robustness of its recommendations within the sustainability field. This study offers a new methodology for aggregating results from multiple evaluation scenarios, thereby enhancing decision-maker awareness and robustness. Through comparative analysis with traditional compromise solution methods, this paper highlights the limitations of current approaches and indicates the advantages of adopting fuzzy ranking aggregation. This study significantly advances the field of decision-making by enhancing the understanding of the stability of decision outcomes.

The Use of a Multi-Criteria Decision Analysis Method to Select the Most Favourable Type of Fish Pass in Mountainous Areas

Fish passes are a key element enabling the migration of aquatic organisms in the context of restrictions resulting from the presence of weirs. Multi-criteria decision analysis, AHP, and Rembrandt methods were used to assess the effectiveness of fish passes on mountain rivers. Three common types of fish passes were considered: slotted fish pass, block ramps, and a circulation channel with boulders. The results of the study indicated that block ramps proved to be the most favourable solution, achieving the highest preference values in both methods (Rembrandt: 0.77, AHP: 0.63). The key factors influencing the effectiveness of the fish passes are the availability of space and the water requirements, which reached values of 0.38 and 0.27 in the Rembrandt method and 0.33 and 0.28 in the AHP method, respectively. The differences between the results of both methods were minimal and did not have a significant impact on the final choice. The discussion emphasised the advantage of nature-like fish passes, such as block ramps, which better preserve the ecological continuity of rivers and can be more easily adapted to local hydrological conditions. The study also indicated the need for continuous monitoring of the fish passes and their optimisation to reduce problems related to sedimentation and flow blocking. The obtained results can provide a valuable basis for decision making in the planning and construction of fish passes, especially in demanding mountainous conditions, contributing to improving the effectiveness of fish migration and minimising negative impacts on the natural environment.

Sustainability assessment of products of the tropical tree moringa in Ghana with a focus on small-scale producers

With their numerous products and uses, multifunctional crops offer an attractive means for improving smallholder farmer livelihoods. This study applies a Life Cycle Sustainability Assessment (LCSA) to Moringa oleifera (moringa), a multifunctional crop with diverse applications in nutrition, cosmetics, and water treatment. The LCSA includes an Environmental Life Cycle Assessment (ELCA), Social Life Cycle Assessment (SLCA), and Life Cycle Costing (LCC). Surveys were conducted with 58 smallholder farmers and five moringa processors in Ghana. The ReCiPe 2016 Midpoint method was used for the ELCA. The 10-year Net Present Value (NPV) and Payback Period (PBP) were calculated for farmers and processors in the LCC. The SLCA focused on the Worker stakeholder category, particularly smallholder farmer impacts, including indicators for Next Generation Farming, Inclusiveness, Access to Services, Food Security, and Livelihood. A composite sustainability score was calculated from the ELCA, SLCA, and LCC results using the Characteristic Objects Method (COMET), a multi-criteria decision analysis method resistant to rank-reversal. The study compared five supply chains: leaf-only, leaf-and-seed, leaf-and-seed with seedcake reuse, seed-only, and seed-only with seedcake reuse. Environmental hotspots were identified in leaf and seed collection. Economically, leaf-only cultivation provided the highest 10-year NPV for farmers, while seed-only with seedcake reuse yielded the highest NPV for processors. The leaf-only supply chain had the best PBP for both farmers and processors. Socially, leaf-only cultivators outperformed reference points across all indicators, making it the most socially sustainable supply chain. Our findings highlight that improving market access, organizing seed cultivators into farmer-based groups, and optimizing farm gate product collection can enhance the sustainability of moringa supply chains, offering a model for other multifunctional crops in rural development. This study is the first to integrate LCSA with COMET, a promising approach that could be adopted in other sustainability assessment case studies.

Delineation of Groundwater Potential Zones Using Geospatial Techniques. Case Study: Roman City and the Surrounding Area in the Northeastern Region, Romania

Effective groundwater management is crucial under the current climatic conditions, addressing both qualitative and quantitative aspects. An important step in delineating groundwater potential zones involves remote sensing (RS) data and geographic information systems (GISs), facilitating resource assessment, and the implementation of suitable field data management. This study introduces the delineation of potential groundwater zones using seven layers and the Multi-Criteria Decision Analysis (MCDA) method. Satty’s Analytic Hierarchy Process (AHP) was employed to rank the seven selected parameters, contributing to the advancement of groundwater research and resource assessment. All seven thematic layers (Rainfall, Geology, Land Use/Land Cover, Drainage Density, Elevation, Slope, and Soil) were prepared and analyzed to delineate groundwater potential zones. The resulting groundwater potential zone map was categorized into four classes, Very Good, Good, Moderate, and Poor, covering areas of 81.53 km2 (45.1%), 56.36 km2 (31.2%), 19.54 km2 (10.8%), and 23.17 km2 (12.8%) of the total area, respectively. The accuracy of the output was validated by comparing it with information on groundwater prospects in the area, and the overall accuracy of the method was approximately 72%. High-yield boreholes were drilled and concentrated in the Very Good groundwater potential zones, while low-yield ones were developed in the Poor areas.

7956 Assessing Factors Influencing Polycystic Ovarian Syndrome Using Hybrid Multi-Criteria Decision Analysis

Abstract Disclosure: N.B. Trivedi: None. J. Amin: None. M. Patel: None. S. Patel: None. P. Rana: None. A. Rajani: None. V. Dave: None. Polycystic Ovarian Syndrome (PCOS) is a complex hormonal disorder having multifactorial origins. This study aims to apply Multi-Criteria Decision Analysis (MCDA) methods like Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytical Hierarchy Process (AHP) to prioritize factors influencing PCOS. In our study, 10 criteria impacting PCOS were identified through extensive literature review. AHP and DEMATEL were used via matrices, assessed by 13 experts. AHP determined relative importance/weights while DEMATEL assessed interconnectedness among criteria. AHP revealed that family history held the highest weight (0.22) while Insulin resistance held the lowest weight at 0.04. The results obtained from DEMATEL indicated that factors like obesity, insulin resistance, diet, psychological stress and sedentary lifestyle displayed higher D+R values, suggesting a strong influence on other factors; indicating their significant impact on the syndrome. The division of criteria into cause and effect groups based on D-R values revealed that family history, environmental factors, diet, sedentary lifestyle and psychological stress appear to be causative and sleep disturbances, low-grade inflammation, excess androgen and insulin resistance seem to be the consequences of PCOS. In AHP the criteria are treated as being independent to each other and their hierarchal order is determined. However, in realistic scenarios factors may have some sort of interdependency/relationship with one another. To identify that interdependency which AHP fails to focus on, DEMATEL was used. DEMATEL ensues that Insulin resistance along with obesity, sedentary lifestyle, and psychological stress show strong level of influence and interconnectedness, indicating a significant impact on the syndrome. However, insulin resistance, when treated as an independent criterion in AHP, displays lower weightage. In the end, improved PCOD treatment would come from targeted interventions and strategies that centre on the variables with a higher D+R value. When the criteria were considered independent of each other in AHP, highest weight is conferred to family history (despite of it having moderately low level of influence/interconnectedness on other criteria as ensued from DEMATEL) thus prompting more research in the direction of investigating genetic and familial factors associated with PCOS. This could consequently lead to early diagnosis and effective long term management. The combined methodology (DEMATEL+AHP) ensures comprehensive assessment of PCOS factors. Previous studies might have focused on individual factors separately or failed to adequately explore their interrelationships and relative importance. MCDA can be further employed in healthcare to assess conflicting treatment modalities, risk factors etc. for other multifactorial disorders. Presentation: 6/2/2024

A Fuzzy PROMETHEE Method for Evaluating Strategies towards a Cross-Country Renewable Energy Cooperation: The Cases of Egypt and Morocco

Recognising the urgency of addressing climate change and the imperative to mitigate its effects, the European Union (EU) has embarked on a transformative journey to reshape its energy landscape, with a pronounced emphasis on harnessing renewable energy sources (RESs) and augmenting their contribution to electricity generation. To propel Europe towards sustainable development through its energy transition, EU member states are encouraged to foster collaborative efforts on a European scale, inviting neighbouring countries to participate in joint ventures aimed at leveraging RESs for electricity generation. Consequently, it becomes imperative to evaluate the potential depth of cooperation among these nations, assessing how such partnerships can align with Europe’s overarching objectives while fostering mutually beneficial conditions. This paper seeks to undertake a thorough analysis and evaluation of the potential impacts of such cooperation, both in advancing RES objectives and in promoting broader cooperation goals concerning the countries involved. The appropriate methodological framework has been developed, utilising and implementing the fuzzy PROMETHEE multicriteria decision analysis method, to address the problem’s multidimensional character, intending to implement an appropriate action plan and promote production from RESs. The methodology has been applied to assess alternative strategies in two case study countries, Morocco and Egypt, while important outcomes have emerged towards the successful implementation of cooperation mechanisms promoting RES.

Interdependencies in industry 4.0 maturity: Fuzzy MCDA analysis for open innovation in developing countries

The emergence of Industry 4.0 (I4.0) is reshaping industries worldwide, driven by rapid technological progress and the need for open innovation. This study focuses on understanding the interdependencies of driving factors of I4.0 maturity in developing countries using Fuzzy Multi-Criteria Decision Analysis (MCDA) methods. By analyzing Indonesia, Pakistan, and Venezuela, the research aims to foster open innovation and address the unique challenges these nations face in adopting I4.0 technologies. I4.0 maturity models are essential for evaluating current maturity levels and identifying areas for improvement. However, the complexity and interdependence of various factors—ranging from data science and technology to policy, governance, and open innovation dynamics, such as social open innovation and the role of SMEs—complicate this process. This study employs Fuzzy TOPSIS and Fuzzy DEMATEL to identify the critical factors influencing I4.0 maturity and analyze their interdependencies and prioritization. The results indicate that 'Data and Information' and 'Willingness to Change' are crucial across all countries, while strategic differences between large enterprises and SMEs highlight the need for tailored approaches. This research highlights the importance of continuous IT investment, digital leadership, collaborative ecosystems, and agile strategies in fostering open innovation and driving I4.0 adoption. This research contributes to the theoretical and practical understanding of I4.0 maturity, offering valuable insights for practitioners and academics to explore the dynamic interactions of I4.0 factors and their impact on operational efficiency.

Sustainability Assessment Methods for the Transport Sector Considering the Life Cycle Concept—A Review

This paper presents a summary and review of life cycle sustainability assessment (LCSA) methods for the transport sector. The paper provides a comprehensive overview of articles that employ a variety of methods for assessing sustainable development in the transport sector, taking into account the economic, social, and environmental dimensions. In the sustainability assessment of transport, three methods were evaluated: life cycle assessment (LCA), life cycle cost analysis (LCC), and social life cycle assessment (SLCA). An overview of sustainability assessment methods in transport and a review of the indicators used in the life cycle sustainability assessment was conducted. It was found that the selection of indicators within the LCSA for assessing various aspects of sustainable development is dependent on various geographic and policy contexts. An overview of the application of multi-criteria decision analysis (MCDA) methods to assess LCSA in the transport sector was performed. MCDA methods are used to support decision-making regarding the selection of the most sustainable transport options and allow for the simultaneous consideration of multiple criteria, enabling a more sustainable assessment of different transport options. MCDA methods help to rank alternative transportation fuels and help decision-makers consider indicators encompassing economic, environmental and social aspects.

ASSESSMENT OF CURRENT AND FUTURE LAND DEGRADATION UNDER CLIMATE CHANGE IN THE UPSTREAM OUERGHA WATERSHED (MOROCCO) USING GIS AND AHP METHOD

Land degradation is a worldwide phenomenon that negatively affects the topsoil of agricultural land. The Mediterranean region is the hardest hit by this phenomenon, which is set to increase due to climate change. This study aims to assess the susceptibility of land to degradation under climate change, based on the calculation of the Land Degradation Sensitivity Index (LDI) in the upstream Ouergha catchment in in Northern Morocco (Southern Mediterranean), using Geographic Information Systems (GIS) and the Analytic hierarchy process (AHP) multi-criteria decision analysis (MCDM) method. In this study, 4 main criteria (climate, vegetation, soil and human environment) divided into 15 sub-criteria were used to assess soil sensitivity to degradation. The weight of each criterion was assigned using the AHP method. The degree of soil degradation was classified on a map based on four categories (low, moderate, high and critical). About 14.9% of land is not very sensitive to degradation, while 37.2% of land is highly sensitive to degradation. Vegetation and climate were identified as the main drivers of land degradation (37% and 28% respectively). climate scenarios indicate a significant drop in precipitation (around 10% for RCP 4.5 and 20% for RCP 8.5 scenarios by 2050), which accelerates soil degradation. The result represents a planning tool that can help decision-makers to take the necessary measures to ensure sustainable soil management in the face of climate change.

Supporting strategy for investment evaluation of photovoltaic power generation engineering projects using multi-criteria decision analysis methods

This scientific study examines the evaluation of photovoltaic power generation projects through the application of multi-criteria decision analysis methods. Two groups of large-scale grid-connected PV power generation system projects with a nominal power of 50 MW and 500 MW respectively were analyzed and evaluated. These systems were designed to be installed in the same wider geographical area in northern Greece, but they were differentiated in the part of the PV circuit. Twelve systems were analyzed in which either monocrystalline silicon panels or poly-crystalline silicon panels or bifacial photovoltaic panels were to be installed. In these systems either central photovoltaic inverters or photovoltaic string inverters were considered for installation. The following criteria were used to evaluate the investment in these projects. These criteria were related to the profitability, the financial cost, the technical level, and the electrical energy production of the systems and these were the initial investment cost, the operation and maintenance cost, the levelized cost of electricity, the net present value, the internal rate of return, the capital recovery or payback period, the technical level of the photovoltaic circuit, the technical maturity of the photovoltaic circuit, and the annual electricity production. The evaluation of these criteria was initially conducted with fixed weighting coefficients followed by a sensitivity analysis of these weighting coefficients. The results of the evaluation using the PROMETHEE, AHP and TOPSIS multi-criteria decision analysis methods showed that the PV power generation systems which should be preferred are those with increased nominal power, where monocrystalline silicon technology panels are employed following the central inverter topology.