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The influence of Acrylate Styrene Acrylonitrile (ASA) and ASA/nanosilica (ASA/Si) additives was investigated by using a dynamic shear rheometer (DSR). Firstly, an ASA polymer was blended with the virgin asphalt binder at two different concentrations (3% ASA and 5% ASA). After observing that 5% ASA was the optimum concentration for modification, nanosilica particles were further incorporated into the 5% ASA-modified asphalt binder with two different percentages (5% ASA 3%Si; 5% ASA 5%Si). Frequency sweep tests were conducted across various frequencies at elevated temperatures. The experimental outcomes were analyzed using master curves, rutting, and fatigue resistance parameter plots. Additionally, to provide a more holistic analysis, two different multicriteria decision analysis (MCDA) techniques, namely the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and the Technique for the Order of Preference by a Similarity to Ideal Solution (TOPSIS), were conducted to identify the best-performing asphalt binder by considering three different parameters: workability, performance under different conditions, and cost. The frequency sweep tests showed that the 5% ASA 5%Si asphalt worked best in terms of resistance to rutting. On the other hand, the virgin binder performed better than all modified binders when it failed to resist fatigue. On the other hand, the PROMETHEE analysis identified the 5% ASA-modified asphalt binder as the optimal choice, while the TOPSIS analysis determined that the 5% ASA 3%Si-modified binder provided the best performance. The differences between the experimental results and the MCDA were due to using more than one evaluation parameter and looking at how well the asphalt binder worked at different temperature ranges at the same time. [ABSTRACT FROM AUTHOR]

In today's competitive environment, multi-branch companies allocate their stores with the aim of expanding their territorial coverage to attract new customers and increase their market share. Consumer satisfaction surveys either produce global performance results or they are not able to differentiate consumer perceptions using location analytics. This research develops a novel framework to assist multi-branch companies in mapping the consumer satisfaction performance of their stores, expanding conventional customer relationship management to the spatial context. The framework developed proposes a decision model that combines the Group Decision Support extension of the PROMETHEE and CRITIC methods in a GIS environment to generate satisfaction performance mappings. The developed decision-making framework converts consumer responses into satisfaction performance maps, allowing the company's stores and their competitors to be evaluated. Moreover, it provides insight into the potential opportunities and threats for each store. The performance of the proposed framework is highlighted through a case study involving a multi-branch coffeehouse company in a Greek city. Finally, a tool developed to assist the computational part of the framework is presented. [ABSTRACT FROM AUTHOR]

Since Kraljic's strategic matrix was applied to supply chain management, classification of items, suppliers, and customers has become of increasing interest to research and companies. The aim of this research is to develop an easily interpretable multicriteria classification matrix method and validate it in real-world scenarios with a robustness analysis. This method assigns alternatives to one of four classes defined by critical dimensions that integrate several evaluation criteria. Initially, a global search pre-classifies the alternatives using the PROMETHEE net flows. Then, two local searches are carried out that make use of the discriminant properties of the net flow signs to improve the quality of the assignments. This approach is specifically applied to pre-classified alternatives near the boundary between two or more categories. The method has been validated by segmenting thousands of customers. Four customer segments were identified: strategic, collaborative, transactional, and non-preferred. A comparison was made between the results and those derived from an alternative method. Through an extensive sensitivity analysis, the proposed method was shown to be robust to parameter variation, highlighting its reliability in real dynamic contexts. The method provides valuable, easily interpretable information, which constitutes the basis for developing personalised strategies to enhance customer relationship management. [ABSTRACT FROM AUTHOR]

The project was conducted in the Ashigua páramo, Mulaló, Latacunga, Cotopaxi province. Its objective was to develop a composite environmental sustainability indicator for the conservation of the páramo. Neutrosophic logic and the PROMETHEE TODIM method were used to select and evaluate sub-indicators in a context of uncertain and complex environmental data. Neutrosophic logic facilitated the management of ambiguities and contradictions in the data. The findings highlighted the relevance of sub-indicators linked to carbon storage and ecological productivity. Indicators of regulatory compliance and sustainable management were evaluated, emphasizing the importance of adjusting environmental policies to regulations and social expectations. This study introduced a novel methodological approach to environmental assessment, providing valuable insights for policy development and effective management of natural resources and ecosystems. [ABSTRACT FROM AUTHOR]

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Amaç: Günümüz rekabetçi ekonomisinde firmaların ayakta kalabilmeleri için dijital olgunluk seviyelerinin değerlendirilmesi önemlidir. Bu çalışmada, Türk savunma sanayii kapsamındaki dört firmanın dijital olgunluk düzeyleri incelenmiştir. Değerlendirme, firmaların eksikliklerini belirleyip çözümler sunarak rekabet avantajı, sürdürülebilirlik ve verimlilik sağlamayı hedeflemektedir.Yöntem: Problemde kullanılan kriterlerin ağırlıklandırılmasında Analitik Hiyerarşi Süreci (AHP) yöntemi kullanılmıştır. Ardından alternatif seçimlerinde ise çok kriterli karar verme yöntemlerinden Technique for Order Preference By Similarity To Ideal Solution (TOPSIS) ve Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) yöntemleri tercih edilmiştir.Bulgular: Dijital olgunluk, akıllı üretim sistemleriyle verimliliği artırma kapasitesi olarak tanımlanmıştır. AHP ile ağırlıklandırılan kriterler arasında “Endüstri 4.0 Teknolojileri Kullanma Süreçleri” en öncelikli kriter olarak belirlenmiştir. TOPSIS ve PROMETHEE yöntemleriyle yapılan sıralamalarda 1. Firma en üst sırada yer almıştır. Çalışma, firmaların dijital dönüşüm süreçlerindeki güçlü ve zayıf yönlerini ortaya koymuştur. Gelecek araştırmalar için sektörel analizler ve yeni yöntemler önerilmektedir.Özgünlük: Bu çalışma, AHP, TOPSIS ve PROMETHEE yöntemlerini kullanarak Türk savunma sanayiindeki firmaların dijital olgunluk seviyelerini analiz eden özgün bir metodolojik yaklaşım sunmaktadır. Analiz, sektördeki firmaların dijital dönüşüm stratejilerini optimize etmelerine katkı sağlamaktadır.

Timber industries make an essential contribution to economies worldwide, while the sustainable supply of timber generates revenue, supports employment, and contributes to economic activity. The strategic choice of wood species using specific criteria can have substantial economic outcomes for the timber industry in Greece. This study assessed the suitability of hardwood species most commonly used by Greek timber industries. The assessment was conducted with the use of a Multiple-Criteria Decision Analysis approach, taking into consideration specific criteria that affect the quality of timber. According to the findings, walnut was the optimal alternative that outranked the other examined species. Chestnut, oak, beech, ash, and hornbeam also achieved positive scores, and therefore, they are also acceptable alternatives as broadleaved species suitable for furniture manufacturing and sawn timber production. Greek timber industries can enhance their products’ value and market appeal by focusing on species that meet high-quality standards and consumer preferences.

This article is devoted to the assessment of the country’s sustainable cyberspace strategy by comparing the cyber security strategies of those countries that are economically developed and use advanced information and communication technologies. Based on the components of the National Cyber Security Index, a calculation was made for 10 countries, which made it possible to monitor the effectiveness of their cyber strategies and identify their shortcomings. The calculation is made for the years 2018 and 2021. During this period, no significant critical changes form abnormal indicators, but it is possible to reflect a particular trend. Application of the PROMETHEE method (I and II) and the formation of net flows, determination of importance and comparison of alternatives allowed to carry out an in-depth analysis of various aspects of cyber security, such as protection measures, response to cyber threats and general resistance to cyber-attacks. It is determined that Belgium and Lithuania are the most resistant to threats from cyber-attacks and in the fight against money laundering among the countries considered, as they are rapidly developing and implementing new measures. Their indicators increased by 0.1 values in 2021 compared to 2018. According to the calculations, Great Britain is the most conservative in implementing certain strategies, and its indicators remained at the level of 0-0.2 according to various criteria for the years studied. Most countries improve their indicators by a total of 0.2 values, which reflects their development. Ukraine tends to decrease some indicators by 0.1 value; some are at the same level. The regulatory framework's introduction rate in the cyber security field increases. It shows that Ukraine has many rapidly changing risks, but is trying to achieve flexibility in responding to them. The research findings identify each country’s cybersecurity strengths and weaknesses, as well as potential risks and vulnerabilities. Thanks to this analysis, recommendations are made for developing and implementing effective cyber security strategies, both at the national and international levels. The study contributes to the understanding of the complexity of modern cyber threats. It emphasizes the need for continuous improvement and adaptation of protection measures to ensure the stability and security of financial and information systems in a global digital environment.

Polycentricity is a multiscalar concept referring to the interconnections of neighboring spatial areas in search of synergies. More specifically, urban polycentricity has lately gained increasing momentum in academic research and strategic planning as urban polycentric structures could stimulate urban and regional performance in a sustainable way. Architectural heritage, with its connotative meanings, is among the indicators of incorporation in polycentric networking. The present paper investigates the challenges of implementing urban polycentricity methodological approaches to highlight new polycentric networks based on built architectural heritage. In this study, appointed architectural assets are considered as nodes of the potential networks. As a new research strand, the MCDA PROMETHEE method is applied to compile and assemble all indicators (namely, rank size and node hierarchy, accessibility, transportation, and digital promotion). The ultimate research goal is to build a new polycentricity index as an innovative methodological tool to highlight polycentric networking synergies on interurban and intraregional scales. The region of Thrace in Northern Greece is chosen to apply the proposed methodology. Research on ways to couple polycentricity and PROMETHEE methods is limited, and thus, the proposed methodological approach is expected to contribute to the field of urban and regional strategies for researchers and practitioners. [ABSTRACT FROM AUTHOR]

Logistics and distribution centres are essential to the supply chains of many manufacturing and logistics companies. Efficiently locating logistic centres involves thorough search for optimal place, prioritizing proximity to suppliers and minimizing costs. Companies' management often solves the location problem of new halls, mostly to minimise the associated costs. Such a problem is solved in the automotive industry as part of the launch of a new international project. The key factor for the decision of where to locate the logistics centre is the location of the suppliers, since material deliveries generate a large part of the project costs. The aim of this study is to define a methodology for the location of the logistics centre, considering several alternative locations and relevant criteria. The location alternatives and criteria are defined in terms of minimising project costs and sustainability elements. The problem is solved using the multi-criteria decision-making approach. First, the AHP method is used to assign weights to the criteria. Then the PROMETHEE method is applied to find a suitable location for the logistics centre and to perform a thorough sensitivity analysis. The sensitivity analysis is focused on the impact of values of weights on the solution. Consequently, the analysis proves the correctness of the selected alternative. Based on the case study, a general methodology for locating a logistics centre is proposed. [ABSTRACT FROM AUTHOR]

The current economic and political crisis has brought about a change in the environment in which non-governmental development organisations (NGDOs) have traditionally operated. This change can be summed up as a reduction in the funds they receive and an increase in the population they must serve. The need then arises to have mechanisms that allow an analysis of the good work performed by the NGDOs. Knowing the efficiency of the NGDOs in the management of their previous projects can contribute towards improving their future achievements. The aim of this research is to establish some objective indicators that allow an evaluation of the efficiency of these organisations. Firstly, a detailed analysis of the regulation of the three agencies is conducted (Spanish-AECID, European-EuropeAid, and American-USAID). This allows us to synthesise the indicators of good performance of the NGDO based on the study of the eligibility criteria of public donors. The research concludes with the study of the efficiency following the Promethee Approach. Our results reveal that 44.6% of the NGDOs (33 out of the 74 studied) operate inefficiently, compared to 29.7%, which are efficient. [ABSTRACT FROM AUTHOR]

The structural design of prestressed arched trusses presents a complex challenge due to the need to balance multiple conflicting objectives such as structural performance, weight, and constructability. This complexity is further compounded by the interdependent nature of the structural elements, which necessitates a comprehensive optimization approach. Addressing this challenge is crucial for advancing construction practices and improving the efficiency and safety of structural designs. The integration of advanced optimization algorithms and decision-making techniques offers a promising avenue for enhancing the design process of prestressed arched trusses. This study proposes the use of three advanced multi-objective optimization algorithms: NSGA-III, CTAEA, and SMS-EMOA, to optimize the structural design of prestressed arched trusses. The performance of these algorithms was evaluated using generational distance and inverted generational distance metrics. Additionally, the non-dominated optimal designs generated by these algorithms were assessed and ranked using multiple multi-criteria decision-making techniques, including SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR. This approach allowed for a robust comparison of the algorithms and provided insights into their effectiveness in balancing the different design objectives. The results of the study indicated that NSGA-III exhibited superior performance with a GD value of 0.215, reflecting a closer proximity of its solutions to the Pareto front, and an IGD value of 0.329, indicating a well-distributed set of solutions across the Pareto front. In comparison, CTAEA and SMS-EMOA showed higher GD values of 0.326 and 0.436, respectively, suggesting less convergence to the Pareto front. However, SMS-EMOA demonstrated a balanced performance in terms of constructability and structural weight, with an IGD value of 0.434. The statistical significance of these differences was confirmed by the Kruskal–Wallis test, with p-values of 2.50 × 10 − 15 for GD and 5.15 × 10 − 06 for IGD. These findings underscore the advantages and limitations of each algorithm, providing valuable insights for future applications in structural optimization. [ABSTRACT FROM AUTHOR]

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.

The decision-making process regarding the allocation of professor positions is a recurring theme in higher education institutions. The study shows a model of allocation of teaching positions based on a hybrid methodology AHP-PROMETHEE, proposed for a higher education institution with distribution of vacancies in departments. Where, through the case study, it was possible to determine, through a scientific technique, the appropriate allocation of places, presenting as a result the department in which the vacancy will be allocated. For this purpose, the AHP was used to define the weights of the analyzed criteria and the PROMETHEE to select the department that will receive the teaching post. The work also compares the result presented by the proposed method with the solutions proposed by the model used by the analyzed institution and the MEC/Andifes model, in addition to demonstrating the advantages of using the AHP-PROMETHEE technique to solve the problem. [ABSTRACT FROM AUTHOR]

Heart failure is a progressive disease that leads to high mortality rates if left untreated, and inotropes are a class of drugs used to treat a type of heart failure where patients have reduced ejection fraction (HFrEF). This study aims to utilize the Fuzzy-Preference Ranking Organization Method for Enrichment Evaluation (F-PROMETHEE), an effectively used multi-criteria decision making (MCDM) technique. To analyze the characteristics of the most often used inotropes for acute HFrEF and chronic HFrEF, we use the same parameters set with distinct importance factors and aims for each property and, therefore, mathematically demonstrate the strengths and weaknesses of each inotrope alternative. As a result, a detailed ranking list for each HFrEF class was obtained, with supplementary information on how each parameter contributed to the ranking of each inotrope. From these results, it was concluded that the F-PROMETHEE method is applicable for evaluating the risks and benefits of various inotropes to determine a starting point for treating an average patient when making a quick decision without complete patient data. As demonstrated in this study, it is possible to easily use the same data set and only change some preference parameters according to individual needs to produce patient-specific results. In this study, we showed that creating a decision-making system that mathematically assists clinical specialists with their decision-making process is feasible. [ABSTRACT FROM AUTHOR]

With an increasing focus on sustainable development and the reuse of disused infrastructure, there is a pressing need for effective decision-making methods. This study explored the use of the PROMETHEE method within a collaborative decision-making framework to assess the adaptive reuse options for a disused railway in Sicily, Italy. Using a participatory paradigm, the research actively involves stakeholders in the assessment process. The collaborative PROMETHEE approach acts as a tool for multi-criteria decision analysis, facilitating the comparison and prioritization of different reuse alternatives based on predefined criteria. The research highlights the effectiveness of PROMETHEE in streamlining decision-making processes for adaptive reuse efforts, particularly in the context of integrated conversions of abandoned railways. By combining multi-criteria decision analysis with collaborative methods, the study contributed to the formulation of sustainable and socially responsible strategies for the valorization of disused railway infrastructure. The results underline the importance of collaborative decision-making processes and the instrumental role of PROMETHEE in assisting stakeholders in evaluating and selecting adaptive reuse options for disused railways, thereby, in turn, promoting the sustainable development of these sites. [ABSTRACT FROM AUTHOR]

The advancement toward sustainable infrastructure presents complex multi-objective optimization (MOO) challenges. This paper expands the current understanding of design frameworks that balance cost, environmental impacts, social factors, and structural integrity. Integrating MOO with multi-criteria decision-making (MCDM), the study targets enhancements in life cycle sustainability for complex engineering projects using precast modular road frames. Three advanced evolutionary algorithms—NSGA-II, NSGA-III, and RVEA—are optimized and deployed to address sustainability objectives under performance constraints. The efficacy of these algorithms is gauged through a comparative analysis, and a robust MCDM approach is applied to nine non-dominated solutions, employing SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR decision-making techniques. An entropy theory-based method ensures systematic, unbiased criteria weighting, augmenting the framework's capacity to pinpoint designs balancing life cycle sustainability. The results reveal that NSGA-III is the algorithm converging towards the most cost-effective solutions, surpassing NSGA-II and RVEA by 21.11% and 10.07%, respectively, while maintaining balanced environmental and social impacts. The RVEA achieves up to 15.94% greater environmental efficiency than its counterparts. The analysis of non-dominated solutions identifies the A 4 design, utilizing 35 MPa concrete and B500S steel, as the most sustainable alternative across 80% of decision-making algorithms. The ranking correlation coefficients above 0.94 demonstrate consistency among decision-making techniques, underscoring the robustness of the integrated MOO and MCDM framework. The results in this paper expand the understanding of the applicability of novel techniques for enhancing engineering practices and advocate for a comprehensive strategy that employs advanced MOO algorithms and MCDM to enhance sustainable infrastructure development. [ABSTRACT FROM AUTHOR]

The highly dynamic nature of the real-world environment poses significant challenges for electric bus system operations (EBSOs), which are prone to serious accidents due to their complexity and a wide variety of risk factors. The accidents are often the result of ignoring the most serious risk sources because of a lack of comprehensive risk assessments. Therefore, this paper proposes an improved failure mode and effects analysis (FMEA) multicriteria group decision-making model to ensure the reliability and safety of EBSOs. First, an expert group is invited to evaluate the risk failure modes (FMs) of the EBSOs and transform them into a normal wiggly hesitant fuzzy set (NWHFS) form. Because the risk assessment process involves a large number of team members with different backgrounds, the experts are grouped based on scoring function values using the K-medoids clustering technique. Then, the evaluation values of the expert group are integrated using the normal hesitant fuzzy weighted geometric (NWHFWG) aggregation operator to obtain the final aggregation matrix, and the weights of the three criteria of occurrence (O), severity (S) and detection (D) are determined for each FM via the CCSD method. Finally, considering the cross-correlation between factors within the system, the relationships between FMs are analyzed, and their impact and importance are quantified using the gray correlation-based DEMATEL method, followed by the final ranking of the FMs using regret theory and the PROMETHEE II methodology to achieve a rational allocation of resources. The results are analyzed with sensitivity and comparative analyses to illustrate the superiority of the model. [ABSTRACT FROM AUTHOR]

Sustainable construction becomes a highly key topic in the construction sector owing to its benefits and advantages over economy, environment, and society. This study considers one such application of sustainable construction, rehabilitation of RC beams. Through such rehabilitation, it is possible to extend the life of existing concrete structures. A numerical analysis approach has been framed to study to this phenomenon. Total 10 criteria have been selected to evaluate the rehabilitation material alternatives on RC beams with ultra-high-performance concrete. 9 Alternative materials have been considered for rehabilitation of RC beams. DEMATEL-ANP and PROMETHEE has been used as a methodology in two phases, the first phase used DEMATEL-ANP to analyze the considered criteria and the second phase used PROMETHEE to evaluate the considered alternative materials based on the analyzed criteria. Finally, this study concludes with the effective rehabilitation material for RC beams with ultra-high-performance-concrete. The considered numerical framework can be adopted in various forms of similar or different applications. [ABSTRACT FROM AUTHOR]

This article is devoted to the assessment of the country's sustainable cyberspace strategy by comparing the cyber security strategies of those countries that are economically developed and use advanced information and communication technologies. Based on the components of the National Cyber Security Index, a calculation was made for 10 countries, which made it possible to monitor the effectiveness of their cyber strategies and identify their shortcomings. The calculation is made for the years 2018 and 2021. During this period, no significant critical changes form abnormal indicators, but it is possible to reflect a particular trend. Application of the PROMETHEE method (I and II) and the formation of net flows, determination of importance and comparison of alternatives allowed to carry out an in-depth analysis of various aspects of cyber security, such as protection measures, response to cyber threats and general resistance to cyber-attacks. It is determined that Belgium and Lithuania are the most resistant to threats from cyber-attacks and in the fight against money laundering among the countries considered, as they are rapidly developing and implementing new measures. Their indicators increased by 0.1 values in 2021 compared to 2018. According to the calculations, Great Britain is the most conservative in implementing certain strategies, and its indicators remained at the level of 0-0.2 according to various criteria for the years studied. Most countries improve their indicators by a total of 0.2 values, which reflects their development. Ukraine tends to decrease some indicators by 0.1 value; some are at the same level. The regulatory framework's introduction rate in the cyber security field increases. It shows that Ukraine has many rapidly changing risks, but is trying to achieve flexibility in responding to them. The research findings identify each country's cybersecurity strengths and weaknesses, as well as potential risks and vulnerabilities. Thanks to this analysis, recommendations are made for developing and implementing effective cyber security strategies, both at the national and international levels. The study contributes to the understanding of the complexity of modern cyber threats. It emphasizes the need for continuous improvement and adaptation of protection measures to ensure the stability and security of financial and information systems in a global digital environment. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of undesired events. Design/methodology/approach: To address the constraints considered in the conventional failure mode and effects analysis (FMEA) method for criticality assessment, the authors propose a new hybrid model combining different multi-criteria decision-making (MCDM) methods. The analytical hierarchy process (AHP) is used to construct a criticality matrix and calculate the weights of different criteria based on five criticalities: personnel, equipment, time, cost and quality. In addition, a preference ranking organization method for enrichment evaluation (PROMETHEE) method is used to improve the prioritization of the failure modes. A comparative work in which the robust data envelopment analysis (RDEA)-FMEA approach was used to evaluate the validity and effectiveness of the suggested approach and simplify the comparative analysis. Findings: This work aims to highlight the real case study of the automotive parts industry. Using this analysis enables assessing the risk efficiently and gives an alternative ranking to that acquired by the traditional FMEA method. The obtained findings offer that combining of two multi-criteria decision approaches and integrating their outcomes allow for instilling confidence in decision-makers concerning the risk assessment and the ranking of the different failure modes. Originality/value: This research gives encouraging outcomes concerning the risk assessment and failure modes ranking in order to reduce the frequency of occurrence and gravity of the undesired events by handling different forms of uncertainty and divergent judgments of experts. [ABSTRACT FROM AUTHOR]

Existen dos grandes divisiones en las situaciones de toma de decisiones con múltiples obje­tivos: programación multiobjetivo, referido a variables continuas, y decisiones multicriterio discretas. El trabajo se centra en la problemática de las decisiones multicriterio discretas (DMD). Estos métodos son técnicas de apoyo para resolución de situaciones en las cuales se debe escoger una alternativa entre varias. El trabajo presenta un modelo de la escuela francesa –PROMETHEE–, que se centra en el cálculo de las preferencias de cada alternativa respecto a las otras en cada criterio y luego en forma conjunta. En una primera parte del trabajo se expone el método PROMETHEE en su versión clásica. Posteriormente se aclaran las posibles transiciones entre no preferencia y la preferencia completa. La última parte del trabajo propone una variante para el cálculo de la transición de modo de no agregar mayores subjetividades. Se contempla, además, el aspecto de la sensibilidad de la decisión sugerida. La categoría propuesta se fundamenta en que los métodos cuantitativos son herramientas de apoyo en el proceso decisorio y éste, a su vez, es una temática central en la gestión de todas las organizaciones, sean pequeñas o importantes.

Cultural tourism products refer to the special tourism products supported by cultural tourism resources and aimed at obtaining cultural impression and increasing intelligence. This study establishes a hybrid multi-criteria decision-making (MCDM) framework to solve the evaluation and pricing issues of cultural tourism products. In the proposed framework, picture fuzzy sets are adopted to thoroughly depict decision-making information with four independent functions. Simultaneously, a novel picture fuzzy numbers cross-entropy-based optimization model (PFN-CEBOM) is proposed to reveal the significance of decision-making attributes. The advanced PFN-CEBOM can get comprehensive criteria weights by covering both objective and subjective weight data in picture fuzzy environments. Then, the traditional preference ranking organization method for enrichment evaluations (PROMETHEE) is modified with picture fuzzy information to get non-compensatory decision-making results. And finally, the proposed method is applied to evaluate and pricing different cultural tourism products in China. Additionally, the availability and superiority of the proposed model are verified through sensitive and comparative analyses. Results evince that the proposed approach is valid for reasonable pricing of cultural tourism products and can serve as a promising instrument for tourism industries.

This paper addresses the imperative task of assessing and ranking cryptocurrencies, particularly pertinent in the context of the burgeoning popularity of public blockchains. The proliferation of available options necessitates a rigorous evaluation, prompting the formulation of a novel model grounded in both objective and subjective criteria. To contend with the challenge posed by the expanding landscape of public blockchains, ten discerning criteria are delineated, encompassing facets such as Technology, TPS, Market capitalization, GitHub fork, GitHub stars, Twitter followers, Twitter hashtags, trading volume, sentiment score, and the price range differential. Leveraging expert opinions, the pairwise impact of these criteria is ascertained, and the DEMATEL method is judiciously employed to derive their respective weights. Subsequently, the PROMETHEE method is harnessed to effectuate the ranking of 20 cryptocurrencies predicated on the identified criteria. Furthermore, the integration of LSTM enables the prediction of values for four predictable criteria, seamlessly incorporated into the PROMETHEE model to furnish rankings across diverse temporal intervals. The proposed model, thus, presents a holistic and pragmatic approach to inform investment decision-making within the dynamic cryptocurrency market. By embracing a comprehensive set of criteria and integrating predictive analytics, this model stands as a valuable contribution to the field, offering nuanced insights to stakeholders navigating the complexities of cryptocurrency investment.

In today’s competitive environment, multi-branch companies allocate their stores with the aim of expanding their territorial coverage to attract new customers and increase their market share. Consumer satisfaction surveys either produce global performance results or they are not able to differentiate consumer perceptions using location analytics. This research develops a novel framework to assist multi-branch companies in mapping the consumer satisfaction performance of their stores, expanding conventional customer relationship management to the spatial context. The framework developed proposes a decision model that combines the Group Decision Support extension of the PROMETHEE and CRITIC methods in a GIS environment to generate satisfaction performance mappings. The developed decision-making framework converts consumer responses into satisfaction performance maps, allowing the company’s stores and their competitors to be evaluated. Moreover, it provides insight into the potential opportunities and threats for each store. The performance of the proposed framework is highlighted through a case study involving a multi-branch coffeehouse company in a Greek city. Finally, a tool developed to assist the computational part of the framework is presented.

The combination of MCDM and fuzzy sets offers new potential ways to solve the challenges posed by complex image contents, such as selecting the optimal segmentation algorithm or evaluating the segmentation quality based on various parameters. Since no single segmentation algorithm can achieve the best results on satellite image datasets, it is essential to determine the most appropriate segmentation algorithm for each satellite image, the content of which can be characterized by relevant visual features. In this research, we proposed a set of visual criteria representing the fundamental aspects of satellite image segmentation. The segmentation algorithms chosen for testing were evaluated for their performance against each criterion. We introduced a new method to create a decision matrix for each image using fuzzy fusion, which combines the image content vector and the evaluation matrix of the studied segmentation algorithms. An extension of the Preference Ranking Organization Method Enrichment Evaluation (PROMETHEE) using intuitive fuzzy sets (IFSs) was applied to solve this problem. The results acquired by the proposed methodology were validated by comparing them with those obtained in expert ratings and by performing a sensitivity analysis. [ABSTRACT FROM AUTHOR]

Large amounts of hydrochar have been produced during the last decade by various hydrothermal carbonisation (HTC) processes. While the products of HTC seem to have widespread acceptance as valuable and efficient materials with advantages in their energy and environmental applications, which include soil improvement, heavy metal recovery, and many more, a comprehensive framework for the assessment of the different hydrochars based on their characteristics is missing. In this study, a framework for the assessment of hydrochars is proposed with the utilisation of Multi-Criteria Decision-Aiding (MCDA) methodologies. A hierarchical structure of independent criteria is established on a comprehensive level including three lines of evidence (LoE), i.e., Environmental, Economic, and Social LoE, which further include the assessment criteria. Hierarchical-SMAA-PROMETHEE is proposed as the most suitable MCDA methodology to be applied for assessing hydrochars based on the proposed framework. A case study is performed to demonstrate the utility of the framework and the advantages it offers to analysts and decision-makers. Hierarchical-SMAA-PROMETHEE is a non-compensatory method that enables exploring the decision problem on more than one level (comprehensive vs. LoE) and includes robust recommendations on the preference model and the elicitation of weights. [ABSTRACT FROM AUTHOR]

Mitigating maladaptation and effectively managing climate risks are crucial components of strategic planning in agriculture amidst climate change. Evaluation serves as a pivotal element in this process, facilitating the identification of effective adaptation strategies tailored to local contexts. Consequently, it's imperative to thoroughly evaluate these strategies to ensure their success and resilience. The current study evaluated adaptation methods tailored to the local context in southwest Iran across three categories-crop, farm, and water management-employing Multi-Criteria Decision-Making (MCDM) and the PROMETHEE-GAIA. Sensitivity analysis was performed during the AHP (Analytical Hierarchy Process) stage to confirm the criteria weights and in the PROMETHEE to confirm the ranking. A set of eight criteria, including effectiveness/importance, affordability, institutional feasibility, technical feasibility, social feasibility, traditional acceptance, flexibility, and environment side effects (positive) were applied to evaluate the adaptation measures. Our results indicated the three highest rankings in each set of measures, as follows: i) crop management—relay intercropping, change of crop type, and mixed intercropping; ii) farm management—pest and disease management, weed control, and crop rotation; iii) water management—lining water canals or covering their earth floors with nylon, using pipes rather than open canals to transfer water to the field, and increasing the time intervals between irrigations to deal with water shortages. The outcomes underscore the urgency of formulating region-specific adaptation policies that align with local expertise and contextual needs. By prioritizing the identified effective strategies, policymakers can enhance resilience against water scarcity in southwest Iran. Moreover, the study highlights the importance of ongoing evaluation and adaptation, emphasizing the dynamic nature of climate challenges and the need for continuous refinement of adaptive policies.

Selective laser melting (SLM) has attracted recent attention in building parts with improved functionality and properties. Accordingly, the present work attempts to fabricate a novel 13Ni400 maraging steel parts with maximum theoretical density by SLM. A Box Behnken Design response surface methodology (RSM) approach is used to evaluate the influence of critical process parameters (laser power, laser scan speed, layer thickness, and hatch spacing) on the relative density, micro hardness, surface roughness, and tensile strength. Both the powder and the fabricated parts were characterized for the structural and microstructural features using X-ray diffraction (XRD) and scanning electron microscopy (SEM). The influences of energy density and other process variables are explored through careful parametric studies and 3-D surface plots. Analysis of variance (ANOVA) is carried out, which reveals that the laser power and layer thickness are the most dominant process parameters impacting the performance of the SLM process. A revolutionary preference ranking organization technique for enrichment evaluation (PROMETHEE) is adopted to convert the multiple responses into an equivalent net outranking flow and rank the alternatives. The PROMETHEE-based outcomes are further improved by using a cuckoo search (CS) algorithm. A confirmatory test is conducted on the optimum build conditions obtained by the hybrid methodology (combination of PROMETHEE and Cuckoo Search) to validate the proposed work. It is seen that the value of net outranking flow is enriched by ∼5 %. This work is proficient in producing defect-free parts, with maximum densification and with improved mechanical properties for a newly developed 13Ni-400 maraging steel.

The focus of this research is how to rank the West Sumatra creative economy sub-sector using creative economy indicators according to law No. 24 of 2019, Added Value, Intellectual Property, Cultural Heritage and Science and Technology. The research was conducted in six sub-sectors with the largest proportion of creative entrepreneurs in 2022. This study used the promethee ranking method, and GAIA analysis. The results of the study show that the competitiveness of the West Sumatra creative economy sub-sector is determined by cultural heritage and use of technology as well as the role of the workforce and developing the creative economy. There are three sub-sectors that have advantages based on creative economic indicators in West Sumatra, namely the Fashion, Performing Arts and Design sub-sectors. The factors causing its superiority are mainly in terms of cultural inheritance and the application of technology in the products created. The design policy for increasing the competitiveness of the West Sumatra creative economy sub-sector can be carried out most urgently (necessary conditions), increasing awareness of creative business actors registering intellectual property rights for creative products.

Postal service providers can reorganize the last-mile delivery process within the scope of universal service and apply some of the flexible models for the organization of the delivery process. In this paper, the question of the selection of Flexible Last-Mile Delivery Models (FLMDM) is treated using multicriteria decision-making. We have identified four different sustainable models of last-mile delivery with an emphasis on the number of delivery workers. One postal service provider from Europe was selected, where proposed FLMDM are tested. The proposed last-mile delivery models are ranked using Multiple Criteria Decision Analysis (MCDA) technique. In this context, MCDA techniques are used to make a comparative assessment of alternatives. This paper aims to find the optimal delivery costs in each variant of the delivery model - an optimal number of workers for the last-mile delivery. The obtained results suggest the AB delivery model as the optimal choice for the last-mile delivery. Also, results ensure the complete allocation of the required number of technological workers in the delivery (the number of delivery workers), by applying the originally proposed, flexible solution (models) for the organization of last-mile delivery.

Polycentricity is a multiscalar concept referring to the interconnections of neighboring spatial areas in search of synergies. More specifically, urban polycentricity has lately gained increasing momentum in academic research and strategic planning as urban polycentric structures could stimulate urban and regional performance in a sustainable way. Architectural heritage, with its connotative meanings, is among the indicators of incorporation in polycentric networking. The present paper investigates the challenges of implementing urban polycentricity methodological approaches to highlight new polycentric networks based on built architectural heritage. In this study, appointed architectural assets are considered as nodes of the potential networks. As a new research strand, the MCDA PROMETHEE method is applied to compile and assemble all indicators (namely, rank size and node hierarchy, accessibility, transportation, and digital promotion). The ultimate research goal is to build a new polycentricity index as an innovative methodological tool to highlight polycentric networking synergies on interurban and intraregional scales. The region of Thrace in Northern Greece is chosen to apply the proposed methodology. Research on ways to couple polycentricity and PROMETHEE methods is limited, and thus, the proposed methodological approach is expected to contribute to the field of urban and regional strategies for researchers and practitioners.

Küreselleşme ve teknolojinin gelişmesi ile birlikte, gerek yurtiçi gerekse ithalat ve ihracatla artan ticari faaliyetler, bankaları ve bankacılık sektörünü de ekonomi açısından önemli bir noktaya getirmiştir. Bankaların finansal performans incelemeleri genellikle hem teorik hem de uygulama açısından önemli konuların başında gelmiştir. Literatürde birçok çok kriterli karar verme yöntemlerinin finansal performans ölçümleri amacıyla kullanıldığı görülmektedir. Bu çalışmada Türkiye’de faaliyet gösteren kamu, özel veya yabancı sermayeli 20 bankanın finansal performanslarının BWM tabanlı TOPSIS, PROMETHEE ve COPRAS yöntemleri ile ölçülmesi ve elde edilen bulguların karşılaştırmalı olarak yorumlanması amaçlanmıştır. Çalışmada bankalar CAMELS değişkenleri altında 10 kriter belirlenerek değerlendirilmiştir. Aynı zamanda çalışma sonucunda elde edilen bankaların sıralamasına ait sonuçlar Spearman sıra korelasyon analizi ile karşılaştırılmıştır. Çalışma 2009 ile 2020 yılları arasını kapsamaktadır. Çalışmanın sonucunda ise kamu bankalarının performans olarak daha yüksek olduğu ve yöntemler açısından her üç yöntem bulgularının benzer olduğu ancak uygulama kolaylığı ve sağladığı faydalı bilgiler açısından PROMETHEE yönteminin daha uygulanabilir olduğu sonuçlarına ulaşılmıştır. Bu sonuçların çok kriterli karar verme yöntemleri literatürüne ve bankacılık sektörüne katkı sağlayacağı düşünülmektedir.

In occupational health and safety, it is important to use personal protective equipment that reduces or prevents injury to employees during an accident. Advanced sensors and computing technologies that can be worn on the body to create, store and transmit data during daily activities are wearable technology products. These products also make significant contributions to employee health and safety. The use of wearable technologies, which are very important in creating a safe work environment, is seen as a promising solution in contributing to the reduction of accidents and increasing industrial efficiency by increasing employee safety, reducing hazards and risks in sectors where occupational accidents are common.The study, it is aimed to prioritize the use of wearable technology devices in sectors that have potential risks that may cause fatigue, injury, error, and accident of employees due to labor-intensive and challenging tasks. Within the scope of this target, five alternatives and eight criteria were determined in line with the literature research and expert opinions. The criteria weights were determined by the AHP method, and the ranking of the alternatives was made by the AHP and PROMETHEE methods. Alternatives are listed as construction, mining, agriculture, textile, and chemistry. The increase in the use of wearable technology devices in the leading sectors in terms of work accidents and occupational diseases will open a new door to occupational safety and health management in the sectors and will significantly reduce health and safety problems.

Sustainable manufacturing technologies are the new challenge faced by enterprises, industries, and researchers. The development of a sustainability-based assessment method considering the environmental and economic impacts is crucial to realize viable manufacturing. However, few studies have addressed environmental economics and social flows using a common perspective. Mechanical machining is one of the most-used manufacturing techniques. The overall ecological, economic, and social footprint requires accurate and effective estimation and optimization. Several studies have addressed this issue by examining the entire process of machining, but sustainability flows for machining parameters and toolpaths have remained relatively unexplored. The lack of systematic assistance tools bridging the gap between decision-maker preferences and the three sustainability pillars—economic, social, and environmental—has impeded the widespread adoption of sustainable machining practices. To this end, this paper proposes an integrated approach to the decision-making problem that combines the Analytical Hierarchy Process (AHP) with the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) for selecting a sustainable machining strategy. The sustainability criteria are driven by manufacturing process parameters commonly employed and regulated during the manufacturing phase. This includes toolpath strategies as a qualitative input factor and manufacturing parameters such as cutting speed, feed rate, depth of cut, and stepover as quantitative input factors, affirming the practical applicability of the method in industrial contexts. New fundamental methods are also presented for selecting the most efficient machining parameters and toolpaths according to the weights assigned to each ecological, social, and economic footprint by the decision-maker (the manufacturer or production manager). In this way, sustainable machining strategies in the manufacturing industry will be strengthened in integrity. In a case study of part-end milling, both manufacturing parameters and toolpath strategies are considered to establish sustainable feature-based machining decisions. [ABSTRACT FROM AUTHOR]

This work proposes a methodology based on multicriteria decision aid (MCDA) and a cluster analysis to identify ideal locations for the installation of police facilities or vehicle parking and policing around stadiums in Recife, Brazil, during potential violent sports events (criminal occurrences from football supporters or fanbases). A K-means unsupervised clustering algorithm is used to group criminal data into homogeneous clusters based on their characteristics. Each type of criminal occurrence is linked to a single cluster. The optimal location is addressed based on the PROMETHEE method (Preference Ranking Organization Method for Enrichment Evaluation), allowing clusters to be organized into a hierarchy based on the number of facilities (N), the average distance (D) from the criminal occurrence to the associated cluster, and the coverage level (C), which is the proportion of crime occurring in a location less than 500 m from the associated cluster. Through a data analysis of crimes and violence in the region, this study seeks to identify patterns of criminal behaviour and high-risk areas to determine the most strategic locations for police units and enhance the public security decision-making process. The choice for the k parameters ranged from 1 to 30, incorporating all regions of the analysis, with a computational cost of 43 min of running time using an Intel Core i3-3217U (1800 GHz and 10 GB of RAM). This approach and methodology can be useful for supporting public security policies in the region and can contribute to reducing violence around stadiums. The empirical application can help guide public managers' decisions regarding resource allocation and the implementation of more effective security policies, with the aim of ensuring a safer environment for fans and residents in the areas near stadiums. [ABSTRACT FROM AUTHOR]

A country that relies on developing industrialization and GDP requires a lot of energy. Biomass is emerging as one of the possible renewable energy resources that may be used to generate energy. Through the proper channels, such as chemical, biochemical, and thermochemical processes, it can be turned into electricity. In the context of India, the potential sources of biomass can be broken down into agricultural waste, tanning waste, sewage, vegetable waste, food, meat waste, and liquor waste. Each form of biomass energy so extracted has advantages and downsides, so determining which one is best is crucial to reaping the most benefits. The selection of biomass conversion methods is especially significant since it requires a careful study of multiple factors, which can be aided by fuzzy multi-criteria decision-making (MCDM) models. This paper proposes the normal wiggly interval-valued hesitant fuzzy-based decision-making trial and evaluation laboratory model (DEMATEL) and the Preference Ranking Organization METHod for Enrichment of Evaluations II (PROMETHEE) for assessing the problem of determining a workable biomass production technique. The proposed framework is used to assess the production processes under consideration based on parameters such as fuel cost, technical cost, environmental safety, and C O 2 emission levels. Bioethanol has been developed as a viable industrial option due to its low carbon footprint and environmental viability. Furthermore, the superiority of the suggested model is demonstrated by comparing the results to other current methodologies. According to comparative study, the suggested framework might be developed to handle complex scenarios with many variables. [ABSTRACT FROM AUTHOR]

In the last decade, shipping decarbonization has accelerated rapidly in response to the regulatory framework. Shifting toward alternative marine fuel options is the subject of extensive study from stakeholders and researchers. This study attempts to propose a decision support model for alternative fuel evaluation. The decision-making process is multidimensional, comprising economic, technical, environmental, and social aspects, and has been carried out with the aid of the outranking multicriteria methodology, Promethee II. The approach is based on a comprehensive list of 11 criteria and 25 sub-criteria, covering all the crucial aspects. The weighting criteria process postulates the viewpoints of six stakeholder categories, including all the stakeholders' preferences: shipowners, fuel suppliers, industry and engine manufacturers, academics, banks and the public. The results demonstrated that although LNG, MGO and HFO are classified in the highest positions, there are renewable options that also appear in high-ranking positions in most categories and especially among academics, banks, the public and in the combined case scenario. The commercially available options of drop-in biofuels, bio and e-LNG, fossil and bio methanol were ranked in these high positions. This approach offers insight into the assessment and selection of alternative marine fuel options, providing an incentive for strategic planning. [ABSTRACT FROM AUTHOR]

The decision-making process is part of everyday life for people and organizations. When modeling the solutions to problems, just as important as the choice of criteria and alternatives is the definition of the weights of the criteria. This study will present a new hybrid method for weighting criteria. The technique combines the ENTROPY and CRITIC methods with the PROMETHE method to create EC-PROMETHEE. The innovation consists of using a weight range per criterion. The construction of a weight range per criterion preserves the characteristics of each technique. Each weight range includes lower and upper limits, which combine to generate random numbers, producing "t" sets of weights per criterion, allowing "t" final rankings to be obtained. The alternatives receive a value corresponding to their position with each ranking generated. At the end of the process, they are ranked in descending order, thus obtaining the final ranking. The method was applied to the decision support problem of choosing policing strategies to reduce crime. The model used a decision matrix with twenty criteria and fourteen alternatives evaluated in seven different scenarios. The results obtained after 10,000 iterations proved consistent, allowing the decision maker to see how each alternative behaved according to the weights used. The practical implication observed concerning traditional models, where a single final ranking is generated for a single set of weights, is the reversal of positions after "t" iterations compared to a single iteration. The method allows managers to make decisions with reduced uncertainty, improving the quality of their decisions. In future research, we propose creating a web tool to make this method easier to use, and propose other tools are produced in Python and R. [ABSTRACT FROM AUTHOR]

Construction industry workers; are exposed to serious safety and health risks, hazardous work environments, and intense physical work. This situation causes fatal and non-fatal accidents, reduces productivity, and causes a loss of money and time. Construction safety management can use wearable sensors to improve safety performance. Since there are many types of sensors and not all sensors can be used in construction applications, it is necessary to identify suitable and reliable sensors. This requirement causes a sensor selection problem. The study aims to determine the priority order of physiological and kinematic sensors in preventing risks in the construction industry. Within the scope of this purpose, five criteria and seven alternatives were determined in line with the literature research and expert opinions. The criteria weights were calculated with the AHP method, and the alternatives were ranked with PROMETHEE and AHP. Providing a proactive approach to the use of sensors in the construction industry will provide safer working conditions, identify workers at risk, and help identify and predict potential health and safety risks. It will contribute to the literature on improving construction health and safety management. [ABSTRACT FROM AUTHOR]

Electric Vehicles offer one of the most efficient solutions towards the direction of providing sustainable transportation systems. However, a broader market uptake of Electric Vehicle--based mobility is still missing. The lack of sufficient infrastructure (Electric Vehicle charging stations) in combination with the lack of information about their availability appears as a major limitation, leading to low user acceptance. Additional, technology based, assistance services provided to Electric Vehicle users is a key solution to unlock the full potential of their utilization. This paper presents a multi-factor dynamic optimization model using multi-criteria analysis to select the best alternatives for Electric Vehicle charging within a smart grid with the goal of supporting a larger uptake of Electric Vehicle -based mobility. The application provides assistance to the Electric Vehicle drivers through functionalities of energy price, cost and travel time of the electric vehicle to the charging station, the specifications of vehicles and stations, the status of the charging stations as well as the user's preferences. The proposed model is developed by incorporating PROMETHEE II and Analytic Hierarchy Process methodologies to provide the best charging solutions after considering all possible options for each Electric Vehicle user. The multi-criteria analysis algorithm is not only limited to comparing alternative charging options at a specific time but also looks at several starting times of charging. A simulated case study is implemented to examine the functionality of the proposed model. From the results, it is evident that by applying the findings of this work entrepreneurial community and industry can develop new services that will improve user satisfaction, electromobility, urban mobility, and sustainability of cities. At the same time, academia, leveraging the methodology and factors that influence the choice of charging station, can conduct further research on digital innovations that will contribute to the consolidation of e-mobility ensuring the sustainability of cities, while accelerating digital transformation in the transport sector.

Abstract A country that relies on developing industrialization and GDP requires a lot of energy. Biomass is emerging as one of the possible renewable energy resources that may be used to generate energy. Through the proper channels, such as chemical, biochemical, and thermochemical processes, it can be turned into electricity. In the context of India, the potential sources of biomass can be broken down into agricultural waste, tanning waste, sewage, vegetable waste, food, meat waste, and liquor waste. Each form of biomass energy so extracted has advantages and downsides, so determining which one is best is crucial to reaping the most benefits. The selection of biomass conversion methods is especially significant since it requires a careful study of multiple factors, which can be aided by fuzzy multi-criteria decision-making (MCDM) models. This paper proposes the normal wiggly interval-valued hesitant fuzzy-based decision-making trial and evaluation laboratory model (DEMATEL) and the Preference Ranking Organization METHod for Enrichment of Evaluations II (PROMETHEE) for assessing the problem of determining a workable biomass production technique. The proposed framework is used to assess the production processes under consideration based on parameters such as fuel cost, technical cost, environmental safety, and $$CO_2$$ C O 2 emission levels. Bioethanol has been developed as a viable industrial option due to its low carbon footprint and environmental viability. Furthermore, the superiority of the suggested model is demonstrated by comparing the results to other current methodologies. According to comparative study, the suggested framework might be developed to handle complex scenarios with many variables.

With an increasing focus on sustainable development and the reuse of disused infrastructure, there is a pressing need for effective decision-making methods. This study explored the use of the PROMETHEE method within a collaborative decision-making framework to assess the adaptive reuse options for a disused railway in Sicily, Italy. Using a participatory paradigm, the research actively involves stakeholders in the assessment process. The collaborative PROMETHEE approach acts as a tool for multi-criteria decision analysis, facilitating the comparison and prioritization of different reuse alternatives based on predefined criteria. The research highlights the effectiveness of PROMETHEE in streamlining decision-making processes for adaptive reuse efforts, particularly in the context of integrated conversions of abandoned railways. By combining multi-criteria decision analysis with collaborative methods, the study contributed to the formulation of sustainable and socially responsible strategies for the valorization of disused railway infrastructure. The results underline the importance of collaborative decision-making processes and the instrumental role of PROMETHEE in assisting stakeholders in evaluating and selecting adaptive reuse options for disused railways, thereby, in turn, promoting the sustainable development of these sites.

Heart failure is a progressive disease that leads to high mortality rates if left untreated, and inotropes are a class of drugs used to treat a type of heart failure where patients have reduced ejection fraction (HFrEF). This study aims to utilize the Fuzzy-Preference Ranking Organization Method for Enrichment Evaluation (F-PROMETHEE), an effectively used multi-criteria decision making (MCDM) technique. To analyze the characteristics of the most often used inotropes for acute HFrEF and chronic HFrEF, we use the same parameters set with distinct importance factors and aims for each property and, therefore, mathematically demonstrate the strengths and weaknesses of each inotrope alternative. As a result, a detailed ranking list for each HFrEF class was obtained, with supplementary information on how each parameter contributed to the ranking of each inotrope. From these results, it was concluded that the F-PROMETHEE method is applicable for evaluating the risks and benefits of various inotropes to determine a starting point for treating an average patient when making a quick decision without complete patient data. As demonstrated in this study, it is possible to easily use the same data set and only change some preference parameters according to individual needs to produce patient-specific results. In this study, we showed that creating a decision-making system that mathematically assists clinical specialists with their decision-making process is feasible.

Dalam mengimplementasikan kebijakan berbasis bukti, perlu didukung oleh data kemiskinan yang tepat, akurat, dan konsisten. Tujuannya agar berbagai bantuan seperti bansos, PKH, sembako, prakerja, dan subsidi lainnya dapat tepat sasaran bagi masyarakat yang mengalami kemiskinan. Keragaman sumber data kemiskinan akan berimplikasi pada ketidaktepatan sasaran penerima bantuan sosial. Hingga saat ini, masih terdapat tiga sumber data kemiskinan, yaitu Badan Pusat Statistik (BPS), Kementerian Sosial (Kemensos) dengan Data Terpadu Kesejahteraan Sosial (DTKS), dan TNP2K yang kini berada di bawah Kementerian Koordinator Bidang Pembangunan Manusia dan Kebudayaan (Kemenko PMK) yang juga mengeluarkan data yang menyasar pada target Percepatan Penanggulangan Kemiskinan Ekstrim (P3KE). Tujuan dari penelitian ini adalah untuk menganalisis sebaran data kemiskinan dan analisis pemeringkatan data kemiskinan dari tiga sumber data di Kabupaten Bekasi. Metode analisis menggunakan perangkat lunak ArcGIS 10.4 dan shapefile peta administrasi wilayah Kabupaten Bekasi. Pada analisis pemeringkatan data kemiskinan dari tiga sumber data di setiap kecamatan di Kabupaten Bekasi, digunakan metode Preference Ranking Organization Methods For Enrichment Evaluation (PROMETHEE). Hasil analisis menunjukkan bahwa sebaran data P3KE sebanyak 1.317.098 jiwa tersebar pada desil-1 hingga desil-4 di seluruh kecamatan di Kabupaten Bekasi. Data BPS sebanyak 229.367 jiwa yang tersebar di setiap kecamatan, dan jumlah DTKS di Kabupaten Bekasi sebanyak 1.035.402 jiwa, dengan sebaran data terbanyak berada di Kecamatan Babelan, yaitu sebanyak 110.867 jiwa dari 34.009 jiwa. Sedangkan peringkat terbaik dari ketiga sumber data kemiskinan tersebut adalah Cikarang Pusat, Kecamatan Pebayuran (0.7273), Tambun Utara (0.6364), Cibitung dan Karang Bahagia (0. 4242), Cikarang Utara (0.3939), Tambun Selatan (0.3636), Sukatani (0.2121), Serang Baru (0.1515), Cikarang Barat (0.1212), Tarumajaya (0.0909), Cikarang Timur (0.0606).

AbstractMeasuring and assessing the degree of technological readiness at macro level is crucial in guiding the overall technological development for the countries. The problem is multi-criteria in nature, involving assessment and consolidation over multiple dimensions. This study develops a composite index to measure basic technological maturity from supply chain integration perspective and utilizes multi-criteria decision-making method PROMETHEE to obtain a ranking among 28 OECD countries. Based on OECD data and metrics set availability, a composite index is developed using 10 relevant metrics. Then a comparative study is performed for 28 countries for the year 2019 under six different scenarios using Visual PROMETHEE. The study is original in developing a composite index with corresponding metrics, providing a comparative evaluation across OECD countries, and offering a comprehensive analysis utilizing almost all major visual analysis tools of the Visual PROMETHEE. The results indicate that the metric set selected and the methodology applied with the visual tools of PROMETHEE provide a systematic which enables country-based benchmarking under different dimensions and enable policymakers to identify the improvement opportunities for each dimension.

The evaluation of the severity of the factors influencing road accidents with a detailed severity distribution is critical to plan evidence-based road safety improvements and strategies. However, currently available studies use statistical and machine learning (ML) models to evaluate the severity of factors causing road accidents without a detailed severity distribution. Further, most of these available models require significant pre-data processing and have certain data-centric limitations. However, the multi criteria decision-making (MCDM) techniques have the potential to combine expert opinions for robust analysis without any pre-data processing calculations. Thus, this study uses a hybrid analytic hierarchy process (AHP) and the preference ranking organisation method for enrichment evaluation (PROMETHEE) approach to analyse the severity of factors and characteristics that influence road accidents within the Gujarat state, using injury types as criteria and minor road accident influencing factors as alternatives. These 82 minor factors have been further characterised into 18 characteristics and 4 major factors. Further, AHP integrated 40 expert inputs to determine criterion weights, while PROMETHEE ranked all minor variables. Then, after applying k-mean clustering, each ranked factor has been classified as very severe, moderately severe, or severe. The result clearly highlights that overspeeding, male gender, and clear weather conditions have been concluded to be the highly severe factors for Gujarat state. Thus, by providing a clear severity analysis and distribution of factors influencing road accidents, the proposed research may help government stakeholders, researchers, and politicians build severity-based road safety reforms and strategies with clarity.

Along with globalization and the development of technology, commercial activities that are increasing both domestically and with imports and exports have brought banks and the banking sector to an important point in terms of economy. Banks' financial performance researches are generally one of the most important issues in terms of both theory and practice. In the literature, it is seen that many multi-criteria decision making methods are used for financial performance measurements. In the study, it is aimed to measure the financial performances of 20 public, private or foreign capital banks operating in Turkey with BWMbased TOPSIS, PROMETHEE and COPRAS methods and to interpret the findings comparatively. In the study, banks are evaluated by determining 10 criteria under the CAMELS variables. At the same time, the results of the ranking of the banks obtained as a result of the study are compared with the Spearman rank correlation analysis. The study involves the years between 2009 and 2020. As a result of the study, it is concluded that public banks have higher performance and the findings of all three methods are similar in terms of methods, but the PROMETHEE method is more applicable in terms of ease of application and useful information it provides. It is thought that these results would contribute to the literature of multi-criteria decision making methods and the banking sector. [ABSTRACT FROM AUTHOR]