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Szilvia Jáhn-Erdős Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary

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Bence Kővári Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary

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Abstract

Fair treatment of individuals in a scheduling task is essential. Unfairness can cause dissatisfaction among workers, faster obsolescence of work tools and underutilization of others. The literature's definitions vary, and there is no clear definition of general scheduling tasks.

This article explores fair scheduling through the lens of final exams, aiming to extend decision support system methodologies. It proposes a method based on Lipschitz mapping to measure fairness and presents a pseudo-algorithm for estimating optimal trend lines.

The model and the algorithm are demonstrated using the example of final exam schedules. In this way, two feasible solutions can be measured and compared in terms of fairness.

Abstract

Fair treatment of individuals in a scheduling task is essential. Unfairness can cause dissatisfaction among workers, faster obsolescence of work tools and underutilization of others. The literature's definitions vary, and there is no clear definition of general scheduling tasks.

This article explores fair scheduling through the lens of final exams, aiming to extend decision support system methodologies. It proposes a method based on Lipschitz mapping to measure fairness and presents a pseudo-algorithm for estimating optimal trend lines.

The model and the algorithm are demonstrated using the example of final exam schedules. In this way, two feasible solutions can be measured and compared in terms of fairness.

1 Introduction

The concept of fairness is becoming increasingly important in many areas of life, in decision support systems, legal and economic issues, and optimization. However, the idea of fairness is not quantifiable, and it can be challenging to define what is meant by it. According to Cambridge Dictionary, fairness is defined as the quality of treating people equally or in a way that is right or reasonable [1]. The aim is to translate this mathematically.

In scheduling tasks, especially when the assignment of people is to determine, this aspect also comes to the foreground. An example of a real-life problem dealing with the scheduling of people is the scheduling of final exams, which is a special sub-task of scheduling problems where specific requirements constrain the state space. For schedules, there is usually a state space and requirements that a schedule must meet. Based on these, an objective function is settled, containing the schedule conditions. In the case of a final exam schedule, the constraints are separated into hard and soft requirements. If a hard requirement is violated, the assignment is not considered acceptable, while the soft requirements aim to meet as many of them as possible. These are built in the objective function as a weighted sum. The fairness requirement, however, is a constraint that cannot be embedded into the other constraints because it cannot be quantified explicitly. It cannot be included in the objective function but must be considered in a separate dimension. This is because one cannot say precisely what makes a final exam distribution fair. It could consider, for example, that for examiners, all examiners should be allocated to the same number of exams. Still, some examiners are teachers in 6–7 subjects, while others may only be allocated to 1 subject. Should there be an even distribution per subject? This may not give good results either, as some subjects require 1-2 students to be examined, while others require several hundred students to be examined individually. However, the aim is to treat instructors of similar competence similarly and fairly (as it is quoted in [1]), and none of the intuitive formulations can explain this. However, if one looks at the notions of fairness found in literature, it can be found that there is no general method or convention for dealing with fairness in the context of scheduling.

In other optimization tasks, however, the need for a general definition of fairness has been raised, for example, in decision support systems Grgic-Hlaca et al. [2]. The input information of the task is similar since a decision must be made from a given set of input properties. A typical example is lending, where the input information is the customer's data and income circumstances, which should be used to decide whether he/she can get a loan. In this case, similar input characteristics (e.g., whether a person can be a chairperson or secretary in a final exam, how many subjects they can sit for, etc.) should be used to determine how many exams it would be fair for them to sit for in the final schedule. In both cases, an essential factor is to never look at fairness as an individual but to define it by comparing candidates to each other.

In this paper, an attempt is made to extend the methodologies used in decision support systems for complex scheduling tasks, and an example is provided of how it can be used through the task of final exam scheduling.

2 Background

The following section discusses the context in which the definition of fairness has been used in literature for scheduling tasks and how fairness has been successfully defined for other optimization tasks, which can also help to create a general definition in scheduling.

2.1 Fairness definitions in scheduling problems

In many scheduling challenges, the concept of fairness is not taken into account at all in any sense (e.g., in articles of Neelakantan et al. [3] and Seyyed et al. [4]) instead, a set of requirements to be satisfied is given in the objective function. However, many articles use the word “fair”, and their interpretation can be divided into two categories.

In many scheduling problems, the task is to allocate a resource in which the definition of fairness is straightforward. In this case, a given resource is allocated equally or according to some given objective function weighting scheme. Some examples are presented below.

The processor utilization of computers is an example where the aim is to distribute the tasks or memory equally. A fair resource scheduler was presented by Shen et al. [5] for flash-based solid-state drives. There are also many recent kinds of research for scheduling algorithms for persistent memory, e.g., the article by Zhao et al. [6]. Even neural network-based control mechanisms were examined by Budhiraja et al. [7]. Fair queuing is also considered in the problem of allocating networks' bandwidth. For example, adaptive bandwidth binning is considered by Hong et al. [8], and weighted fair queues by Pan et al. [9].

Sometimes, the shift scheduling problem could be considered the same problem when the employees should be scheduled for equal timeslots. In his article, Ikeda et al. [10] worked on the same problem.

Another set of definitions of fairness in the literature deals with defining fairness in a concrete problem-specific case. In these cases, the notion of fairness is complex, it is not just the distribution of a resource, but fairness is formulated in terms specific to a given problem.

Woumans et al. [11] article considers timetabling fair if students have sufficient study time between exams.

According to Mansini et al. [12], fairness is accomplished when all doctors are assigned to all kinds of tasks in a hospital.

In his theory, Uhde et al. [13] considered collaboration with stakeholders as the key to achieving fair scheduling. Before assignment, interviews are conducted with hospital staff, and fairness is measured against their criteria.

In his paper Vetschera et al. [14] discusses how partial information in group decisions impacts fairness. He claims that fairness depends on how many different individual's interests are reflected fairly.

In his article, Jütte et al. [15] defined fairness as a soft constraint where the goal was an even distribution of the unpopular duties among depots. While according to Breugem et al. [16], fairness relates to the distribution of work among the roster groups, and he modeled fairness via hard and soft constraints in his context.

These cases have in common that they all gave a problem-specific definition and translated the problem of fairness into a specific task requirement, which thus can neither give a general definition nor measure actual fairness.

2.2 Fairness in decision support systems

The question of fairness can also be found in many other areas of life, e.g., social, ethical and legal issues. There is even a conference [17], where all areas of fairness are covered.

One of the most significant areas of optimization is the decision support systems to focus on fairness. Most of these efforts are in this area, so the following discusses the most relevant concepts.

The article of Verma et al. [18] on definitions of fairness summarizes what is considered fair in a credit decision support system (examines racial and gender discrimination in banking systems). He discusses different ethical issues and provides mathematical formulations of definitions. One of the most significant of these models is Dwork et al. [19] article, which uses the Lipschitz mapping.

Dwork applies the Lipschitz continuity property of functions to probability distributions in the following way, which he calls the Lipschitz mapping. Here, the mapping M is interpreted for each input V, on outputs (A), with distance definitions d and D. (He considers different distance metrics.) This relation is shown in in Eq. (1).

Fairness is interpreted as a linear programming problem to optimize a given loss function considering d and D distances,
If  M:V(A)and d:V×V,   then  DM(x),M(y)dx,y  x,yV.

In previous research, Erdős et al. [20] examined the model for scheduling tasks based on Dwork's article. So far, only the challenges have been interpreted; the actual model and algorithm are presented in the following.

3 Proposed method

The task is to apply the concepts of fairness for planning schedules so that the fairness of individual schedules can be measured. Based on Lipschitz mapping, the goal is to ensure that Eq. (1) is applied to the individuals to be scheduled.

The scope of the study is scheduling tasks where the resources are not homogeneous, so either different types of resources have to be assigned or each resource has different capabilities, i.e. not all individuals can perform all tasks.

Formally, a criterion taken from decision support systems (see Dwork et al. [19]) is that the distance between the distributions of the outputs of any two individuals is always less than or equal to the distance between the input properties of the same individuals. If the concept of Lipschitz mapping is defined in this way, it can also be interpreted for scheduling tasks. On this basis, it can be seen that the task is to schedule individuals with similar properties in a similar way, i.e., the closer two individuals are to each other in terms of properties (e.g., abilities), the more critical it is for their final scheduling (e.g., load number) to be close to each other. In this case, this means that the input information (e.g., how many and what tasks each individual can perform, how many of the tasks to be performed) determines what the ideal number of tasks to be taken on in the fair assignment would be. However, this can never be given individually; this information needs to be interpreted by comparing individuals in relation to each other.

The aim of this research is not to produce fair schedules but to find out how fair a completed schedule is. However, to interpret this, the definition of a fair schedule is needed based on the concept described by Dwork et al. [19].

Introduce the following definitions and notations: Let V denote the set of individuals, N the number of individuals, and n the number of input attributes, i.e., all the information is taken as a basis for the computation of the fairness to be interpreted on it (this means all the influencing factors). The xin vector aggregates the input properties of an individual i. Given these, V is defined as it can be seen in (2):
V={xi|xin,   iN}.

The s(x) gives the output information that is considered in terms of fairness, S(V) denotes the same for the whole schedule, i.e., S(V):=sx1,sx2,. It is important to interpret this for a schedule S that satisfies all the basic requirements of the scheduling task, i.e., only feasible solutions are considered (S gives the feasible solution to the given scheduling task).

Definition: T(x) trend-line tells us what the output of an individual would be in the ideal case, i.e., in the optimal case, s(x)=T(x).

Definition: A distributed scheduling is K-fair, if and only if for xV, s(x) is the element of the hyperspace bounded by T(x)±K, KR. In this case, Sk(V) denotes a K-fair scheduling.

Notion: KT denotes K value of a K-fair scheduling, which belongs to T(x).

Definition: Fair-optimal scheduling is a K-fair scheduling, for which it is true that the value K is the smallest for all other schedules. This means that the schedule Sk0 is fair-optimal, where k0k,  Sk(V).

However, finding the trend line T(x) is not trivial. Finding the optimal value of T(x) is an iterative process in which the goal is to estimate the optimal value of T(x) in a reverse algorithm.

The steps of the pseudo algorithm are the following.

  1. S schedule is given, looking for T(x), the corresponding KT;

  2. Denote T* the polynomial curve that best approximates T(x), V0:=V;

  3. Normalize the data, so the distance calculation between dimensions of different orders of magnitude does not cause distortion or numerical error;

  4. Detect outliers and remove them from the data set. V0:=V0\xoutlier. Practical algorithms for very outliers would significantly distort the expected results. Furthermore, i:=1 and V*:=V0;

  5. Fit a polynomial to V0 based on a heuristic; this trend line will be Ti. Calculate the K-fairness of Ti. T0:=Ti;

  6. V*:=V*\{xj}, where xj element is the “furthest” from the trend curve. The most significant improvement in the error function can be achieved by removing this element. (The furthest element causes the most significant deviation in absolute value in the error function).

  7. Fit a new polynomial (Ti+1) to the new V*. Calculate K-fairness of Ti+1 for V*;

  8. If KTi+1<KTi, (>0,R is an arbitrary small number), then i:=i+1 and GOTO 5;

  9. T*=T0, to obtain the curve of the fair-optimal scheduling trend, which is KT0-fair over V0.

4 Case study

The example of the final exam scheduling illustrates the practical application of the methodology described above. This study is based on a real data set, the final exam schedules of the Department of Automation and Applied Informatics, Budapest University of Technology and Economics. The schedule is an oral exam session, where students take the exam individually in front of a board of 5–6 members in parallel rooms. Up to 300 students may sit the exam in a semester, which must be completed in two weeks. The schedule has to comply with several rules described in the examination regulations. In addition, several human factors must be considered, e.g., considering availability or assigning instructors in blocks if possible. Another important aspect was to try to distribute the instructors' workload as fairly as possible. But as this should be interpreted as a separate dimension, for the reasons explained earlier, the model presented below will be used to test the fairness of previous years' manual final exam distributions.

Since only schedules are considered that satisfy all requirements except fairness, (and are therefore outside the scope of this present analysis), they can be taken as the default. Thus, it is possible to examine only the fairness conditions, filtering out the irrelevant factors.

For the purpose of the present case study, let us take the examiners as a basis, one or two of whom must sit on each committee, depending on whether the student who is taking his final examination is required to take one or two courses. The set of optional examination courses is given, as well as the instructors who can examine a course. Only an instructor who is also an instructor of a course can be an examiner for a subject. It is also important to note that some instructors may be examiners for more than one subject, depending on the number of subjects they teach. In most cases, an instructor is expected to be an examiner for more than one subject, but the distribution of this is quite variable, as it is shown in Fig. 1, where every course belongs to a dot.

Fig. 1.
Fig. 1.

Statistical information about courses

Citation: Pollack Periodica 19, 1; 10.1556/606.2023.00780

The fairness of a given schedule S is determined as follows, based on the algorithm presented in the previous section.

Initially, V0 = V, i.e., the entire set of individuals, in this case all examiners in the final exam schedule.

In the final examination schedule, three attributes determine the input data of an examiner (how many courses he/she can examine, how many students are examined in each course, and how many additional instructors can examine each course). These data compose the x attribute vector of an examiner. In the following example, x is normalized and then the mean value is calculated from these data as the input property of each examiner, which is x*.

In this example, the function s(x) gives the number of times in the final assignment that instructor with x attribute was scheduled. These can also be normalized and reduced to two dimensions to plot s(x*), as it is shown in Fig. 2.

Fig. 2.
Fig. 2.

The normalized input and output data of instructors

Citation: Pollack Periodica 19, 1; 10.1556/606.2023.00780

The next step is to filter out outliers. To do this, the distribution of the above data is examined and shown in Fig. 3.

Fig. 3.
Fig. 3.

Distribution and locals

Citation: Pollack Periodica 19, 1; 10.1556/606.2023.00780

These also show that there are both x* and s(x*) that fall outside the Q3+1.5·IQR, which is used for outlier detection Schwertman et al. [21].

Let us take these from V0 and, after filtering them out, fit the first polynomial to the data of the set V0, this will be T1(x). The value of the error of T1(x) in this example is calculated as R2, the square of the Pearson correlation coefficient, and is denoted by Err. From the possible polynomials, polynomial Pk of degree k is chosen, where Err(Pk+1)<=Err(Pk)1.03, i.e., the error function would not improve by more than 3% for a degree higher than this, thus eliminating overfitting. The R2 values and improvements for each polynomial are it is shown in Table 1. It shows that the polynomial of degree 3 will be chosen since an improvement of less than 3% can be achieved at degree 4. After 1st iteration R2=0.44, T1(x*)=0.32x*3+1.88x*22.45x*+2.96, and KT=1.54.

Table 1.

Performances of the trend-polynomials in 1st iteration

In the 2nd iteration the individual causing the largest deviation in the error function is removed from V*, as R2 can then be improved to 0.4895 and K-fairness reduced to 1.43 in the new V*. Since this improvement is significant, the 2nd iteration is on hold.

Table 2 shows the T(x) curve fitted after each iteration, the R2 value obtained, the K value obtained, and the improvement over the previous iteration.

Table 2.

Iterations of the proposed algorithm

In the 4th iteration, no improvement is achieved, and there is degradation, so at the end of iteration 3, the algorithm stops, and the T(x*) obtained in iteration 3 becomes the best approximation searched by the algorithm. Resetting V0, it can be obtained that the scheduling S is 1.63-fair. The final trend fit on the aggregate x* is shown in Fig. 4.

Fig. 4.
Fig. 4.

Fitting T(x*) curve for s(x*)

Citation: Pollack Periodica 19, 1; 10.1556/606.2023.00780

Of course, this data alone does not provide much information, but the method itself is essential, as the determination of fairness can be achieved. By doing this, a fairness value is assigned to schedule S, and several different schedules can be compared to each other in terms of fairness.

5 Conclusion

Previously, there was no uniform approach or definition of fairness in the literature for scheduling tasks. The possibility of creating a general mathematical concept is explored for evaluating and comparing the fairness of heterogeneous-resource allocating scheduling algorithms independently of the specific problem.

A model was created based on the Lipschitz mapping used in decision theory. The basic idea and the mathematical background of this model were interpreted for general scheduling problems, and an algorithm was constructed to compute the fairness of scheduling independent of the properties of the specific scheduling. With this algorithm, an approximation was also provided of what ideally expected output information would be for an individual with specific input data. The algorithm's operation on a real data set was also derived using a concrete example. Namely, this method was used to measure the inter-examiner fairness of a final exam assignment. This algorithm helps to compare how several different scheduling algorithms perform in terms of fairness.

All this could measure actual fairness because it was not just defined as a concrete requirement in the scheduling process but as general information handled in addition to it. For example, in the case of final exams, it is not just the workload number alone that provides information. However, all the available information is aggregated to determine how fair a person's assignment is compared to the others. After that, the aggregation of all this information is used to measure the fairness of the schedule.

Thus in the future, if this algorithm is included along with the scheduling process, it will be possible to select the one from several different schedules that are the fairest to individuals.

Acknowledgement

The work presented in this paper has been carried out in the frame of project no. 2019-1.1.1-PIACI-KFI-2019-00263, which has been implemented with the support provided by the National Research, Development, and Innovation Fund of Hungary.

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    Fairness Meaning in the Cambridge English Dictionary. Cambridge University Press, 2021. [Online]. Available: https://dictionary.cambridge.org/dictionary/english/fairness. Accessed: Dec. 20, 2022.

    • Search Google Scholar
    • Export Citation
  • [2]

    N. Grgic-Hlaca, E. Redmiles, K. P. Gummadi, and A. Weller, “Human perceptions of fairness in algorithmic decision making: A case study of criminal risk prediction,” in Proceedings of the World Wide Web Conference, Lyon, France, April 23–27, 2018, pp. 903912.

    • Search Google Scholar
    • Export Citation
  • [3]

    P. Neelakantan and A. Reddy, “Decentralized load balancing in distributed systems,” Pollack Period., vol. 9, no. 2, pp. 1528, 2014.

    • Search Google Scholar
    • Export Citation
  • [4]

    M. Seyyed, M. Javadi, and K. Ali, “A combined approach for cloud tasks scheduling based on NSGA-II and harmony search,” Pollack Period., vol. 17, no. 3, pp. 16, 2022.

    • Search Google Scholar
    • Export Citation
  • [5]

    K. Shen and S. Park, “FlashFQ: A fair queueing I/O scheduler for flash-based SSDS,” in 2013 Annual Technical Conference, San Joce, CA, June 26–28, 2013, pp. 6778.

    • Search Google Scholar
    • Export Citation
  • [6]

    J. Zhao, O. Mutlu and Y. Xie, “FIRM: Fair and high-performance memory control for persistent memory systems,” in 2014 47th Annual IEEE/ACM International Symposium on Microarchitecture, Cambridge, UK, December 13–17, 2014, pp. 153165.

    • Search Google Scholar
    • Export Citation
  • [7]

    I. Budhiraja, N. Kumar, and S. Tyagi, “Deep-reinforcement-learning-based proportional fair scheduling control scheme for underlay D2D communication,” IEEE Internet Things J., vol. 8, no. 5, pp. 31433156, 2020.

    • Search Google Scholar
    • Export Citation
  • [8]

    G. Hong, J. Martin, and J. Westall, “Adaptive bandwidth binning for bandwidth management,” Comput. Network., vol. 150, pp. 150169, 2019.

    • Search Google Scholar
    • Export Citation
  • [9]

    J. Pan, G. Chen, H. Wu, X. Peng and L. Xia, “Deep reinforcement learning-based dynamic bandwidth allocation in weighted fair queues of routers,” in 2022 IEEE 18th International Conference on Automation Science and Engineering, Mexico City, Mexico, August 20–24, 2022, pp. 15801587.

    • Search Google Scholar
    • Export Citation
  • [10]

    K. Ikeda, Y. Nakamura, and T. S. Humble, “Application of quantum annealing to nurse scheduling problem,” Scientific Rep., vol. 9, no. 1, 2019, Art no. 12837.

    • Search Google Scholar
    • Export Citation
  • [11]

    G. Woumans, L. De Boeck, J. Belien, and S. Creemers, “A column generation approach for solving the examination-timetabling problem,” Eur. J. Oper. Res., vol. 253, no. 1, pp. 178194, 2016.

    • Search Google Scholar
    • Export Citation
  • [12]

    R. Mansini and R. Zanotti, “Optimizing the physician scheduling problem in a large hospital ward,” J. Schedul., vol. 23, no. 3, pp. 337361, 2020.

    • Search Google Scholar
    • Export Citation
  • [13]

    A. Uhde, N. Schlicker, D. P. Wallach and M. Hassenzahl, “Fairness and decision-making in collaborative shift scheduling systems,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, New York, NY, USA, April 25–30, 2020, pp. 113.

    • Search Google Scholar
    • Export Citation
  • [14]

    R. Vetschera, P. Sarabando, and L. Dias, “Levels of incomplete information in group decision models--a comprehensive simulation study,” Comput. Operations Res., vol. 51, pp. 160171, 2014.

    • Search Google Scholar
    • Export Citation
  • [15]

    S. Jütte, D. Müller, and U. W. Thonemann, “Optimizing railway crew schedules with fairness preferences,” J. Schedul., vol. 20, pp. 4355, 2017.

    • Search Google Scholar
    • Export Citation
  • [16]

    T. Breugem, T. Schlechte, C. Schulz, and R. Borndörfer, “A three-phase heuristic for the fairness-oriented crew rostering problem,” Comput. Operations Res., vol. 154, 2023, Art no. 106186.

    • Search Google Scholar
    • Export Citation
  • [17]

    ACM Conference on Fairness, Accountability, and Transparency, Seoul, South Korea, June 21–24, 2022.

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    S. Verma and J. Rubin, “Fairness definitions explained,” in Proceedings of the International Workshop on Software Fairness, Gothenburg, Sweden, May 29, 2018, pp. 17.

    • Search Google Scholar
    • Export Citation
  • [19]

    C. Dwork, M. Hardt, T. Pitassi, O. Reingold and R. Zemel, “Fairness through awareness,” in Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, New York, NY, US, January 8–10, 2012, pp. 214226.

    • Search Google Scholar
    • Export Citation
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    S. Erdős and B. Kővári, “Examination of fairness in scheduling tasks with heterogeneous resources,” in 8th International Conference on Soft Computing & Machine Intelligence, Cario, Egypt, November 26–27, 2021, pp. 155159.

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    N. C. Schwertman, M. A. Owens, and R. Adnan, “A simple more general boxplot method for identifying outliers,” Comput. Stat. Data Anal., vol. 47, no. 1, pp. 165174, 2004.

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Senior editors

Editor(s)-in-Chief: Iványi, Amália

Editor(s)-in-Chief: Iványi, Péter

 

Scientific Secretary

Miklós M. Iványi

Editorial Board

  • Bálint Bachmann (Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Jeno Balogh (Department of Civil Engineering Technology, Metropolitan State University of Denver, Denver, Colorado, USA)
  • Radu Bancila (Department of Geotechnical Engineering and Terrestrial Communications Ways, Faculty of Civil Engineering and Architecture, “Politehnica” University Timisoara, Romania)
  • Charalambos C. Baniotopolous (Department of Civil Engineering, Chair of Sustainable Energy Systems, Director of Resilience Centre, School of Engineering, University of Birmingham, U.K.)
  • Oszkar Biro (Graz University of Technology, Institute of Fundamentals and Theory in Electrical Engineering, Austria)
  • Ágnes Borsos (Institute of Architecture, Department of Interior, Applied and Creative Design, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Matteo Bruggi (Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Italy)
  • Petra Bujňáková (Department of Structures and Bridges, Faculty of Civil Engineering, University of Žilina, Slovakia)
  • Anikó Borbála Csébfalvi (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Mirjana S. Devetaković (Faculty of Architecture, University of Belgrade, Serbia)
  • Szabolcs Fischer (Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architerture, Civil Engineering and Transport Sciences Széchenyi István University, Győr, Hungary)
  • Radomir Folic (Department of Civil Engineering, Faculty of Technical Sciences, University of Novi Sad Serbia)
  • Jana Frankovská (Department of Geotechnics, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • János Gyergyák (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Kay Hameyer (Chair in Electromagnetic Energy Conversion, Institute of Electrical Machines, Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Germany)
  • Elena Helerea (Dept. of Electrical Engineering and Applied Physics, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, Romania)
  • Ákos Hutter (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technolgy, University of Pécs, Hungary)
  • Károly Jármai (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Teuta Jashari-Kajtazi (Department of Architecture, Faculty of Civil Engineering and Architecture, University of Prishtina, Kosovo)
  • Róbert Kersner (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Rita Kiss  (Biomechanical Cooperation Center, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary)
  • István Kistelegdi  (Department of Building Structures and Energy Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Stanislav Kmeť (President of University Science Park TECHNICOM, Technical University of Kosice, Slovakia)
  • Imre Kocsis  (Department of Basic Engineering Research, Faculty of Engineering, University of Debrecen, Hungary)
  • László T. Kóczy (Department of Information Sciences, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, University of Győr, Hungary)
  • Dražan Kozak (Faculty of Mechanical Engineering, Josip Juraj Strossmayer University of Osijek, Croatia)
  • György L. Kovács (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Balázs Géza Kövesdi (Department of Structural Engineering, Faculty of Civil Engineering, Budapest University of Engineering and Economics, Budapest, Hungary)
  • Tomáš Krejčí (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Jaroslav Kruis (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Miklós Kuczmann (Department of Automations, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University, Győr, Hungary)
  • Tibor Kukai (Department of Engineering Studies, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Maria Jesus Lamela-Rey (Departamento de Construcción e Ingeniería de Fabricación, University of Oviedo, Spain)
  • János Lógó  (Department of Structural Mechanics, Faculty of Civil Engineering, Budapest University of Technology and Economics, Hungary)
  • Carmen Mihaela Lungoci (Faculty of Electrical Engineering and Computer Science, Universitatea Transilvania Brasov, Romania)
  • Frédéric Magoulés (Department of Mathematics and Informatics for Complex Systems, Centrale Supélec, Université Paris Saclay, France)
  • Gabriella Medvegy (Department of Interior, Applied and Creative Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Tamás Molnár (Department of Visual Studies, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Ferenc Orbán (Department of Mechanical Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Zoltán Orbán (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Dmitrii Rachinskii (Department of Mathematical Sciences, The University of Texas at Dallas, Texas, USA)
  • Chro Radha (Chro Ali Hamaradha) (Sulaimani Polytechnic University, Technical College of Engineering, Department of City Planning, Kurdistan Region, Iraq)
  • Maurizio Repetto (Department of Energy “Galileo Ferraris”, Politecnico di Torino, Italy)
  • Zoltán Sári (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Grzegorz Sierpiński (Department of Transport Systems and Traffic Engineering, Faculty of Transport, Silesian University of Technology, Katowice, Poland)
  • Zoltán Siménfalvi (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Andrej Šoltész (Department of Hydrology, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • Zsolt Szabó (Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Hungary)
  • Mykola Sysyn (Chair of Planning and Design of Railway Infrastructure, Institute of Railway Systems and Public Transport, Technical University of Dresden, Germany)
  • András Timár (Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Barry H. V. Topping (Heriot-Watt University, UK, Faculty of Engineering and Information Technology, University of Pécs, Hungary)

POLLACK PERIODICA
Pollack Mihály Faculty of Engineering
Institute: University of Pécs
Address: Boszorkány utca 2. H–7624 Pécs, Hungary
Phone/Fax: (36 72) 503 650

E-mail: peter.ivanyi@mik.pte.hu 

or amalia.ivanyi@mik.pte.hu

Indexing and Abstracting Services:

  • SCOPUS
  • CABELLS Journalytics

 

2022  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
14
Scimago
Journal Rank
0.298
Scimago Quartile Score

Civil and Structural Engineering (Q3)
Computer Science Applications (Q3)
Materials Science (miscellaneous) (Q3)
Modeling and Simulation (Q3)
Software (Q3)

Scopus  
Scopus
Cite Score
1.4
Scopus
CIte Score Rank
Civil and Structural Engineering 256/350 (27th PCTL)
Modeling and Simulation 244/316 (22nd PCTL)
General Materials Science 351/453 (22nd PCTL)
Computer Science Applications 616/792 (22nd PCTL)
Software 344/404 (14th PCTL)
Scopus
SNIP
0.861

2021  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
12
Scimago
Journal Rank
0,26
Scimago Quartile Score Civil and Structural Engineering (Q3)
Materials Science (miscellaneous) (Q3)
Computer Science Applications (Q4)
Modeling and Simulation (Q4)
Software (Q4)
Scopus  
Scopus
Cite Score
1,5
Scopus
CIte Score Rank
Civil and Structural Engineering 232/326 (Q3)
Computer Science Applications 536/747 (Q3)
General Materials Science 329/455 (Q3)
Modeling and Simulation 228/303 (Q4)
Software 326/398 (Q4)
Scopus
SNIP
0,613

2020  
Scimago
H-index
11
Scimago
Journal Rank
0,257
Scimago
Quartile Score
Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score
340/243=1,4
Scopus
Cite Score Rank
Civil and Structural Engineering 219/318 (Q3)
Computer Science Applications 487/693 (Q3)
General Materials Science 316/455 (Q3)
Modeling and Simulation 217/290 (Q4)
Software 307/389 (Q4)
Scopus
SNIP
1,09
Scopus
Cites
321
Scopus
Documents
67
Days from submission to acceptance 136
Days from acceptance to publication 239
Acceptance
Rate
48%

 

2019  
Scimago
H-index
10
Scimago
Journal Rank
0,262
Scimago
Quartile Score
Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score
269/220=1,2
Scopus
Cite Score Rank
Civil and Structural Engineering 206/310 (Q3)
Computer Science Applications 445/636 (Q3)
General Materials Science 295/460 (Q3)
Modeling and Simulation 212/274 (Q4)
Software 304/373 (Q4)
Scopus
SNIP
0,933
Scopus
Cites
290
Scopus
Documents
68
Acceptance
Rate
67%

 

Pollack Periodica
Publication Model Hybrid
Submission Fee none
Article Processing Charge 900 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
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Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription fee 2023 Online subsscription: 336 EUR / 411 USD
Print + online subscription: 405 EUR / 492 USD
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Pollack Periodica
Language English
Size A4
Year of
Foundation
2006
Volumes
per Year
1
Issues
per Year
3
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 1788-1994 (Print)
ISSN 1788-3911 (Online)

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Dec 2023 0 204 9
Jan 2024 0 200 28
Feb 2024 0 402 13
Mar 2024 0 150 35
Apr 2024 0 78 29
May 2024 0 37 11
Jun 2024 0 0 0