Publikační činnost Katedry systémového inženýrství a informatiky/ Publications of Department of System Engineering and Informatics(157)

Permanent URI for this collectionhttp://hdl.handle.net/10084/100633

Kolekce obsahuje bibliografické záznamy publikační činnosti (článků) akademických pracovníků Katedry systémového inženýrství a informatiky(157) v časopisech registrovaných ve Web of Science od roku 2003 po současnost.
Do kolekce jsou zařazeny:
a) publikace, u nichž je v originálních dokumentech jako působiště autora (adresa) uvedena Vysoká škola báňská-Technická univerzita Ostrava (VŠB-TUO),
b) publikace, u nichž v originálních dokumentech není v adrese VŠB-TUO uvedena, ale autoři prokazatelně v době jejich zpracování a uveřejnění působili na VŠB-TUO.

Bibliografické záznamy byly původně vytvořeny v kolekci Publikační činnost akademických pracovníků VŠB-TUO, která sleduje publikování akademických pracovníků od roku 1990.

V ak. roce 2024/2025 sloučeno s katedrou 155, název změněn z Katedra systémového inženýrství na Katedra systémového inženýrství a informatiky.

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Now showing 1 - 20 out of 89 results

On simulation of the 2D P colony with evolving environment
(Springer Nature, 2024) Langer, Miroslav; Valenta, Daniel; Patnaik, Pawan Kumar
To obtain more results in the field of formal models of multi-agent systems it is necessary to step outside of the research done on the paper and move forward to computer simulation. The simulation opens the possibilities to study the behavior of studied model in real-time, or verify designed configurations. To develop suitable application of a good quality and fulfilling all the needs, it is necessary to base the development on detailed analysis. In this paper, we present a development of a simulator of a 2D P colony with the evolving environment. The simulator replaces the previous version of the software we used to simulate the behavior of the 2D P colony with evolving environment. In the previous version, the configuration of the colony was hardcoded into the source code of the simulator; hence, any changes had to be done by developing a new version of the simulator. Recent version is modular and the definition of the simulated colony is held in the separate configuration file. The result of the development is an application in the Python programming language with desired features defined in the analysis of the application. The application was developed under the Open Source license; hence, it is freely accessible and can be used or modified by anyone.
Integrated subsystems of materials and information flow for continuous manufacturing of coal and steel
(Sciendo, 2024) Danel, Roman; Gajdzik, Bożena
With the concept of Industry 4.0 production processes are moving towards autonomy and intelligence. Technol ogies equipped with artificial intelligence (AI) are involved into processes that are more and more digitized. Col laborative technologies are a feature of discrete processes. The automotive industry has achieved many successes in the process innovation towards smart factories. Other plants, such as smelters or coal mining are also striving to develop smart manufacturing with integrated computer systems to support processes. A continuous produc tion is different from a discrete or batch production. Industry 4.0 concept is focused on discrete production (with high level of automation and robotization of manufacturing) meanwhile there is a gap in implementation of these approach in the continuous production. The objective of the publication is to prepare and design the integrated computer management system based on processes realized in coal and steel manufacturing. Coal and steel pro duction are key elements in a chain of any industrial manufacturing e.g. automotive or machinery engineering. These processes are crucial in building of smart value chain. In our paper we present the structure of processes for the continuous production. Based the processes model we proposed the next steps to build the smart manu facturing for continuous production.
A data envelopment analysis model for opinion leaders’ identification in social networks
(Elsevier, 2024) Baziyad, Hamed; Kayvanfar, Vahid; Toloo, Mehdi
Through Online Social Networks (OSNs) such as Instagram, X (Twitter), and Facebook, employing Opinion Leaders (OLs) is becoming integral to companies' strategies for influencing the schema_dspacedb. The graph -based methods are one of the most important approaches for finding OLs in OSNs. Social Network Analysis (SNA)-based OLs finding methods deal with a considerable amount of data due to using entire relationships between all of the users in a network, which makes the algorithms time-consuming. Our main goal is to introduce a new method of OLs discovery that works with fewer data and maintains or improves performance metrics. Consequently, a new application of the Data Envelopment Analysis (DEA) method is presented here for OLs identification in social media. Another contribution of this paper is introducing a new framework (OL-Finder Evaluator or OLFE) for validating the OLs' detection algorithms under imbalanced datasets. DEA methods, when compared with SNA methods, have the advantage of being able to apply over non -graph -based datasets and to work with substantially smaller datasets. In contrast, SNA methods require transparent relationships between people. In this study, we compare both DEA (including CCR and BCC) and SNA measures (including "Betweenness (BC)," "Degree (DC)," "Page Rank (PRC)," "Closeness (CC)," and "Eigenvector (EC)" centralities) on a real Instagram network for OLs detection. Compared with SNA, our proposed method can identify OLs with considerably fewer data. Besides the advantages of DEA for time -saving, close competition exists between the DEA and the SNA methods. On average, DEA performs better in accuracy, precision, recall, and F1 -score performance metrics.
An extended proximity relation and quantified aggregation for designing robust fuzzy query engine
(Elsevier, 2024) Hudec, Miroslav; Vučetić, Miljan; Barčáková, Nina
In this article, we propose a novel model of a robust fuzzy query engine that addresses vagueness in data and users’ requirements. It aims to assist users in recommending similar products or services by retrieving the most suitable entities when the limitations of queries and recommendation approaches are recognized. The proposed fuzzy engine model considers various complex aspects, including imprecise preferences explained by linguistic terms, uncertain data in datasets, connections among elementary requirements, and the lack of historical data necessary for personalized recommendations. To achieve this goal, we propose a similarity matching based on the extended proximity relation and an adapted conformance measure for elementary requirements. In this direction, a new monotonicity property for proximity relation is introduced to ensure consistency in similarities among ordinal categorical data (including binary data) expressed by crisp or fuzzy numbers. Therefore, the conformance measure used to evaluate the similarity between user requirements and attribute values is expressed as a fuzzy number. Next, we propose a quantified aggregation of elementary requirements by strictly monotone fuzzy relative quantifiers. The flexibility is further extended by a convex combination of possibility and necessity measures. A hotel selection experiment is being carried out to explore the potential of the proposed fuzzy query engine. Finally, the limitations and usefulness of the proposed approach are addressed.
Education performance of Czech public higher education institutions using data envelopment and panel regression analysis
(Česká zemědělská univerzita v Praze, 2023) Hančlová, Jana; Chytilová, Lucie
The priority goals of the development of Czech higher education include ensuring the quality of its activities, improving the availability and relevance of flexible forms of education, and increasing efficiency in teaching and research. Several professional articles evaluated educational efficiency, but the proposed models did not include unemployed graduate students. The paper assesses education efficiency at public universities in the Czech Republic in 2020-2021 using an extended Data envelopment model with undesirable outputs, non-proportional and non-radial measures of distance from the efficient frontier. The influence of selected economic, social, regional and institutional factors on education efficiency is estimated by a panel regression model using the Feasible generalized least squares method. The results document the level and development of education efficiency and find insufficient reduction of unemployed graduates as a critical problem of inefficiency. More prominent universities achieve higher education efficiency. The main statistically significant factors influencing changes in education efficiency are population density, the unemployment rate, the location of the university in larger urban centres and the number of students per university employee.
The complex evaluation of the impact of COVID-19 pandemic at universities: A soft computing approach
(Česká zemědělská univerzita v Praze, 2023) Zapletal, František; Hudec, Miroslav; Švaňa, Miloš; Chytilová, Lucie; Hlaváček, Karel; Lokaj, Aleš; Urbanek, Anna; Glova, Jozef; Samartinho, João Paulo; Rodriguez, Cristina Maria Costa; Guðnason, Stefán
The COVID-19 pandemic impacted the educational process since the teaching process has been forced to go online in many countries. This enforced change revealed the weaknesses and strengths of the national educational systems and particular institutions. This article aims to analyse the impact of COVID-19 at selected European universities and assess the satisfaction of students, teachers, IT staff and management. This study is unique for its systematicity and complexity – it aggregates the opinions of all interested groups of stakeholders, distinguishes several time periods (before, during and after the pandemic), and allows the respondents to express hesitance in their evaluation. The evaluation model uses fuzzy sets to capture the uncertainty and to aggregate the opinions of different stakeholder groups. The empirical results show that most of the satisfaction development is the same or similar for all institutions examined. Then, the pandemic strongly influenced the satisfaction of all stakeholder groups at the universities examined. This impact was mostly negative, however, several lessons learnt have been revealed. Therefore, it was shown that it is highly beneficial to include these aspects to obtain a reliable picture of overall satisfaction.
Entropy-based air quality monitoring network optimization using NINP and Bayesian maximum entropy
(Springer Nature, 2023) Haddadi, Ali; Nikoo, Mohammad Reza; Nematollahi, Banafsheh; Al-Rawas, Ghazi; Al-Wardy, Malik; Toloo, Mehdi; Gandomi, Amir H.
Effectual air quality monitoring network (AQMN) design plays a prominent role in environmental engineering. An optimal AQMN design should consider stations’ mutual information and system uncertainties for effectiveness. This study develops a novel optimization model using a non-dominated sorting genetic algorithm II (NSGA-II). The Bayesian maximum entropy (BME) method generates potential stations as the input of a framework based on the transinformation entropy (TE) method to maximize the coverage and minimize the probability of selecting stations. Also, the fuzzy degree of membership and the nonlinear interval number programming (NINP) approaches are used to survey the uncertainty of the joint information. To obtain the best Pareto optimal solution of the AQMN characterization, a robust ranking technique, called Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) approach, is utilized to select the most appropriate AQMN properties. This methodology is applied to Los Angeles, Long Beach, and Anaheim in California, USA. Results suggest using 4, 4, and 5 stations to monitor CO, NO2, and ozone, respectively; however, implementing this recommendation reduces coverage by 3.75, 3.75, and 3 times for CO, NO2, and ozone, respectively. On the positive side, this substantially decreases TE for CO, NO2, and ozone concentrations by 8.25, 5.86, and 4.75 times, respectively.
Selecting slacks-based data envelopment analysis models
(Elsevier, 2023) Toloo, Mehdi; Tone, Kaoru; Izadikhah, Mohammad
Data envelopment analysis (DEA) is a well-known data-driven mathematical modeling approach that aims at evaluating the relative efficiency of a set of comparable decision making units (DMUs) with multiple inputs and multiple outputs. The number of inputs and outputs (performance factors) plays a vital role for successful applications of DEA. There is a statistical and empirical rule in DEA that if the number of performance factors is high in comparison with the number of DMUs, then a large percentage of the units will be determined as efficient, which is questionable and unacceptable in the performance evaluation context. However, in some real-world applications, the number of performance factors is relatively larger than the number of DMUs. To cope with this issue, selecting models have been developed to select a subset of performance factors that lead to acceptable results. In this paper, we extend a pair of optimistic and pessimistic approaches, involving two alternative individual and summative selecting models, based on the slacks-based model. We mathematically validate the proposed models with some theorems and lemmas and illustrate the applicability of our models using 18 active auto part companies in the largest stock exchange in Iran.
Measuring the digital divide: A modified benefit-of-the-doubt approach
(Elsevier, 2022) Mahdiloo, Mahdi; Andargoli, Amir E.; Toloo, Mehdi; Harvie, Charles; Duong, Thach-Thao
In this paper, a modified composite index is developed to measure digital inclusion for a group of cities and regions. The developed model, in contrast to the existing benefit-of-the-doubt (BoD) composite index literature, considers the subindexes as non-compensatory. This new way of modeling results in three important properties: (i) all subindexes are taken into account when assessing the digital inclusion of regions and are not removed (substituted) from the composite index, (ii) in addition to an overall composite index (aggregation of the subindexes), partial indexes (aggregated scores for each subindex) are also provided so that weak performances can be detected more effectively than when only the overall index is measured, and (iii) compared with current BoD models, the developed model has improved discriminatory power. To demonstrate the developed model, we use the Australian digital inclusion index as a real-world example.
Robust non-radial data envelopment analysis models under data uncertainty
(Elsevier, 2022) Hatami-Marbini, Adel; Arabmaldar, Aliasghar; Toloo, Mehdi; Nehrani, Ali Mahmoodi
Russell measure (RM) and enhanced Russell measure (ERM) are popular non-radial measures for efficiency assessment of decision-making units (DMUs) in data envelopment analysis (DEA). Input and output data of both original RM and ERM are assumed to be deterministic. However, this assumption may not be valid in some situations because of data uncertainty arising from measurement errors, data staleness, and multiple repeated measurements. Interval DEA (IDEA) has been proposed to measure the interval efficiencies from the optimistic and pessimistic viewpoints while the robustness of the assessment is questionable. This paper draws on a class of robust optimisation models to surmount uncertainty with a high degree of robustness in the RM and ERM models. The contribution of this paper is fivefold; (1) we develop new robust non-radial DEA models to measure the robust efficiency of DMUs under data uncertainty, which are adjustable based upon conservatism levels, (2) we use Monte-Carlo simulation in an attempt to identify an appropriate range for the budget of uncertainty in terms of the highest conformity of ranking results, (3) we introduce the concept of the price of robustness to scrutinise the effectiveness and robustness of the proposed models, (4) we compare the developed robust models in this paper with other existing approaches, both radial and non-radial models, and (5) we explore an application to assess the efficiency of the Master of Business Administration (MBA) programmes where data uncertainties in-fluence the quality and reliability of results.
Multi-stage stochastic optimization of carbon risk management
(Elsevier, 2022) Zapletal, František; Šmíd, Martin; Kozmík, Václav
Emissions trading within the Emissions Trading Scheme of the European Union (EU ETS) strongly influences European industrial companies. The companies must choose their strategy of reduction the costs of emissions allowances as possible. The changing system's conditions and volatile prices of allowances make this decision challenging. The main aim of this study is to compare different ways of risk management: banking (i.e., buying the allowances in forward) and using derivatives: futures and options. Despite several studies devoted to the relationship between the EU ETS and companies have already been published, there is still a gap in this field. Namely, the published studies have been substantially simplified so far by ignoring the risk of driving parameters. We construct a realistic large-scale stochastic optimization model, which avoids the mentioned simplifications. We use the Markov Stochastic Dual Dynamic Programming algorithm (MSDDP) to find the optimal solution. We apply the model to the data of a real-life industrial company. We find that banking is the most costly way of risk reduction, while using derivatives is efficient in risk reduction. Surprisingly, out of the derivatives, it is always optimal to use futures and not to use options. These results are confirmed by a thorough sensitivity analysis. The preference of the futures over options is mainly due to the less price of futures in comparison to options reducing risk equivalently.
Assessment of risk-sharing ratio with considering budget constraint and disruption risk under a triangular Pythagorean fuzzy environment in public–private partnership projects
(Elsevier, 2022) Dorfeshan, Yahya; Taleizadeh, Ata Allah; Toloo, Mehdi
The public-private partnership (PPP) is a practical and standard model that has been at the center of attention over the past two decades. Sharing risk between government and investors has been a challenging issue over the last year. This study formulates a model that aims to define the investors' longing and allocate risks to the government in a logical range. Besides, in some real-world conditions, foreign investors with lower cost, higher quality, and better technology than domestic investors partner with the government. Under this condition, it is essential to consider the disruption risks because of sanctions and currency price fluctuations. Furthermore, the limited budget of the government for investing in infrastructure projects is intended. In this paper, the government's disruption risks and limited budget are added to the risk-sharing ratio model for the first time in literature. Moreover, the Pythagorean fuzzy sets (PFSs) are applied to cope with the uncertainty of real-world conditions. The PFSs are more potent than classical and intuitionistic fuzzy sets (IFSs) in dealing with uncertainty. The PFSs provide the membership, non-membership, and hesitancy degree for experts to better address the derived uncertainty of real-world conditions. Also, compared with the IFSs, PFSs prepare more space, consequently providing more freedom to address the uncertainty. Finally, a case study is presented to illustrate the applicability and susceptibility of the suggested model. As disruption risks increase, general utility degree, government utility, and investor's effort decrease, and the guarantee risk ratio by government increases. Note that, investor's effort decreases because the government is forced to give the unfinished project to the domestic investor; consequently, exclusive terms arise for the domestic investor.
A comprehensive bibliometric analysis of fractional programming (1965-2020)
(MDPI, 2022) Toloo, Mehdi; Khodabandelou, Rouhollah; Oukil, Amar
Fractional programming (FP) refers to a family of optimization problems whose objective function is a ratio of two functions. FP has been studied extensively in economics, management science, information theory, optic and graph theory, communication, and computer science, etc. This paper presents a bibliometric review of the FP-related publications over the past five decades in order to track research outputs and scholarly trends in the field. The reviews are conducted through the Science Citation Index Expanded (SCI-EXPANDED) database of the Web of Science Core Collection (Clarivate Analytics). Based on the bibliometric analysis of 1811 documents, various theme-related research indicators were described, such as the most prominent authors, the most commonly cited papers, journals, institutions, and countries. Three research directions emerged, including Electrical and Electronic Engineering, Telecommunications, and Applied Mathematics.
Extending a fuzzy network data envelopment analysis model to measure maturity levels of a performance based-budgeting system: A case study
(Elsevier, 2022) Hatami-Marbini, Adel; Toloo, Mehdi; Amini, Mohamad Reza; Azar, Adel
Performance-based budgeting (PBB) aims to formulate and manage public budgetary resources to improve managerial decisions based on actual performance measures of agencies. Although the PBB system has been overwhelmingly applied by various agencies, the progress and maturity of its implementation process are not satisfactory at large. Therefore, it warrants to find, evaluate and improve the performance of organisations in relation to implementing a PBB system. To do so, the composite indicators (CIs) have been proposed to aggregate multiple indicators associated with the PBB system, but their employment is contentious as they often lean on ad- hoc and troublesome assumptions. Data envelopment analysis (DEA) methods as a powerful and established tool help to contend with key limitations of CIs. Although the original DEA method ignores an internal production process, the knowledge of the internal structure of the PBB systems and indicators is of importance to provide further insights when assessing the performance of PBB systems. In this paper, we present a budget assessment framework by breaking a PBB system into two parallel stages including operations performance (OP) and financial performance enhancement (FPE) to open up the black-box structure of the system and consider the indicator hierarchy configuration of each stage. In situations of the hierarchical configuration of indicators, we develop a multilayer parallel network DEA-based CIs model to measure the PBB maturity levels of the system and its stages. It is shown that the discrimination power of the proposed multilayer model is better than the existing models with one layer and in situations of relatively small number of DMUs the model developed in this paper can be a good solution to the dimension reduction of indicators. Moreover, this research leverages fuzzy logic to surmount the subjective information that is often available in collecting indicators of the PBB systems. The major contribution of this research is to examine a case study of a PBB maturity award in Iran, as a developing country with a myriad of financial challenges, to adopt a PBB maturity model as well as point towards the efficacy and applicability of the proposed framework in practice.
Robust optimization and its duality in data envelopment analysis
(Elsevier, 2022) Toloo, Mehdi; Mensah, Emmanuel Kwasi; Salahi, Maziar
Robust Data Envelopment Analysis (RDEA) is a DEA-based conservative approach used for modeling uncertainties in the input and output data of Decision-Making Units (DMUs) to guarantee stable and reliable performance evaluation. The RDEA models proposed in the literature apply robust optimization techniques to the linear and conventional DEA models which lead to the difficulty of obtaining a robust efficient DMU. To overcome this difficulty, this paper tackles uncertainty in DMUs from the original fractional DEA model. We propose a robust fractional DEA (RFDEA) model in both input and output orientation which enables us to overcome the deficiency of existing RDEA models. The linearized models of the fractional DEA are further used to establish duality relations from a pessimistic and optimistic view of the data. We show that the primal worst of the multiplier model is equivalent to the dual best of the envelopment model. Furthermore, we show that the robust efficiency in the input-and output-oriented DEA models are still equivalent in the new approach which is not the case in conventional RDEA models. We finally present a study of the largest airports in Europe to illustrate the efficacy of the proposed models. The proposed RDEA is found to provide an effective management evaluation strategy under uncertain environments.
Optimization of large-scale frame structures using fuzzy adaptive quantum inspired charged system search
(Springer Nature, 2022) Talatahari, Siamak; Azizi, Mahdi; Toloo, Mehdi; Shishehgarkhaneh, Milad Baghalzadeh
In this paper, a metaheuristic-based design approach is developed in which the structural design optimization of large-scale steel frame structures is concerned. Although academics have introduced form-dominant methods, yet using artificial intelligence in structural design is one of the most critical challenges in recent years. However, the Charged System Search (CSS) is utilized as the primary optimization approach, which is improved by using the main principles of quantum mechanics and fuzzy logic systems. In the proposed Fuzzy Adaptive Quantum Inspired CSS algorithm, the position updating procedure of the standard algorithm is developed by implementing the center of potential energy presented in quantum mechanics into the general formulation of CSS to enhance the convergence capability of the algorithm. Simultaneously, a fuzzy logic-based parameter tuning process is also conducted to enhance the exploitation and exploration rates of the standard optimization algorithm. Two 10 and 60 story steel frame structures with 1026 and 8272 structural members, respectively, are utilized as design examples to determine the performance of the developed algorithm in dealing with complex optimization problems. The overall capability of the presented approach is compared with the Charged System Search and other metaheuristic optimization algorithms. The proposed enhanced algorithm can prepare better results than the other metaheuristics by considering the achieved results.
Sustainably resilient supply chains evaluation in public transport: A fuzzy chance-constrained two-stage DEA approach
(Elsevier, 2021) Izadikhah, Mohammad; Azadi, Majid; Toloo, Mehdi; Hussain, Farookh Khadeer
Owing to today's highly competitive market environments, substantial attention has been focused on sustainably resilient supply chains (SCs) over the last few years. Nevertheless, very few studies have focused on the efficiency evaluation analysis of the sustainability and resilience of SCs as an inevitable essential in any profitable business. This study aims to address this issue by proposing a novel fuzzy chance-constrained two-stage data envelopment analysis (DEA) model as an advanced and rigorous approach in the performance evaluation of sustainably resilient SCs. To the best of our knowledge, the current study is pioneering as it introduces a new fuzzy chance-constrained two-stage method that can be used to undertake the deterministic non-fuzzy programming of the proposed model. The proposed approach is validated and applied to evaluate a real case study including 21 major public transport providers in three megacities. The results demonstrate the advantages of the proposed approach in comparison to the existing approaches in the literature.
Haze emission efficiency assessment and governance for sustainable development based on an improved network data envelopment analysis method
(Elsevier, 2021) Wu, Xianhua; Ji, Zhiyong; Gong, Yeming; Chen, Yufeng; Toloo, Mehdi
Accurate evaluation of emission governance efficiency can build fundament to develop haze control strategy towards sustainable development. By features of the haze, we view the haze formation stage as the first subprocess and the haze control stage as the second sub-process. This paper proposes an additive aggregation network data envelopment analysis (DEA) model with undesirable intermediate measures and undesirable outputs, which have not been thoroughly studied in previous literature. We found the newly developed network DEA model was nonlinear and cannot be converted into a linear program, and then developed an improved second-order cone programming approach to solve this problem. After analyzing the data of haze control in China, we drew the following conclusions: Firstly, different weights of preference for two sub-process can lead to the variation in the overall efficiency. Under different weights of preference, although the efficiency of the haze formation has a very small change in some provinces, the efficiency of the haze control has a large change. Secondly, decision makers can achieve the adjust goal of reducing haze by adjusting their preferences on the information of the haze formation and haze control stages, which are helpful for policy making in haze control strategy and sustainable development.
Robust worst-practice interval DEA with non-discretionary factors
(Elsevier, 2021) Arabmaldar, Aliasghar; Mensah, Emmanuel Kwasi; Toloo, Mehdi
Traditionally, data envelopment analysis (DEA) evaluates the performance of decision-making units (DMUs) with the most favorable weights on the best practice frontier. In this regard, less emphasis is placed on non-performing or distressed DMUs. To identify the worst performers in risk-taking industries, the worst-practice frontier (WPF) DEA model has been proposed. However, the model does not assume evaluation in the condition that the environment is uncertain. In this paper, we examine the WPF-DEA from basics and further propose novel robust WPF-DEA models in the presence of interval data uncertainty and non-discretionary factors. The proposed approach is based on robust optimization where uncertain input and output data are constrained in an uncertainty set. We first discuss the applicability of worst-practice DEA models to a broad range of application domains and then consider the selection of worst-performing suppliers in supply chain decision analysis where some factors are unknown and not under varied discretion of management. Using the Monte-Carlo simulation, we compute the conformity of rankings in the interval efficiency as well as determine the price of robustness for selecting the worst-performing suppliers.
Revised PROMETHEE algorithm with reference values
(Springer Nature, 2021) Zapletal, František
PROMETHEE method is a very popular quantitative method of decision-making with many benefits. However, the evaluation of alternatives in the original PROMETHEE method is derived only from differences in values, i.e., regardless the performance values themselves. In some situations, ignoring these values can distort the final results. This paper brings several examples of such situations, for which the original PROMETHEE fails and does not bring reliable results. Ishizaka and Resce (Soft Comput 22:7325-7338, 2018) have recently introduced the modification of PROMETHEE which considers the performance values, but also changed substantially the logic of the ranking algorithm. The aim of this paper is to modify the original PROMETHEE method to make it possible to include the performance values, without losing any main benefit of the original method and with keeping the original logic of the algorithm based on pair-wise comparisons. Two particular preference functions' types are proposed for the proposed extension (Gaussian function and strictly concave function), whose choice depends on the performance of the worst-performing alternative under consideration. In addition, the new algorithm is provided also in the fuzzy environment, which is useful if the decision-maker is not able to set the input parameters of the preference function precisely. Both the deterministic and fuzzy extensions are demonstrated using numerical examples. The results show that the final ranking can be strongly influenced by the level of performance. Moreover, the fuzzy extension brings richer information through the natural interpretation provided by possibility and necessity measures if the parameters of the preference functions are imprecise.

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