=Paper=
{{Paper
|id=Vol-2762/paper9
|storemode=property
|title=Fuzzy Logic System for IT Project Management
|pdfUrl=https://ceur-ws.org/Vol-2762/paper9.pdf
|volume=Vol-2762
|authors=Nadiia Vasylkiv,Lesia Dubchak,Anatoliy Sachenko,Taras Lendyuk,Oleg Sachenko
|dblpUrl=https://dblp.org/rec/conf/ictes/VasylkivDSLS20
}}
==Fuzzy Logic System for IT Project Management==
https://ceur-ws.org/Vol-2762/paper9.pdf
Fuzzy Logic System for IT Project Management
Nadiia Vasylkiva , Lesia Dubchaka , Anatoliy Sachenkob,c , Taras Lendyuka and
Oleg Sachenkoa
a
West Ukrainian National University, 11, Lvivska str., Ternopil, 46001, Ukraine
b
Kazimierz Pulaski University of Technology and Humanities in Radom, Department of Informatics, Jacek
Malczewski Street 29, Radom, 26 600, Poland
c
Research Institute for Intelligent Computer Systems, West Ukrainian National University, 11, Lvivska str., Ternopil,
46001, Ukraine
Abstract
Decision-making processes in IT project management occur, as a rule, in conditions of risk and uncer-
tainty. Therefore, it is advisable to use methods based on fuzzy logic. The authors propose to analyze
the influence of both external and internal factors on the time of the project, financial, human resources,
on the timeliness of project completion. The developed fuzzy IT project management system consists of
subsystems that can be considered as separate self-sufficient parts. The proposed method enabling the
project managers to assess the impact of project environment factors on human and financial resources,
the duration of individual works and the project as a whole, and create the basis for proactive changes
in project activities.
Keywords
IT Project, Fuzzy System, Project Management, Matlab
1. Introduction
The IT project environment is important in its completion success. The implementation of
different projects is influenced by factors such as the external environment (political, economic,
social, legal, scientific, technical, cultural and natural) and its internal environment (organiza-
tional structure of the project management, project team, professionalism of project executors,
style work of the project manager, the specifics of the organization of project work, etc.). The
implementation of an IT project mostly depends on how timely the influencing factors of its
environment identified.
In the process of implementation, the IT project affects by many external and internal
factors that cause changes in its initial or current resource parameters. Due to unpredictable
manifestations of external and internal factors, the actual cost of the IT project may deviate
from the preliminary cost estimate, the human resources involved in the project may change,
and the actual duration of the IT project process may differ from the planned one.
ICT&ES-2020: Information-Communication Technologies & Embedded Systems, November 12, 2020, Mykolaiv, Ukraine
" nvs@wunu.edu.ua (N. Vasylkiv); dlo@wunu.edu.ua (L. Dubchak); as@wunu.edu.ua (A. Sachenko);
tl@wunu.edu.ua (T. Lendyuk); olsachenko231@gmail.com (O. Sachenko)
0000-0002-4247-7523 (N. Vasylkiv); 0000-0003-3743-2432 (L. Dubchak); 0000-0002-0907-3682 (A. Sachenko);
0000-0001-9484-8333 (T. Lendyuk); 0000-0001-9337-8341 (O. Sachenko)
© 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
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
Such changes can later play a crucial role in the success of the IT project, so it is necessary
to take into account the external and internal factors of the project and assess their impact on
individual parameters of the project in the implementation process.
Since each successful IT project will be implemented in a future, we may assume there, the
both uncertainty and risk occur. It is advisable therefore to employ the methods based on fuzzy
logic [1, 2, 3, 4, 5, 6, 7] for IT project management.
2. Related Work
Currently, many scientific studies are conducted using fuzzy set theory, aimed at identifying
risk situations in the implementation of projects and decision-making to eliminate or reduce
their impact [8].
The ability to evaluate risk contingency value by aggregating rules combining company
risk index and project risk index using fuzzy logic approach and MATLAB software has been
described in Reference [9].
The fuzzy logic system developed in Reference [10] identifies the financial project risk due
to changes occur in project values, in the duration of the projects and in the implementation
durations.
In Reference [11] the fuzzy model of risk assessment of IT-project unsuccessful completion is
proposed.
In Reference [12] a fuzzy model for the risk evaluation of projects is designed. This model is
based on the Elena guideline and comprises a three-stage procedure, including vulnerability
assessment, consequence assessment, and the overall risk evaluation. All stages employ the
fuzzy reasoning to cope with the inherent uncertainty imposed by projects.
The Reference [13] describes a step-by-step approach for accurate estimation of time and
cost of projects using the Project Evaluation and Review Technique as well as expert views of
fuzzy numbers.
The use of fuzzy logic makes possible the incorporation of knowledge-based assessments into
the estimate of the statistical parameters, which permits overcoming the limited availability of
information that makes probability-based modeling techniques unsuitable for this purpose [14].
The Reference [15] proposes a model based on fuzzy logic, the inputs of which are financial
value, strategic value, level of risk, and output - the level of project priorities.
The fuzzy inference system for evaluation of project success is presented in Reference [16].
The reliable proposed expert decision-making fuzzy model consists of three input variables
(project status, project risk, project quality), one rule block (with 125 fuzzy rules) and one output
variable (project success).
In Reference [17] a fuzzy model of the management system for the alignment of the value-
oriented portfolio of projects has developed. There are four input variables (structure, process,
technology and team), and the output variable determines the value of the portfolio. A Mamdani
fuzzy inference system is employed to solve the fuzzy modeling problem.
The Reference [18] presents an expert fuzzy model for evaluation of the project success rate,
including partial sub-models, and Reference [19] describes the integrated fuzzy DEMATEL-fuzzy
ANP model for evaluating the construction projects by considering the risk factors.
The impact of the external environment of the project on its implementation is considering
partly in the fuzzy model [20], which has the input values of the environmental factor, financial
and human resources.
The conducted analysis of existing References above confirmed that fuzzy modeling does not
take into account the impact of the internal environment of the project on its implementation.
Therefore, authors have run a deeper research below to monitor the impact of factors of both
external and internal environment on the time of the project, financial, human resources, and,
consequently, on the timeliness of project completion.
3. Architecture of IT Project Management using Fuzzy Logic
The project environment has an impact on its implementation and successful achievement of
project objectives. The external environment can be favorable for the IT project, i.e. provide
opportunities for timely and successful achievement of the goal. On the other hand, some
environmental factors may be the cause of risk situations in the project implementation, i.e.
pose a threat of non-implementation or late implementation.
Such positive or negative manifestations of environmental factors of the IT project in some
way affect the financial and human resources, as well as the implementation time of the project
certain stages. For example, the economic conditions of the external environment of the project
are related to the project budget. Therefore, they have a direct impact on its financial resources
and retention during the project work of professional contractors (human resources), which in
turn affects compliance with project timelines. Other unforeseen environmental factors, such
as natural ones, which may be accompanied by, for example, energy problems, can increase the
execution time of certain works of the IT project and, as a result, cause its untimely execution.
Under the influence of the external environment, the need for the product of the IT project or the
form of its implementation may change, and this, in turn, will require significant changes in time,
material, human and cost resources or even abandonment of further project implementation.
Risk situations associated with adverse effects, such as economic or political factors, can
significantly mitigate investments that has been made in sufficient quantities in advance of the
project and are therefore a guarantee of its financial stability.
The successful implementation of an IT project is significantly influenced by the internal
environment, in particular, the psychological climate and atmosphere in the IT project team,
organizational structure, interests, professionalism and degree of involvement in the IT project
of its participants, methods and means of communication between them. That is, the internal
sources of changes in the project are formed among the project participants in the process of
their relationship during the project and are the result of strong or weak manifestations of the
internal environment of the project.
These factors have a great influence on the preservation of the contractor number (human
resources) and the execution time of individual works of the project. If the strengths of the
internal environment of the IT project are manifested. It means that the project team is formed
of highly qualified employees, communication tools are established and the leader maintains
a normal psychological state in the team, thanks to the right leadership style. Conversely,
if the project team is unqualified, the leadership style is chosen incorrectly, the means of
communication work poorly or do not exist at all, i.e. the weaknesses of the internal environment
prevail, the project will go beyond the time.
Thus, the external environment of the project is manifested in terms of opportunities or
threats, the internal environment can have strengths and weaknesses. Together with the
investments made in the project, these manifestations of the external and internal environment
affect such parameters as financial resources, human resources, time of execution of individual
stages or works of the project and, as a consequence, the completion of the project (Figure 1).
Figure 1: The impact of the project environment on its completion
As can be seen from Figure 1, the impact of project environment factors and the amount of
investment can be traced not only on the completion of the project, but also on its individual
parameters. Therefore, it is proposed to consider three subsystems "Financial Resources",
"Human Resources", "Project Execution Time" as part of the overall environmental impact
assessment system, the outputs of which can help the manager to properly assess or predict at
any time during the project. The value of a certain parameter, and all the outputs together - to
predict the situation regarding the timely completion of the project.
Since the appearance of any factor influencing the implementation of the project is uncertain,
it is advisable to create such a system and subsystems in its composition using the methods
of fuzzy logic. Dependence of output values on inputs is given by a fuzzy knowledge base on
Mamdani algorithm, which is minimum-maximum composition [21], [22]. The general scheme
of this fuzzy system is given in Figure 2.
Figure 2: The structure of the fuzzy system "Project Completion"
3.1. Subsystem "Financial Resources"
The subsystem "Financial Resources", taking into account the impact of the external environment
and the inward investment of the project, based on fuzzy logic assesses the financial condition
of the project (Figure 3).
Figure 3: Subsystem "Financial Resources"
The input variable of this subsystem is the external environment, the fuzzy states of which
are opportunities or threats.
The states of the input variable "Investments" are large, small, medium.
The output variable of this subsystem has three states: large, small, medium.
All input fuzzy variables have another additional state “none”, which describes the case when
the fuzzy system did not receive the current value of a certain variable, for example, in case of
system failure. Thus, the input variable "Investments" has 4 states for rule base designing, and
the variable "external environment" - 3.
The case when there are no values of all input variables is excluded, because in this case the
fuzzy system doesn’t work.
Since the input variables have three and four states, respectively, the rule base of this subsys-
tem consists of 3 ⋅ 4 − 1 = 11 rules of the "if then" type.
3.2. Subsystem "Human Resources"
The subsystem "Human Resources", taking into account the current impact of the external
environment and the state of the internal environment of the project, evaluates the amount of
available human resources for the project.
Figure 4: Subsystem "Human Resources"
The distributions of fuzzy sets of input and output variables of this subsystem are as follows:
• external environment: opportunities, threats;
• internal environment: strengths, weaknesses;
Table 1
Correlation of the input and output variables of the fuzzy subsystem "Human Resources"
External environment Internal environment Human Resources
Threats Weaknesses Small
Threats Strengths Middle
Threats None Middle
None Weaknesses Small
None Strengths Large
Opportunities Weaknesses Middle
Opportunities Strengths Large
Opportunities None Middle
• human resources: large, small, medium.
As was note above, all input fuzzy variables have another additional state “none”, which
describes the case when the fuzzy system did not receive the current value of a certain variable.
The case when there are no values of all input variables is excluded, because in this case the
fuzzy system doesn’t work.
The total number of rules of this fuzzy subsystem is 3 ⋅ 3 − 1 = 8. The base of rules is given in
table 1. Table 1 provides an example of some of the input and output variables that are based on
the fuzzy model rule base.
3.3. Subsystem "Project execution time"
The subsystem for analyzing the project execution time as input variables has external and
internal environment and investments (Figure 5).
Figure 5: Subsystem "Project execution time"
The distributions of fuzzy sets of input and output variables of this subsystem are as follows:
• external environment: opportunities, threats;
• internal environment: strengths, weaknesses;
• investments: large, small, medium;
• project execution time: overdue, short, scheduled.
The rule base of this subsystem consists of 3 ⋅ 3 ⋅ 4 − 1 = 35 rules.
4. Fuzzy System "Project Completion"
The fuzzy system for assessing the timeliness of project completion is based on the Mamdani
fuzzy inference mechanism. The outputs of the subsystems, described above ("Project execution
time", "Human resources", "Financial resources"), are input variables of the main system. The
initial variable "Project Completion" can be in one of the following states: timely (in time), early
(in advance), untimely (late).
The total number of rules of the developed fuzzy system, taking into account the “none”
states of each of the input variables, is equal to 4 ⋅ 4 ⋅ 4 − 1 = 63 rules.
The implementation of the proposed system was carried out in the Matlab environment using
Fuzzy Logic Toolbox.
The bell-shaped form, which best reflects fuzzy sets, is used to specify the membership
functions of the input variables because it smooths out sharp transitions between values. The
bell-shaped form can be described by the equation
1
𝑀𝐹 (𝑥) = 2𝑏
, (1)
1 + [ 𝑥−𝑐
𝑎 ]
which is given by three numbers (𝑎, 𝑏, 𝑐) corresponding to the abscissas of the extreme points
and the center of the curve.
An example of the membership functions of the fuzzy variable "Investment" is given in Figure
6.
Figure 6: The membership functions of the variable "Investments"
The output variables of each subsystems are set in a trapezoidal shape (allows use them as
input variables of the fuzzy system "Project Completion"):
⎧
⎪ 𝑏−𝑥
, 𝑎 ≤ 𝑥 ≤ 𝑏,
⎪ 𝑏−𝑎
𝑀𝐹 (𝑥) = ⎨ 1, 𝑏 ≤ 𝑥𝑐, (2)
⎪
⎪ 𝑥−𝑐
⎩ 𝑑−𝑐 , 𝑐 ≤ 𝑥 ≤ 𝑑
where (𝑎, 𝑏, 𝑐, 𝑑) denote the abscissas of the vertices of the trapezoid. As an example, the
form of membership functions of the variable "Human Resources" is given in Figure 7.
Table 2
The simulation results of a fuzzy estimation system
Qualification level Workload Reliability of technical support Quality of IS functioning
0.888 0.908 0.45 0.365
0.0764 0.254 0.879 0.498
0.328 0.0665 0.14 0.361
0.909 0.894 0.929 0.5
0.919 0.0567 0.939 0.631
0.928 0.946 0.865 0.497
Figure 7: The membership functions of the variable "Human Resources"
5. Case study
Ruleviewer has been used for analysis of the correct operation of the proposed fuzzy system in
the Matlab environment. The advantage is that it is possible to evaluate the performance of
each subsystem of the proposed system, as well as the overall system. An example of a fuzzy
system with given input variables "Project execution time" 0,143, which corresponds to a fuzzy
set "short", "Human Resources" 0,113 (fuzzy set "average"), and " Financial Resources " = 0,87
(belongs to the set of "big"). According to the rule base proposed by the authors at such values
of input variables, the output of the fuzzy system should be set by the value belonging to the
fuzzy set "almost in time".
Data in Table 2 confirms the correct operation of the proposed system due to input and output
variables values.
In addition, the correctness of the work is confirmed by the surface values (Figure 8).
The surface presented in Figure 8 confirm the nonlinear effect of the input variables value on
the output, i.e. the correctness of the rules base, and hence the work of the developed fuzzy
system.
Thus, the fuzzy IT project management system proposed by the authors is workable and can
be used in real systems.
Among the systems described in the literature, the closest to the proposed are [18].
The expert decision-making fuzzy model system for the evaluation of project success [18]
consists of three sub-models: a fuzzy model of project state evaluation, a fuzzy model of total
project risk evaluation, and a fuzzy model of project quality evaluation. There are six input
variables, four rule blocks and one output variable in the fuzzy model.
Figure 8: The values surface of fuzzy system
The inputs of the submodel evaluates the project status are represented by two variables:
schedule performance index and cost performance index.
The inputs of the sub-model evaluate the total value of project risk are represented by two
variables: number of sub-risk and total value of sub-risks.
The inputs of the third sub-model evaluate project quality are represented by two variables:
degree of compliance with the requirements and eligibility for use.
Partial outputs from the sub-models are simultaneously inputs for fuzzy model of project
success evaluation, from which there is a single output variable – project success.
Although the structure of such a fuzzy system for the evaluation of project success, consisting
of three fuzzy sub-models, is similar to that proposed in this paper, but the input variables do
not reflect the influence of external and internal environmental factors on external parameters.
Thus, the proposed by authors fuzzy system takes into account more project parameters and is
therefore better to use.
6. Conclusions and Further Research
The proposed fuzzy IT project management system consists of subsystems, each of them can
be used as a separate system for assessing a certain factor. The outputs of each subsystem are
input values for a fuzzy system for timeliness estimating of project completion.
The proposed method, based on fuzzy logic, enabling the project managers to assess: (i) the
impact of project environment factors on human and financial resources, (ii) the duration of
individual works and the project as a whole. Moreover, the proposed method can be considered
as the basis for proactive changes in project activities.
The experimental research have been conducted using Ruleviewer in the Matlab environment,
and the confirmed a correct operation of the proposed fuzzy system as well as a possibility of
its usage in real systems.
A direction of the further research is developing a tool for assessing project completion.
References
[1] L. Zadeh, Knowledge representation in fuzzy logic, IEEE Transactions on Knowledge and
Data Engineering 1 (1989) 89–100. URL: https://doi.org/10.1109/69.43406. doi:10.1109/
69.43406.
[2] F. Habibi, O. T. Birgani, H. Koppelaar, S. Radenović, Using fuzzy logic to improve the
project time and cost estimation based on project evaluation and review technique (PERT),
Journal of Project Management (2018) 183–196. URL: https://doi.org/10.5267/j.jpm.2018.4.
002. doi:10.5267/j.jpm.2018.4.002.
[3] R. R. Yager, L. A. Zadeh (Eds.), An Introduction to Fuzzy Logic Applications in Intelligent
Systems, Springer US, 1992. URL: https://doi.org/10.1007/978-1-4615-3640-6. doi:10.1007/
978-1-4615-3640-6.
[4] S. G. MacDonell, A. R. Gray, Applying fuzzy logic modeling to software project
management, in: Software Engineering with Computational Intelligence, Springer
US, 2003, pp. 17–43. URL: https://doi.org/10.1007/978-1-4615-0429-0_2. doi:10.1007/
978-1-4615-0429-0_2.
[5] M. Komar, V. Kochan, L. Dubchak, A. Sachenko, V. Golovko, S. Bezobrazov, I. Romanets,
High performance adaptive system for cyber attacks detection, in: 2017 9th IEEE Inter-
national Conference on Intelligent Data Acquisition and Advanced Computing Systems:
Technology and Applications (IDAACS), IEEE, 2017. URL: https://doi.org/10.1109/idaacs.
2017.8095208. doi:10.1109/idaacs.2017.8095208.
[6] Y. P. Kondratenko, D. Simon, Structural and parametric optimization of fuzzy control and de-
cision making systems, in: Recent Developments and the New Direction in Soft-Computing
Foundations and Applications, Springer International Publishing, 2018, pp. 273–289. URL:
https://doi.org/10.1007/978-3-319-75408-6_22. doi:10.1007/978-3-319-75408-6_22.
[7] I. Romanets, A. Sachenko, L. Dubchak, Method of protection against traffic termination
in VoIP, in: 2018 10th International Conference on Electronics, Computers and Artificial
Intelligence (ECAI), IEEE, 2018. URL: https://doi.org/10.1109/ecai.2018.8678992. doi:10.
1109/ecai.2018.8678992.
[8] J. Zeng, M. An, N. J. Smith, Application of a fuzzy based decision making methodology
to construction project risk assessment, International Journal of Project Management
25 (2007) 589–600. URL: https://doi.org/10.1016/j.ijproman.2007.02.006. doi:10.1016/j.
ijproman.2007.02.006.
[9] H. A. E. Khalek, Risk and uncertainty assessment model in construction projects using
fuzzy logic, American Journal of Civil Engineering 4 (2016) 24. URL: https://doi.org/10.
11648/j.ajce.20160401.13. doi:10.11648/j.ajce.20160401.13.
[10] M. I. Boloş, D.-C. Sabău-Popa, P. Filip, A. Manolescu, Development of a fuzzy logic system
to identify the risk of projects financed from structural funds, International Journal of
Computers Communications & Control 10 (2015) 480. URL: https://doi.org/10.15837/ijccc.
2015.4.1914. doi:10.15837/ijccc.2015.4.1914.
[11] A. A. Khanfar, R. K. Mavi, F. Jie, Prioritizing critical failure factors of IT projects with
fuzzy analytic hierarchy process, Author(s), 2018. URL: https://doi.org/10.1063/1.5054257.
doi:10.1063/1.5054257.
[12] P. Asadi, J. R. Zeidi, T. Mojibi, A. Yazdani-Chamzini, J. Tamošaitieṅe, PROJECT RISK
EVALUATION BY USING a NEW FUZZY MODEL BASED ON ELENA GUIDELINE, Journal
of Civil Engineering and Management 24 (2018) 284–300. URL: https://doi.org/10.3846/
jcem.2018.3070. doi:10.3846/jcem.2018.3070.
[13] F. Habibi, O. T. Birgani, H. Koppelaar, S. Radenović, Using fuzzy logic to improve the
project time and cost estimation based on project evaluation and review technique (PERT),
Journal of Project Management (2018) 183–196. URL: https://doi.org/10.5267/j.jpm.2018.4.
002. doi:10.5267/j.jpm.2018.4.002.
[14] G. A. Corona-Suárez, S. M. AbouRizk, S. Karapetrovic, Simulation-based fuzzy logic
approach to assessing the effect of project quality management on construction perfor-
mance, Journal of Quality and Reliability Engineering 2014 (2014) 1–18. URL: https:
//doi.org/10.1155/2014/203427. doi:10.1155/2014/203427.
[15] J. H. Ezzabadi, M. D. Saryazdi, A. Mostafaeipour, Implementing fuzzy logic and AHP into
the EFQM model for performance improvement: A case study, Applied Soft Computing
36 (2015) 165–176. URL: https://doi.org/10.1016/j.asoc.2015.06.051. doi:10.1016/j.asoc.
2015.06.051.
[16] R. Doskočil, P. Dostal, Project success evaluation model based on fis, in: 17th International
Conference Perspectives of Business and Entrepreneurship Development in Digital Age,
2017, pp. 147–153.
[17] S. D. Bushuiev, V. M. Molokanova, Formalization of the accounting valuable memes
method for the portfolio of organization development and information computer tools for
its implementation, INFORMATION TECHNOLOGIES AND LEARNING TOOLS 62 (2017)
1–15.
[18] R. Doskočil, S. Škapa, P. Olšová, Success evaluation model for project management,
Economics and Management (2016).
[19] S. M. Hatefi, J. Tamošaitieṅe, AN INTEGRATED FUZZY DEMATEL-FUZZY ANP MODEL
FOR EVALUATING CONSTRUCTION PROJECTS BY CONSIDERING INTERRELATION-
SHIPS AMONG RISK FACTORS, JOURNAL OF CIVIL ENGINEERING AND MANAGE-
MENT 25 (2019) 114–131. URL: https://doi.org/10.3846/jcem.2019.8280. doi:10.3846/
jcem.2019.8280.
[20] N. Vasylkiv, I. Turchenko, L. Dubchak, Fuzzy model of the IT project environment impact on
its completion, in: 2020 10th International Conference on Advanced Computer Information
Technologies (ACIT), IEEE, 2020. URL: https://doi.org/10.1109/acit49673.2020.9208914.
doi:10.1109/acit49673.2020.9208914.
[21] S. Shtovba, Providing of accuracy and transparency of fuzzy mamdani model for studying
by experimental data, Problems of Control and Informatics 4 (2007) 102–114.
[22] L. Dubchak, N. Vasylkiv, V. Kochan, A. Lyapandra, Fuzzy data processing method, in: 2013
IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Com-
puting Systems (IDAACS), IEEE, 2013. URL: https://doi.org/10.1109/idaacs.2013.6662709.
doi:10.1109/idaacs.2013.6662709.