=Paper=
{{Paper
|id=Vol-3295/paper10
|storemode=property
|title=Managing Projects Portfolio in Complex Environments Based On Fuzzy Situational Networks
|pdfUrl=https://ceur-ws.org/Vol-3295/paper10.pdf
|volume=Vol-3295
|authors=Jahid Babayev,Mladen Vukomanovic,Sergiy Bushuyev,Igor Achkasov
|dblpUrl=https://dblp.org/rec/conf/itpm/BabayevVBA22
}}
==Managing Projects Portfolio in Complex Environments Based On Fuzzy Situational Networks==
https://ceur-ws.org/Vol-3295/paper10.pdf
Managing Projects Portfolio in Complex Environments Based
On Fuzzy Situational Networks
Jahid Babayeva, Mladen Vukomanovicb, Sergiy Bushuyevc and Igor Achkasovc
a
Azerbaijan project management association, Baku, Azerbaijan
b
University of Zagreb, Faculty of Civil Engineering, Zagreb, Croatia
c
Kyiv National University of Construction and Architecture, Povitroflotsky Avenue, 31, 03680 Kyiv, Ukraine
Abstract
This article is dedicated to the main approaches used in the modern methodologies of project
management and their logical connection as the base for forming fuzzy technology and
information system. The purpose of this article is to create an effective tool for the project
managers to be able to assess the success of the project in the first stages, including the time
when the main planning document is being composed. The task of the project coordinator is
to find out the interested party's purposes and find a possible compromise. Considering
people and organizations involved in the project, or those whose interests can influence the
results of the project's execution or successful completion positively or negatively as an
interested parties, the project team should determine them, find out their needs and
expectations, and then manage them, influencing them to provide the guarantee of the
project's successful completion. Considering people and organizations involved in the
project, or those whose interests can influence the results of the project's execution or
successful completion positively or negatively as interested parties, the project team should
determine them, find out their needs and expectations, and then manage them, influencing
them to provide the guarantee of the project’s successful completion. Having the goal to
solve the multi-criteria linguistically described problem of project success evaluation, this
work offers an approach, based on the fuzzy situational rules for multi-criteria selection.
Therefore, this work analyzes the setting and discusses the possible ways of solutions and
suggests the approach to the project success evaluation, based on a multi-criteria selection
problem in the conditions of linguistic description and using the integrated approach of fuzzy
situational management with production rules, widely used in the expert systems.
Keywords 1
Project management, project life, information system, innovative technologies, linguistic
description, fuzzy networks
1. Introduction
The traditional method of initiating projects and programs, as well as the formation of a portfolio
of projects in certain areas and their management, is one of the priority issues in the development of
this region. Practice shows that the environment (Enterprise Environmental Factors) greatly affects
the entire life cycle of projects and their successful completion. Let's take a closer look at the essence
of this question, why the environment has a large weight factor in the successful completion of
projects [1].
The vast majority of man-made technologies are based on the imitation and copying of various
natural processes and phenomena. Innovative technologies are no exception, they try to model the
Proceedings of the 3nd International Workshop IT Project Management (ITPM 2022), August 26, 2022, Kyiv, Ukraine
EMAIL: babayevjahid@gmail.com (Jahid Babayev); mladen@ipma.world (Mladen Vukomanović); Sbushuyev@ukr.net (Sergiy
Bushuyev); achckasov.i@ukr.net (Igor Achkasov)
ORCID: 0000-0003-4633-8261 (Jahid Babayev); 0000-0002-3037-1908 (Mladen Vukomanović); 0000-0002-7815-8129 (Sergiy
Bushuyev); 0000-0002-7049-0530 (Igor Achkasov)
2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org) Proceedings
creative behaviour of the individual and are based on the deep historical traditions of different
cultures in uncertain conditions. For simulation uncertainty used entropy and fuzzy models [2].
Previously, the main object of various innovative technologies was an individual or a group, the task
was to educate, educate, and organize new behaviour in adverse, deadly and aggressive external
conditions [3]. The traditions of these schools cover various aspects of activity: philosophy,
preaching, commerce, intelligence, diplomacy, and politics. Now, in connection with the rapid
development of information technology, a new association has emerged, consisting of a deeper use of
computer systems and networks in innovation: artificial intelligence systems, and expert systems [4].
The trend of such penetration is growing and expanding significantly, so there is a need for a new
organization of innovation activities with broad involvement of information technology [5].
The creative activity of man, who transforms nature, being a consequence, hinders and hinders the
creative activity of the cause, that is, nature, which seeks to improve man [6]. A hypothetical way to
solve this problem is to find out the fundamental difference between the level of innovative
technology used by nature and which man has been able to master so far [7]. The cognitive process,
developing and improving itself, aimed at a simple expansion of needs, may need to be adjusted
concerning the unknown motives of nature's behaviour [8]. The emergence of information systems
promises to provide a means of expanding the innovative resources of society, which can indicate the
path to such innovative technologies that do not conflict but are in harmony with nature. These
conclusions, of course, should be considered at the level of hypotheses. For the development of
society, the time has already come when it is necessary to flexibly adapt their innovative technologies
to natural ones to prevent and avoid global troubles [9].
In project management, there is such a thing as a project environment [10]. What is a project
environment? The project environment is a set of external and internal factors that affect the
achievement of project results [11]. Anything can be a factor in the external environment of the
project - from the political situation in the country to the procurement process adopted by the
company. Management of the external environment of the project is most often associated with very
great difficulties or even impossible, for that it is external [12]. Of course, you can influence
something, but this is more an exception than a rule, and these are factors that affect the project, and
which are beyond the competence of the project manager [13].
Therefore, when forming and initiating vital projects in such environments, on the one hand, it is
of great importance, and on the other hand, it is very difficult to implement them, due to the
complexity of the project environment [14]. We observe similar facts, especially in unstable political
and social regions. I would especially like to note that the implementation of any type of project in an
environment with difficult circumstances surrounded by projects, ranging from humanitarian, and
social to technical projects, is of great importance for this region. The results of the implementation of
such projects are fundamentally reflected in the effectiveness of the regulatory processes in the region
[15]. Therefore, when forming projects, a careful study of the environment with external and internal
factors is required, and following the results of these studies, such projects are initiated that
significantly affect the development of this region and the correct formation of project portfolios
guarantees the successful completion of projects [16]. The modern stage of development of methods
and means of project management in the world is characterized by the general formula "from trust to
understanding and active using" [17]. At the same time, with the development of modern information
systems and technologies, the results of research in the field of "soft components of project
management" (leadership in projects and building effective management teams) are defined as the
main areas of research [18]:
the creation of effective organizational structures based on competence centres, and offices
for managing projects, programs and project portfolios;
effective partnership through joint training in international programs (benchmarking);
integration of modern information technologies at the enterprise level;
globalization and effective exchange of knowledge;
assessment of the potential of project management at various levels of presentation
(project manager, team, organization, industry, country) and management of this potential
based on a system of models and tools.
2. Analysis of recent research and publication
Studying various scientific literature, we can conclude that today the mechanisms for the
formation of project portfolios concerning the environment of global uncertainty are not sufficiently
described [19, 20]. At the moment, project management in complex environments is studied within
the framework of the theory of active systems, strategic planning, models of proactive development,
etc. A special place is occupied by the theory of project management based on values, which are
understood as utility and benefit [21]. Thus, the usefulness of projects as a whole is estimated by the
degree of their attractiveness to all participants, although individual components of the usefulness of
the project result for the environment may have different significance [22]. Considering all the
different elements in one package that complement each other creates a complementary relationship
between these elements in the process of forming and managing projects in these situations [23, 24].
Based on the foregoing, the author proposes a special tool for the formation of new projects in
special regions with a difficult situations in the environment, using linguistic descriptions of the
process for the successful completion of projects [25].
3. The multi-criteria task of managing the projects portfolio in a clear
statement
A multi-criteria task for well-defined systems is presented as:
f ( x) ( f1 ( x),..., f n ( x)) max (1)
xD
where D - is the allowable area of possible changes in the solution X.
In the case when the set D has large power, then this task is classified as a task of vector (multi-
criteria) optimization, but if the number of alternatives in D is small, then the task is called the task of
multi-criteria decision making. Assuming that the solution X is determined by k parameters (X 1, ...,
Xk) effective (Pareto - optimal, non-dominated), such a solution is called, if there is no other, for
which the value of at least one local criterion is better than X*
It is known that the solution of the multi-criteria task is the Pareto set P, which consists of all
possible effective solutions X*, i.e. the whole area of compromises.
Usually, the project portfolio manager in the conflict zone, acting as a decision-maker (DM), is
interested in one or more solutions from P, and therefore the choice is made in the dialogue procedure
of the decision-maker - Competence Center - Center of Energy through decision generation - elements
of Pareto sets. This process is interpreted by the introduction of global criteria F (super criteria),
explicitly or implicitly known manifestations of the project. At the same time, methods for identifying
and describing Pareto sets with an adaptive or non-adaptive form of the dialogue organization, are
aimed at narrowing the Pareto sets.
Note that the Pareto set is closely related to the idea of forming a convolution of local criteria of
the form, where usually they are the result of examinations. In doing so, one should keep in mind the
important result of J. Germeier, according to which one can find a scalar function F(c,f) such that
~ ~
a) X P c( X ) Sc : Xˆ (c) arg max F (c( X ), f ( X )) X
~ (2)
b) c Sc X (c) P, г де Sc c / c j 0, ( j 1, k ), C j 1
j
The linear convolution of the criteria also satisfies the above requirements (in case X is a convex
set, fi(X) is a concave function).
Based on the foregoing, the search methods are based on the selection of the appropriate
convolution and coefficients C during sequential movement to a new state, which is also an element
of the Pareto set. The reaction of the program manager for development (DM) consists either in
comparing a pair of decisions X with an indication of preferences or in comparing fi with an
indication of its possible change (increase, decrease).
Along with this, mixed cases are also possible, when the improvement occurs both in the space of
decisions and in the space of criteria.
A special place is occupied by a multi-criteria choice based on the preferences of the development
program manager (DM). In the clear case, preferences are given as a pair (X, R), where Rj={R1, …
,Rn} – is a vector preference relation, each component of which R j ( j 1, n). In this case, the
local criteria fi can be represented by the corresponding preference relations
R j ( x, y) / f j ( X ) f j (Y ) (3)
4. Formalized task statement in the case of a linguistic description
Below we will show the specific features of a multi-criteria choice under the conditions of a
linguistic description of the task.
In this model, it is assumed that the vector of input variables, which are targets and described
linguistically, consists of four components X i (i 1,4) , including;
X1 - interests of the customer and donor;
X2 - interests of the local population and authorities;
X3 - the creation of a special product for a certain region;
X4 - other targets, such as state bodies or private companies.
The linguistic criteria (local) f j ( j 1,3) of the parties are dependent on the specified linguistic
variables X i (i 1,4) and reflect the assessment of the success of the project for the three categories
presented. It is required to find a compromise solution, such that:
F ( X i ) f j ( X i ) max ( j 1,3), (i 1,4), where max f j is understood in the Pareto
xD
sense. Let's take the production form of the rules to describe the local quality criteria, presented in
linguistic form, and get:
IF X 1( i ) and X 2(i ) and X 3(i ) and X 4(i ) , THEN f1(i ) and f 2(i ) and f 3(i ) , (as well as) (i 1, N ) , (4)
here i 1, N - is the number of production rules expressed by fuzzy implications. Assuming that
there is no interaction between the outputs, i.e. fi ( j 1, n) are independent of each other, we rewrite
the reduced system of productions for each of the outputs fi separately and obtain:
IF X 1( i ) and X 2(i ) and X 3(i ) and X 4(i ) , THEN f j (i ) , (as well as) (i 1, N ), ( j 1,3) . (5)
As a concrete example, here is some conditional rule from the rule base.
If X1="very low", X2="small", X3="high" and X4="small", then F1="satisfactory",
F2="unsatisfactory" and F3="unsatisfactory". Here the values X 1 – X4 are considered on the term-set
{”very small”, “small”, “medium”, “high”, “very high”}, and the values F 1 – F3 express satisfaction
on the term-set {”unsatisfactory” “satisfactory” and “good”}.
Structural and production description is presented in Figure 1.
X1
X2 fj
X3 O Rj
X4
Figure 1: Structural and production description
Here Rj is a fuzzy relation «input-output», i.e.
R j X1 X 2 X 3 X 4 f j ( j 1,3)
(6)
there is a fuzzy subset on a cartesian product of input and output quantities. A fuzzy relation can
be formed in various known ways, from which we choose a fuzzy implication
R j ( j 1,3) representation using Gödel's logic and get:
X1 X 2 X 3 f ; R Rg X g f (7)
g
with membership function:
1, IF x ( x1 ,..., x4 ) f ( f )
Rg ( x1 , x2 , x3 , x4 , f ) (8)
f ( f ), IF x ( x1 ,..., x4 ) f ( f )
сonditional universe for X X 1 X 4
universe for f ; X 1 ,..., X 4 , f сorresponding fuzzysets x и f
The compositional inference rule will be presented as:
f tek ( f ) ( X1tek ... X 4tek ) Rg ( X1tek ... X 4tek ) (( X1 ... X 4 ) f ) (9)
g
or in terms of the belonging function
f ( f ) ( X ( x) ... X ( x) Rg ( x1,..., x4 , f )) ,
tek tek tek (10)
1 4
The given system of production rules forms the so-called registering knowledge base for expert
systems (ES). The logical processor, based on the logical inference, using the compositional rule,
generates the possible (predictive) values of the fuzzy values of the linguistic criteria.
The main task in constructing an ES of multi-criteria choice is associated with an automated search
for compromise solutions for a set of local criteria fj specified in a linguistic form. The knowledge
base containing the information necessary for compromise decisions will be considered the control
one.
Thus, in the conditions of multiple goals, the local authorities of the crisis region face a multi-
criteria task, the features of which are a qualitative description of the goals, the subjectivity of the
choice and the degree of confidence in the assessments when developing a compromise solution on
the part of donors. If we assume that in developing a compromise solution for the head of the region,
which will support him with information, an expert system will be implied, then the need for donors
to communicate with it in a language close to nature should be taken into account.
As for the proposed expert system, the latter has two knowledge bases: registering and managing,
which was mentioned above. There are several requirements for the control base:
First, the choice set of alternatives must be small.
Secondly, such a knowledge base should be universal for the application of various methods of
multi-criteria selection.
Thirdly, the knowledge base accumulated in the process of finding a compromise must be
adaptive, i.e. provide for correction, as well as take into account the interactive exchange of
information.
Fourth, the processor interaction system must support the features of the description of the Pareto-
optimal approach in the fuzzy case.
Fifthly, since the clarification of the compromise solution is due to the dialogue of the local
authorities (DM) with the centre of competence, it is necessary to provide for the originality of this
exchange, when both preliminary engineering of expert information is required for the formation of
knowledge and clarification of the inference rules, as well as its clarification, correction based on the
results of adaptive exchange, the use of criteria convolution, and, in particular, with a fuzzy
preference relation, starting with the simplest representations of the convolution coefficients Сj
(j=1,2,3) for cases where C and X are linguistic variables.
Comparative analysis shows that the simplest cases of multi-criteria choice in terms of linguistic
description are those when the leadership of the region sets criterion estimates as the degree of
compliance of alternatives with the concepts defined by the criteria fj and, thus, each alternative can
be described as a set of fuzzy values of linguistic criteria, and the choice is feasible from the
maximum correspondence condition.
It is also possible to use such a choice of alternatives, which is based on the ranking, i.e. revealing
the significance of criteria fj and reducing them to additive convolution. These methods are simple,
but require prior knowledge and the introduction of estimates of alternatives and "weights" of criteria
into the knowledge base; in the logical processor that performs the inference, it is necessary to
identify all alternatives, i. combinations of input fuzzy values X i (i 1,4) and corresponding fuzzy
values of linguistic criteria f j ( j 1,3) . However, as alternatives grow, a significant amount of
memory is required. For example, if the leadership of the region specifies a linguistic description of
3
the importance of local criteria, then in the convolution F C f , using the operation of
j 1
j j
multiplying two fuzzy numbers, you can restore the fuzzy values of F.
Another method is not associated with a comparison of alternatives (controlled linguistic
variables), but with a direct comparison of fuzzy values of local criteria.
In this case, the region's leadership is also required to have explicit or implicit knowledge of the
super-criterion F, previously entered into the knowledge base.
In this case, we most often use an approach based on the representation of a global criterion in the
form of an intersection of fuzzy values of local criteria, i.e. as
F f1 f 2 f 3 or in terms of membership F ( f1 , f 2 , f 3 ) f j (11)
j
IF the restoration of the super criterion is carried out using production rules such as:
IF f 1( i ) and f 2(i ) and f 3(i ) , THEN Fi , (as well as ) (i 1, N ) , (12)
THEN the choice is made based on fuzzy inference rules (composition rule).
In this case, it will be necessary to create a managing knowledge base, i.e. F in its meaning will
reflect the idea of the region's management about the satisfaction or fuzzy usefulness, which will be
defined as a fuzzy subset of the unit interval, and the choice will be made based on a comparison of
point estimates.
Super criterion F can also be restored based on knowledge previously obtained by the leadership
of the region as a result of the dialogue. For this purpose, a production system seems:
" IF f 1( i ) and f 2(i ) and f 3(i ) , THEN f1(i ) и f 2(i ) и f 3(i ) (as well as)" (i 1, N ) , (13)
where f j is the deviation of the jth local criterion, i.e. it is assumed that the leadership of
the region "owns" the desired characteristics f1, f2, f3 and its response in the form of answers is
presented in the form of the desired change f . We add that, by analogy with fuzzy controllers, to
improve the search, we can recommend, along with setting the deviation f , to a large extent also the
rate of this change f .
Finally, we should emphasize the special place occupied by a multi-criteria choice based on a
fuzzy preference relation, in which the source of information is the environment, that is, the region
and local authorities (DM), which compare their preferences.
5. Implementation of the multi-criteria task of managing the projects
portfolio
For linguistic multi-criteria problems, one can foresee the product system, connecting typical
situations with solutions. Such a decision matrix as "situation-action" is a foundation of the managing
base. Let's consider these products:
f1
" If f 2 , then (U j ,V j , Y j )" where : U j is “increase” managing solution; Vj is “reduce”; (14)
f
3
Yj is “not change” accordingly for j criterion of fj. Managing solutions, being linguistic variables, are
agreed with term pluralities of condition criteria { “little“, “middle“, “big”} and have fuzzy values
such as {“a little”, “much”} for U j and V j and { “not change”} for Yj. As an example, we will
consider the managing matrix for U j - (increase) = «a little».
Table 1
Example of fuzzy relation
Little Middle Big
Rj Little 0,2 1 0,4
Middle 0 0,3 1
Big 0 0 1
As demonstrated in the matrix, it sets fuzzy relation on term plurality of fj criterion, describing the
effect of managing solutions from the term plurality of the linguistic variables (here set as U j ).
Preliminary task of fuzzy relation (expert method), unlike traditional production fuzzy "input-output"
description, allows getting the resulting value of fj criterion by composing its initial fuzzy value fj and
fuzzy relation R, i.e. f j
result
f jinitinal R j .
For example, if f j 0.8 /" little " , 0.4 /" middle" , 0.2 /" big " , applying relation «increase»
with the above-mentioned value «a little», one will have the following:
0.2 /" little " , 0.4 /" middle" , 0.4 /" big " .
result
fj
Therefore, by setting the limited number of sample fuzzy situations, for each of which fuzzy
values of local criteria are set, and connections between situation and managing action as a product
(set of preliminarily prepared decisive matrices) we define a fuzzy situational network (UST).
Comparing each current situation with a sample, by operations of fuzzy including and fuzzy equality,
we select the most appropriate sample and applying managing effects by the maximin compositions,
we estimate new values of the local criteria. Therefore, in production rules «situation-action» and
decisive matrices included all preliminarily gathered information for managing the knowledge base.
In return, production rules, such as «If X, then f», centred in the registering knowledge base, allow
to logically estimate the current fuzzy values of f local criteria vector, based upon the fuzzy current
description of X parameters vector.
In cases when the obvious description of the products, providing the output of managing solutions,
is absent, the managing effect is based on the analysis of possible transfer between the current
situation and the goal. Such models are called «situation – managerial strategy – action». In this case,
the goal situation (needed condition) can be set as a product «situation – situation» as a result of
preliminary dialogue with the project coordinator (decision maker), for example:
" If f 1( i ) and f 2(i ) and f3(i ) , then f1(i ) and f 2(i ) and f3(i ) (as well as)" (i 1, N ) . (15)
On this base, we can input the information “If condition f 1* and … and f3* then condition f1** and
… and f3*”, to the managing knowledge base, i.e. estimate the space for the possible transfers as a
product. It is necessary to notice that such production models are based on the structure of the
dialogue with the project coordinator (decision maker).
Along with that, one can determine the goal situation based on the analysis of the selection
preference degree of the managing effects on the preliminarily constructed UST (Figure.2).
Figure 2: The scheme of fuzzy situational network
The fragment of the above-depicted UST shows that each top of UST is a fuzzy sample (typical)
situation, while each arch of it is weighted with managing solution, necessary for transfer from the
condition to the condition and degree of preferences of these solutions. I.e. S Si is a plurality of
sample situations, R j (U j ,V j , Y j ) is a plurality of managing solutions; ( Si , R j ) is the degree of
preference; and is not changeable for each S, and revealed by the expert way. It is not revealed
as a result of the expert survey, one can construct the product such as «situation – solution
preference», for example, «If f1=f1* and ... and f3=f3*, then
1 1 for R1 and 2 2 for R2 and 3 3 for R3 . Preference degrees can be either fuzzy
* * *
numbers from [0,1] or ordinary numbers from the same interval.
Therefore, in the model «situation – managerial strategy – actions» two stages are significant:
the setting of the goal situation;
review of the product – strategy.
To construct the fuzzy situational network for multi-criteria linguistic tasks, it is necessary, first, to
employ an expert survey to reveal a plurality of R1,…,Rn managing solutions, which are set as
relations between local criteria values. Secondly, for each Si S situation to form Г S i , i.е. Sis S ,
in which one can transfer by managerial effect, connect Si arches with the tops, where one can transfer
and load the arches with solutions and preferences degree. A reverse way is also available, first, input
some relations on the plurality of sample situations, the graph of which reflects possible transfers
from one situation to another. Secondly, determine the values of managing effects R and preferences
degrees of their use, necessary for the transfers. So, in the straightway of the construction of the fuzzy
situational network, each new situation should be estimated according to the local criteria values,
while for the reverse order the ways should be estimated as to means of managing effects. To
illustrate the transfers on the network we will consider the example, where the situation is determined
by fuzzy values of two criteria f1 and f2. Let Пусть S1 have f11, and f21 and S2 have f12 and f22. Let’s
imagine the transfer from S1 to S2 ( S1 S2 ) a f11, f 21 S1 S2 f12 , f 22 nd mark managing solutions
f1 and f2 accordingly through R1 and R2, in general, the managing solution for S1 S2 will be marked
as R. Stage-by-stage transfer f11 , f 21
R1
f12 , f 21
R2
f12 , f 22 is equivalent to the “max-
min” composition R1 R2 = R, i.e. f11, f 21 R f12 , f 22 is provided utilizing compositional rule
for integral relation R. As for the selection of the preference degree of the managing solution, it is
estimated as a conjunction of the local criteria degree components min( R f 1 , R f 2 , R f 3 ) .
It is important that set the goal situation where the simplest solution is stored in the "situation-
goal" products base в базе. This way is complicated and requires significant computer memory, and
that is why the more preferable approach is that which uses the information about preferences degree
for the solutions, available in fuzzy situational networks.
6. Conclusion
Therefore, this work analyzes the setting and discusses the possible ways of solutions and suggests
the approach to the project success evaluation, based on a multi-criteria selection problem in the
conditions of linguistic description and using the integrated approach of fuzzy situational management
with production rules, widely used in the expert systems. The formation and initiation of vital projects
in such environments, on the one hand, is of great importance, and on the other hand, it is very
difficult to implement them, due to the complexity of the project environment. We observe similar
facts, especially in unstable political and social regions. I would especially like to note that the
implementation of any type of project in an environment with difficult circumstances surrounded by
projects, ranging from humanitarian, and social to technical projects, is of great importance for this
region. The results of the implementation of such projects are fundamentally reflected in the
effectiveness of the regulatory processes in the region. Therefore, when forming projects, a careful
study of the environment with external and internal factors is required, and following the results of
these studies, such projects are initiated that significantly affect the development of this region and the
correct formation of project portfolios guarantees the successful completion of projects.
7. Acknowledgements
The authors express their deep gratitude to the German Academic Exchange Service (DAAD),
financed from funds by the Federal Foreign Office, for its financial support of the “Virtual Master
Cooperation Data Science” (Project-ID: 57513461) and the European Union ERASMUS + program
for financial and technical support of the project «Cross-domain competences for healthy and safe
work in the 21st century (Work4Ce)» (№ 619034-EPP-1-2020-1-UA-EPPKA2-CBHE-JP).
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