=Paper=
{{Paper
|id=Vol-2851/paper38
|storemode=property
|title=Fuzzy Controller of IT Project Management
|pdfUrl=https://ceur-ws.org/Vol-2851/paper38.pdf
|volume=Vol-2851
|authors=Nadiia Vasylkiv,Lesia Dubchak,Anatoliy Sachenko
|dblpUrl=https://dblp.org/rec/conf/itpm/VasylkivDS21
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==Fuzzy Controller of IT Project Management==
https://ceur-ws.org/Vol-2851/paper38.pdf
Fuzzy Controller of IT Project Management
Nadiia Vasylkiva, Lesia Dubchaka, Anatoliy Sachenkoa,b
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
The external environment can be favorable for the IT project, i.e. provide opportunities for
timely and successful achievement of the goal. 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. 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. Factors of the project environment that may affect its implementation, managers
mostly predict at the stage of project origin. But it is impossible to determine such an impact
in advance, because the IT project operates in conditions of uncertainty. Therefore, in the
study of the impact of the project environment on its implementation and completion, it is
advisable to use fuzzy logic. The fuzzy controller of IT project management, proposed by the
authors, consists of subsystems that allow analyzing the impact of both external and internal
environmental factors on the time of individual works or stages of the project, financial,
human resources, and in total - on project completion. Each of the subsystems can be
considered as separate self-sufficient parts. The proposed approach allows project managers
to assess the impact of project environment factors on its implementation.
Keywords 1
IT Project, Project Management, Project Environment, Project Execution Time, Human
Resources, Financial Resources, Investments, Fuzzy Controller, Simulink
1. Introduction
Projects in the field of information technology are characterized by certain specifics of their
implementation. But, like any other, they are exposed to various factors of the project environment.
These factors can contribute to the timely and high-quality implementation of project work, which
will lead to the successful achievement of the project goal. But they can, conversely, have a negative
impact on the project implementation process. As a result of such impacts, the project undergoes
changes that concern either deadlines, or contractors, or other resource provision.
Under the influence of the external environment, in particular its factor such as scientific and
technological progress, the form and means of realization of the IT project product may change, and
this, accordingly, will require significant changes in time, material, human resources and cost or even
refusal to further implementation of this project.
1
Proceedings of the 2nd International Workshop IT Project Management (ITPM 2021), February 16-18, 2021, Slavsko, Lviv region,
Ukraine
EMAIL: nvs@wunu.edu.ua; as@wunu.edu.ua; dlo@wunu.edu.ua
ORCID: 0000-0002-4247-7523, 0000-0002-0907-3682, 0000-0003-3743-2432
©️ 2021 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)
Project management in conditions of risks and uncertainties caused by both external and internal
environment of the IT project requires timely identification of such factors, comprehensive
assessment of their impact on project implementation and appropriate decision-making to increase
their positive, conversely, eliminate, or reduce negative effects per project.
Since during IT project implementation there is always a some uncertainty associated with the
project environment, so it is advisable to manage the project using methods based on fuzzy logic
[1, 2], which allow not only assess the project status in real-time, but also proactively manage its
successful implementation.
2. Related Work
In research that aimed at identifying factors of the projects implementation influencing, the theory
of fuzzy sets is increasingly used, in particular in [3, 4].
The developed contingency risk model [5]demonstrate 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.
The paper [6] provides a step-by-step approach for accurate estimation of time and cost of projects
using the project evaluation and review technique and expert views as fuzzy numbers.
The fuzzy logic system developed in [7] seeks to identify the financial risk of projects financed
from structural funds when changes occur in project values, in the duration of the projects and in the
implementation durations. Those two factors influence the financial risk.
The fuzzy model of risk assessment of unsuccessful completion of IT-project was proposed in [8].
The aim of this paper is to identify critical failure factors for IT projects, classify these factors based
on their original source, and their prioritize using fuzzy analytic hierarchy process.
The fuzzy inference system for evaluation of project success is presented in [9]. The reliable
proposed expert decision-making fuzzy model consists three input variables (project status, project
risk, project quality), one rule block and one output variable (project success).
Fuzzy model for the risk evaluation of projects based on the Elena guideline, proposed in [10],
comprises a three-stage procedure, including vulnerability assessment, consequence assessment, and
the overall risk evaluation. All stages use the fuzzy reasoning to cope with the inherent uncertainty
imposed by projects.
The reference [11] presents an expert fuzzy model for evaluation of the project success rate,
including partial sub-models.
Reference [12] describes the integrated fuzzy model for evaluating the construction projects by
considering the risk factors. Proposed fuzzy model is used to determine the interrelationships and
interdependencies among risk factors.
The fuzzy estimation method of the information system providing part influence on the
functioning quality is developed in [13]. It`s based on a fuzzy system, the inputs of which are the
states of reliability for software, technical and information providing, and the output is the quality of
system functioning.
The paper [14] analyzes various methods of structural and parametric optimization for fuzzy
control and decision-making systems. Special attention is paid to hierarchical structure selection, rule
base reduction, and reconfiguration in the presence of incomplete data sets.
The impact of the external environment of the project on its implementation is considering partly
in the fuzzy model [15], which has the input values of the environmental factor, financial and human
resources.
Some researchers suggest, in addition to fuzzy systems, to develop fuzzy controllers that make
management processes particularly effective in conditions of risk and uncertainty.
In the paper [16], the analytical structure of a Takagi-Sugeno Fuzzy Logic Controller with two
inputs and one output for software development effort estimation is discussed. The analytical study is
also presented with two sample inputs.
The paper [17] deals with the problems of fuzzy controllers design. The optimization approach for
increasing efficiency of fuzzy controllers design process based on their structural-parametric
optimization are discussed.
Given current trends in the fuzzy logic methods using in project management, the authors
proposed in [18] to analyze the influence of both external and internal factors on the project time,
financial, human resources, to 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.
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 (see Figure 1) [18].
Internal Human
environment resources
External Project execution time Project
environment Completion
Financial
Investments resources
Figure 1: The impact of the project environment on its completion
As can be seen from Fig. 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 [19-21]. The general scheme of this fuzzy system is given
in Figure 2 [18].
Subsystem
"Project Execution
Time"
Subsystem Fuzzy system
"Human Project
based on Mamdani fuzzy
Resources" Completion
inference method
Subsystem
"Financial
Resources"
Figure 2: The general structure of the fuzzy system "Project Completion"
3.1. Subsystem "Project execution time"
The subsystem for analyzing the project execution time as input variables has external and internal
environment and investments (see Figure 3) [18].
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 35 rules.
External
environment
FS1 Project
(Fuzzy system execution
Internal based on Mamdani fuzzy
environment time
inference method)
Investments
Figure 3: Fuzzy subsystem "Project execution time"
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 (see Figure 4) [18].
External
environment FS2
(Fuzzy system Human resources
based on Mamdani fuzzy
Internal inference method)
environment
Figure 4: Fuzzy 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;
• 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 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.
Table 1
Correlation of the input and output variables of the fuzzy subsystem "Human Resources"
External Internal Human
Environment Environment 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
3.3. 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 (see Figure 5) [18].
External
environment FS3
(Fuzzy system based on Financial
Mamdani fuzzy resources
Investments inference method)
Figure 5: Fuzzy 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 subsystem consists of 11 rules of the "if then" type. Thus, considering noted structures of fuzzy
subsystems, the fuzzy system will have the architecture shown in Figure 6.
External
environment
Internal FS1 Project Execution
environment Time
Investments
External
environment Fuzzy
FS2 Human Resources system Project
Completion
Internal
environment
Investments
FS3 Financial
Resources
External
environment
Figure 6: The structure of the fuzzy system "Project Completion"
The proposed fuzzy IT project management system should be implemented using a fuzzy
controller.
4. Fuzzy Controller
An important application of fuzzy set theory is fuzzy logic controllers, which are used in various
control systems. Instead of a mathematical model to describe the system, such controllers use the
integrated knowledge of experts, which in the structure of the representation is close to spoken
language and is described using linguistic variables and fuzzy sets
The basic fuzzy controller consists of four main components:
• fuzzification unit (simply changes the inputs so that they can be interpreted and compared with
the rules of the knowledge base);
• knowledge base (rule base and knowledge base, in the form of a set of rules on how best to
manage the system);
• decision-making unit (a logical inference mechanism that evaluates which rule is currently
relevant and then decides what should be submitted for entry);
• defuzzification unit (transmits the conclusions made by the logical inference mechanism to the
inputs).
The model of fuzzy controller of access to the evaluation system of a general education institution
can be built with the help of Simulink tools. Simulink is an interactive tool for modeling, simulating
and analyzing dynamic systems, including discrete, continuous and hybrid, nonlinear and
discontinuous systems.
Modeling of the proposed controller is carried out using the Fuzzy Logic Controller unit. This unit
connects the fuzzy system developed in the previous section and allows further coding in HDL, which
can be used when programming FPGA.
The proposed fuzzy controller consists of three controllers that implement the subsystems
“Financial resources”, “Project execution time” and “Human resources”, described above, and can be
independently implemented in certain systems.
Input variables “External environment”, “Internal environment” and “Investments” are set to
Random Number.
The general scheme of the fuzzy controller of IT project management is given in Figure 7.
Figure 7: The general scheme of the fuzzy controller
General scheme of every noted subsystem are given in Figure 8.
Figure 8: General scheme of subsystem
Figure 9 shows the scheme of processing the input fuzzy values according to the rule of type "if -
then". Simulink processes the rules from the knowledge base, taking into account the rating displayed
by the constant Weight.
Figure 9: Scheme of processing of incomplete fuzzy values by a rule of type "if - than"
To make a conclusion according to the Mamdani mechanism, the fuzzy controller performs
defuzzification. This process performs a "min-max" composition of the rules of the fuzzy knowledge
base and gives the value of the center of gravity of the final figure, which corresponds to a certain
value of the output variable.
The scheme of defuzzification of the fuzzy controller for IT project management of the breast is
presented in Fig. 10.
Figure 10: Scheme of defuzzification unit of proposed fuzzy controller
5. Case Study
Analysis of the fuzzy controller is performed by analyzing the data from the Scope Block, which
are connected to the corresponding blocks of subsystems “Financial resources”, “Project execution
time” and “Human resources”.
The results of the developed fuzzy controller of IT project management in accordance with the
data of Figure 11 are presented in Figure 12.
Figure 11: The outputs of fuzzy subsystems
Figure 12: The output of IT project management fuzzy controller
Analysis of simulation results confirms the correctness of the results and the efficiency of the
developed fuzzy controller of IT project management. Therefore, the proposed tool can be used in
appropriate control systems.
6. Conclusion and Further Research
The proposed fuzzy IT project management controller consists of subsystems, each of them can be
used as a separate controller for assessing a certain factor. The outputs of each subsystem are input
values for a fuzzy controller 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 Scope Block in the Simulink
environment, and the confirmed a correct operation of the proposed fuzzy controller as well as a
possibility of its usage in real systems. A direction of the further research is FPGA programming for
assessing project completion.
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