PROMETHEE Method Research Articles

Selection of Artificial Intelligence Technology For Digitalization of Order Fulfillment on Drug Factory Using Multi Criteria Decision Making

Objective: This paper aims to identify the most suitable Artificial Intelligence (AI) technology for the order fulfillment process in pharmaceutical factory, along with the key criteria and sub-criteria for successful AI implementation. Theoretical Framework: The study integrates theories of digital transformation, process optimization, and decision-making. Method: The research assesses digital readiness to evaluate a company's preparedness for digital transformation. The as-is process is mapped using BPMN to identify potential AI applications. AI technologies are then ranked using BWM and PROMETHEE methods, followed by scenario analysis to determine the optimal technology. Results and Discussion: Demand Forecasting is the top AI technology for adoption in the pharmaceutical factory's order fulfillment process, reducing cycle time by 29.50% to 21.80 hours. Research Implications: The research contributes to the understanding of selecting the most effective AI technology for optimizing the order fulfillment process in pharmaceutical companies, providing valuable insights for improving efficiency. Originality/Value: The study proposes a perspective on applying AI to optimize the order fulfillment process in pharmaceutical factory, offering valuable insights for improving operational efficiency through digital transformation.

The Evaluatıon Of Technıcal Unıversıtıes In Turkey In Accordance Wıth Tübitak Entrepreneurıal And Innovatıve Unıversıty Index

Educational institutions compete with each other with entrepreneurial and innovative steps, just like production enterprises operating in different sectors. The Scientific and Technical Research Council of Turkey (TÜBİTAK) has been evaluating universities on a total of twenty-three sub-dimensions under four dimensions in line with the Entrepreneurial and Innovative University Index (GYÜE) every year since 2012. It shares the top fifty universities in the ranking with the public. These universities include both state universities and foundation universities. Eight of the top fifty universities in the ranking are Technical Universities. Since technical universities can integrate into more university-industry collaborations, they can undertake innovative and entrepreneurial projects and create value. In this context, the aim of the study is; It is the evaluation and ranking of Technical Universities in line with TÜBİTAK-GYÜE. Method of the research; The criterion weights of the technical universities included in the TÜBITAK 2022 index were determined by the AHP method, and in this direction, the findings were obtained by using the PROMETHEE method. The study is also important in terms of determining the differences between technical universities and other universities in the context of the entrepreneurial and innovative university index and the reasons for these differences.

Analysis of the Application the Promethee Method in Determining Work Partners at the Central Statistics Agency of Central Aceh Regency

The selection of partners at the Central Statistics Agency of Central Aceh Regency faces challenges because manual selection methods are considered inefficient and inaccurate. Therefore, this study aims to improve the accuracy and efficiency of the selection process by applying the Promethee (Preference Ranking Organization Method for Enrichment Evaluations) method. This method is used as a multi-criteria decision-making technique and is implemented using the Python programming language for automatic and real-time data processing. The data used comes from the 2024 partner recruitment process at the Central Statistics Agency of Central Aceh Regency, consisting of candidates who have met the administrative requirements. The validity of the promethee method is tested through the calculation of an error percentage that reaches 10%, demonstrating the method's ability to filter partners according to the established criteria. In conclusion, the application of the promethee method has been proven to improve the efficiency and accuracy of the partner selection process, where the Python-based system developed can reduce the time and effort required in decision-making while producing more valid and reliable partner recommendations.

Location selection for logistics centre using PROMETHEE method

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.

Identification and Ranking of Factors Affecting the Emission of CO Gas in Karkar Coal Mine

Karkar coal mine is one of the biggest mines in the country, where mining has been going on since 1938. During the mining period, the Karkar mine has witnessed unfortunate incidents caused by the release and explosion of gases, which have caused substantial financial and human losses. Carbon monoxide gas is one of the essential factors in the occurrence of accidents in the Karkar mine. This research includes literature reviews, field data collection using the CEM CO-181 model gas meter, and statistical calculations using Shannon entropy and Promethee methods. First, the concentration of CO gas was measured in ventilation tunnels, development, and excavation areas. Extraction workshops of the Karkar coal mine at different working times, and then 26 cases that may result in a reduction in accidents were used as effective criteria, and 9 cases were used as influential factors on CO emissions through the order and distribution of questionnaires and interviews with identified experts. The final weights of twenty-six effective criteria on the emission of CO gas were calculated based on the Shannon entropy method. As a result, the criterion of consumed oils with a final weight of 0.1790 was ranked first, and the criterion of lack of experience with a final weight of 0.1065 was ranked last. The influential factors have been ranked based on the amount of net flow and the parameter method. As a result, the factors of mining fire and coal dust explosion ranked first and last, respectively, with net flows of 0.55 and -0.84, and the rest of the factors are placed in different positions according to their net flow rate and have their effects on the emission of CO gas.

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.

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.

Comprehensive decision-making considerations in the transition to electrification transportation system in a developing country

Introducing suitable alternatives to the existing fossil fuels can provide insightful aspects of the expected benefits. In the energy sector, the transportation system is one of the key contributors to fossil fuel consumption and greenhouse gas productions. To elaborate on the performance of each introduced alternative, two perspectives are considered; short-term and long-term evaluations. In the short outlook, the PROMETHEE method is used for evaluation. Six scenarios are introduced based on the technical, economic, social, and policy criteria and each scenario benefits from different weights. In this scheme, the impacts of evolutions in the battery applications in the vehicles are investigated. Based on the short-term study, CNG and gasoline are considered the best options for the fuels of vehicles in Iran by taking into consideration the current situation. By viewing the technical and economic criteria, it was concluded that the Li-Ion battery provides better performance in comparison with gasoline in the long run. By 2040, the number of EVs will reach 10% of the overall vehicle production. It is obtained that the benefits of the electrical vehicles’ presence as the alternative to the internal combustion vehicles can provide growing interest in this outlook from 2.02 × 106 US$ in 2025 to 17.55 × 108 US$ in 2040.

Graph model for conflict resolution with hybrid information based on prospect theory and PROMETHEE method and its application in water resources conflict

Water resources are crucial for national economic growth, but industrial development has led to severe pollution, sparking conflicts between government and enterprises over their responsibilities. Addressing these water pollution conflicts is urgent. The Graph Model for Conflict Resolution (GMCR) offers a simple and logical approach to analyzing such conflicts with minimal data. However, current models overlook decision makers’ (DMs) heterogeneity, bounded rationality, and the theoretical foundation for preference ranking. To overcome these limitations, this study integrates hybrid information to account for DMs’ diverse knowledge and experience. Prospect theory is employed to capture DMs’ bounded rationality, enhancing the accuracy of decision outcomes. The PROMETHEE method is introduced for preference ranking during GMCR modeling, guided by multi-attribute decision-making (MADM) theory. Additionally, the four basic stability concepts are redefined and proven, expanding GMCR’s theoretical applications. Finally, a case study of the Huangshui River water conflict and the comparative analysis demonstrate the model’s validity and advantages.

Urban Polycentricity and Architectural Heritage: A PROMETHEE-Based Multicriteria Approach

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.

Minat Generasi Muda dalam Usaha Tani Padi Organik di Desa Gentungan Kecamatan Mojogedang Kabupaten Karanganyar

<p>The development of organic rice farming in Gentungan Village faced aging farmers. This research aims to assess the interest of the younger generation in organic rice farming in Gentungan Village, analyze the efforts made to increase youth interest in organic rice farming, and the impact of the existence of the Taruna Tani Lestari group. This research method uses descriptive qualitative. The location selection was purposively in Gentungan Village, Mojogedang Sub-district, Karanganyar. The number of informants in this research was 18, consisting of youth members of the Taruna Tani group, youth farmers not members of the Taruna Tani groups, and non-youth farmers—data analysis using Miles and Huberman using the Promethee method. Data validity used triangulation of sources and data collection methods. The results showed that youth interest in organic rice farming began to increase due to various efforts made by the village in collaboration with universities and other agricultural start-ups. The existing efforts include forming the Taruna Tani group, introducing technology and development of agricultural infrastructure, education and training in agriculture, collaborating with various stakeholders, establishing agro-tourism, and developing other commodities. However, the Gentungan Village government needs to formulate multiple policies in the Village Medium Term Development Plan (RPJM) related to efforts to increase youth interest in organic farming businesses to realize Gentungan Village as an Organic Village in 2030.</p>

Application of the PROMETHEE Method in Determining Scholarship Recipients at University

This study aims to use the PROMETHEE method as a decision support system in determining the recipients of the Academic Achievement Improvement Scholarship at Universitas Dharmas Indonesia (UNDHARI). The methodological steps include problem identification, analysis, goal setting, and application of the PROMETHEE method. In this study, criteria and alternatives have been identified to evaluate achievement improvement scholarship recipients. The criteria weights are set, and the criteria preference types are determined. After obtaining baseline data from questionnaire assessments, pairwise preference values and multicriteria preference index values are calculated. Then, ranking is performed using the Leaving Flow, Entering Flow, and Net Flow methods, resulting in the priority order of Achievement Improvement scholarship recipients. The ranking results show that Irza Silvia (IS) has the highest Net Flow value (0.30), while Aldian Virgo Vani (AV) has the lowest Net Flow value (-0.35). Thus, the priority order from highest to lowest is IS (Irza Silvia), AV (Abdullah Gimnastiar), RD (Rini Damayanti), YM (Yesnia Marni), and AV (Aldian Virgo Vani). In the context of Net Flow scores, these results indicate that Irza Silvia (IS) has the greatest chance of receiving the achievement improvement scholarship. This study provides important insights for UNDHARI in the scholarship recipient determination process using the PROMETHEE method as a decision-making tool.

New hybrid EC-PROMETHEE method with multiple iterations of random weight ranges: Step-by-step application in Python

The decision-making process consists of finding the best solution to an analyzed problem. This search is carried out in the face of countless interactions when analyzing an alternative criterion by criterion, under which weights are assigned that distinguish the degree of importance they have for the decision-makers. The definition of weight for each criterion gives rise to three lines of thought on the subject. There are objective, subjective, and hybrid methods. This discussion concerns the degree to which experts define the criteria weights. Based on this discussion, we developed a hybrid method to integrate the Entropy and CRITIC methods with the PROMETHEE method, called EC-PROMETHEE. The innovation of this method is that the combination of the Entropy and CRITIC methods does not result in a single set of weights. In reality, the weights generated by each method are used to define each criterion's upper and lower limits. The range of weights generated for each criterion is emulated "n" times and builds a set of weights that are applied to the ranking definition process. The model generates "n" rankings, defining a single ranking. In this article, we demonstrate a step-by-step application of a tool developed in Python called EC-PROMETHEE and use it as an example of the problem of choosing rotary-wing airplanes for application in the military police service.➢The method reduces discretion in determining the weights of the criteria;➢The innovation lies in the use of a range of weights for criteria;➢Consistency in defining the final ranking.

Comparative analysis of Turkey’s environmental performance with Eastern European countries according to international EPI 2022 data

In order to effectively address environmental issues, it is important for governments to identify the problems causing environmental pollution within their scope and to develop appropriate solution policies. In order to identify environmental problems and develop necessary policies, it is necessary to consider the environmental performance of countries. Therefore, measuring the environmental performance of countries is of great importance. One of the prominent indices in measuring environmental performance today is the Environmental Performance Index (EPI) developed by Yale University. The main purpose of EPI is to quantitatively measure and compare the environmental performances of countries in order to monitor their progress in environmental sustainability. As of 2022, EPI consists of three main criteria: climate change, environmental health, and ecosystem vitality. In this study, Turkey’s environmental performance was evaluated using the Entropy-based PROMETHEE method with international EPI 2022 data. Subsequently, the environmental performances of Turkey and other Eastern European countries were compared in terms of criterion superiority to determine their strengths and weaknesses. In this context, various sub-criteria were analyzed under main criteria such as climate change mitigation, air quality, sanitation and drinking water, heavy metals, waste management, biodiversity and habitat, ecosystem services, fisheries, acid rain, agriculture, and water resources. As a result of the analysis conducted as of 2022, it emerged that Turkey generally exhibited weak environmental performance among Eastern European countries and there is a need for improvement in each criterion. Giving priority to criteria such as Climate Change Mitigation, Sanitation and Drinking Water, Biodiversity and Habitat, Fisheries, and Acid Rain, in which Turkey obtained the lowest scores in the ranking, would be a more strategic approach.

“Facility location selection and layout planning through AHP, PROMETHEE, and CORELAP methods in the furniture industry

“Facility location selection and layout planning through AHP, PROMETHEE, and CORELAP methods in the furniture industry

Development of the Country’s Sustainable Cyberspace Strategy to Ensure the Country’s National Security

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.

A Collaborative Multi-Criteria Decision-Making Framework for the Adaptive Reuse Design of Disused Railways

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.

Ranking Search Results Based on User Preferences in the Absence of Personalized Statistics

The article considers the algorithm of search results ranking according to the compliance with user's expectations due to the feedback on the data of search results pre-release, which allowed to reduce the time of search for the necessary information. A numerical experiment aimed at evaluating the effectiveness of the proposed approach on the example of real estate objects is presented. Aggregated data of real estate listings were used as data, and the results of user surveys were used to evaluate the relevance. TOPSIS and PROMETHEE methods were used as pre-ranking algorithms. The ranking results were combined considering their order in the output of both methods. To implement the next step of the algorithm, the pre-release list was partitioned into classes for which users selected a few objects they liked. Machine learning models were trained on the partitioned data. Based on their average accuracy and variance estimates, a naive Bayesian classifier model was selected and used for subsequent computations. The results of further experiments showed the possibility of taking into account personal preferences when organizing search and selection of objects of interest in the absence of personalized statistics on the example of real estate objects. As a result of the experiments, the time of searching a group of results of interest was reduced by 74 % on average.

Multi-Criterion Client Selection for Efficient Federated Learning

Federated Learning (FL) has received tremendous attention as a decentralized machine learning (ML) framework that allows distributed data owners to collaboratively train a global model without sharing raw data. Since FL trains the model directly on edge devices, the heterogeneity of participating clients in terms of data distribution, hardware capabilities and network connectivity can significantly impact the overall performance of FL systems. Optimizing for model accuracy could extend the training time due to the diverse and resource-constrained nature of edge devices while minimizing training time could compromise the model's accuracy. Effective client selection thus becomes crucial to ensure that the training process is not only efficient but also capitalizes on the diverse data and computational capabilities of different devices. To this end, we propose FedPROM, a novel framework that tackles client selection in FL as a multi-criteria optimization problem. By leveraging the PROMETHEE method, FedPROM ranks clients based on their suitability for a given FL task, considering multiple criteria such as system resources, network conditions, and data quality. This approach allows FedPROM to dynamically select the most appropriate set of clients for each learning round, optimizing both model accuracy and training efficiency. Our evaluations on diverse datasets demonstrate that FedPROM outperforms several state-of-the-art FL client selection protocols in terms of convergence speed, and accuracy, highlighting the framework's effectiveness and the importance of multi-criteria client selection in FL.

A Developed Model and Fuzzy Multi-Criteria Decision-Making Method to Evaluate Supply Chain Nervousness Strategies

Nervousness is thought to be a source of confusion, instability, or uncertainty in SC systems due to disruptions and frequent changes in decisions. Nervousness persists even with consistent SCs, which arise from planning flexibility in response to changes, where responsiveness and customer satisfaction balance. Although the term “nervousness” is well known, to our knowledge no prior research has examined and explored supply chain nervousness strategies (SCNSs). This research explores supply chain nervousness strategies, factors, reduction methods, and recent trends in the supply chain’s relationship with nervousness. The main purpose of this research is to determine the comprehensive and relevant nervousness strategies in the supply chains, especially in light of the unprecedented development and change in business, economics, and technology and the fierce competition. SCN strategies are introduced in a developed model to designate SCN measurements and indicators, mitigation strategies and stages, and management strategies. The fuzzy PROMETHEE method is employed to rank the strategies based on their importance and order of implementation. The suggested method for managing nervousness is then presented with a numerical case, along with the results. The research outcomes indicate that the top five strategies for managing nervousness include planning continuity, utilizing technology, managing nervousness, improving the SC cyber system, and managing supplies. The findings assist decision makers, practitioners, and managers in focusing on SC improvement, resilience, and sustainability.