Critical Paths Identification on Fuzzy Network Project

IOSR Journal of Mathematics (IOSR-JM)
e-ISSN: 2278-5728, p-ISSN: 2319-765X. Volume 11, Issue 1 Ver. VI (Jan - Feb. 2015), PP 49-54
www.iosrjournals.org
DOI: 10.9790/5728-11164954 www.iosrjournals.org 49 | Page
Critical Paths Identification on Fuzzy Network Project
Alaulden N. A.1
and Saad M. S.2
Professor, Department of Mathematics and Computer Applications, College of Science, Al-Nahrain University,
Baghdad-Iraq1
Ass.Prof., Department of Mathematics and Computer Applications, College of Science, Al-Nahrain University,
Baghdad-Iraq2
Abstract: In this paper, a new approach for identifying fuzzy critical path is presented, based on converting the
fuzzy network project into deterministic network project, by transforming the parameters set of the fuzzy
activities into the time probability density function PDF of each fuzzy time activity. A case study is considered as
a numerical tested problem to demonstrate our approach.
Keywords: Project network, Fuzzy Network, Defuzzification Techniques.
I. Introduction
In recent years, the range of project management applications, under uncertainty are extremely critical
for many organizations has greatly expanded. Project management concerns the scheduling and controlling of
activities (tasks) in such a way that the project can be completed in as little time as possible.
As a result, the uncertainty associated with such risky projects should be reduced. The problem of
identifying critical activities in deterministic problems is well understood. Since a project could be delayed if
these activities were not completed in the scheduled time, Standard Critical Path Method (CPM) analyses can be
used to identify the longest path(s), known as the critical path(s), in an activity network. However, there are
many cases where the activity times may not be presented in a precise manner. To deal quantitatively with
imprecise data, the (PERT) based on the probability theory can be employed, and the probability distributions of
each activities is needed, it is difficult to use in some situations when the priori data of the activity probability
distributions are absence.
Identifying critical activities in a fuzzy project is difficult problems. Several methods had been
proposed contain series draw backs which lead to identifying critical fuzzy activities incorrectly, leaving project
mangers without means to identify the most probable sources of project delays. A new direction for identifying
fuzzy critical path activities in stochastic project is based on different philosophy, than in deterministic project,
where each critical activity must correspond to zero time slack activity, while such condition need not to be
necessary. We immediately encounter difficulties developing concepts analogous to total slack and "critical"
activities for stochastic project. Such concept is a criticality index, defined as the probability that an activity
will lie on a critical path. However, an activity may lie on a critical path without introducing risk of project
delay. Based on such concept, a critical degree is constructed to schedule fuzzy critical paths, [1].
Fuzzy set theory has been proposed to handle non crisp parameters (fuzzy) by generalizing the notion
of membership in a set. Essentially, in a fuzzy set each element is associated with a point value selected from
the unit interval [0,1], which is an arbitrary grade of truth referred to as the grade of membership in the set. The
main objective in FLP is to find the best solution possible with imprecise, vague, uncertain or incomplete
information.
Many previous studies on fuzzy project management network are reviewed before. In [2], Prade (1979)
first applied fuzzy set theory into the project scheduling problem. Furthermore, in [3], [4], [5], [6] and [7],
various types of project scheduling problems with fuzzy activity duration times are discussed.
An alternative way to deal with imprecise data is to employ the concept of fuzziness; the main
advantages of methodologies based on fuzzy theory are that they do not require prior predictable regularities or
posterior frequency distributions . The problems of computing the intervals of possible values of the latest
starting times and floats of activities with imprecise durations represented by fuzzy or interval numbers, and
many solution methods have been proposed, in which most of them are straightforward extension of
deterministic CPM. In [8], a nice literature survey about this subject mentioned the works had been presented.
Most of the systems work with fuzzy values, which have to be mapped to non-fuzzy (crisp) values after
conversion processing called defuzzification. Various defuzzification methods have been proposed in [9], [10]
& [11]. Many researchers attempted to understand the logic of the defuzzification process from the perspective
of invariant transformation between different uncertainty paradigms, including basic defuzzification distribution,
semi-linear defuzzification and generalized level set defuzzification. from the perspective of invariant
transformation between different uncertainty paradigms, including basic defuzzification distribution, semi-linear
defuzzification and generalized level set defuzzification. s from the perspective of invariant transformation

between different uncertainty paradigms, including basic defuzzification distribution, semi-linear defuzzification
and generalized level set defuzzification, see [12], [13], [14] & [15]. In [16] they attempt to understand the
defuzzification problem from the scope of optimal selection of an element from a fuzzy set. They used the
concepts of interaction, variability, and voting techniques to compute an optimal solution. In [17], they proposed
a fuzzy clustering based defuzzification method. In [18], they proposed a defuzzification method with most
typical values. In [19], he proposed a method for defuzzification with weighted distance. Smith [20], proposed a
dynamic switching defuzzification method for fuzzy control. In [21], he proposed a defuzzification method with
the “nearest” symmetric triangular fuzzy number of a fuzzy set. In [22], he proposed a procedure to defuzzify
fuzzy subsets and interval values by employing the concept of sensitivity analysis with a kind min-max principle.
There are also researchers who tried to build an axiomatic foundation for the defuzzification theory [23] & [24].
It should be noted that with the developments of intelligent technologies, some adaptive and parameterized
defuzzification methods that can include human knowledge have been proposed. In [25], they used neural
networks for defuzzification. Song, et al. [26], proposed an adaptive learning defuzzification technique. In [27],
he proposed a knowledge based defuzzification process become more intelligent. Although so many
defuzzification methods have been proposed so far, no one method gives a right effective defuzzified output.
The computational results of these methods often conflict, and they don’t have a uniform framework in
theoretical view. We often face difficulty in selecting appropriate defuzzification methods for some specific
application problems. Most of the existing defuzzification methods tried to make the estimation of a fuzzy set in
an objective way. However, an important aspect of the fuzzy set application is that it can represent the
subjective knowledge of the decision maker. Different decision makers may have different perception for the
defuzzification results.
In this paper, we are presented a new approach for identifying critical path based on defuzzification
parameters set of fuzzy types activities, by using the concept of the probability density function of each fuzzy
activity.. A case study is considered to explain the proposed approach.
II. Basic Concepts
In this section, we are presented basic concepts of fuzzy set theory and fuzzy network problems.
Usually the structures embedded in fuzzy set theories are less rich than the Boolean lattice of classical set theory.
Moreover, there is also some arbitrariness in the choice of the valuation set for the elements: the real interval
[0,1] is most commonly used.
Definition (1) Fuzzy Sets, [27],[28],[29]:
Let U be a universe set. A fuzzy set 𝐀 of 𝐔 is defined by a membership function µA x → [0,1], where µA x
indicatesthe degree of x in 𝐀 which defined as following:
µ =
0, −∞, a1
f1 x , a1,a2
1, a2, a3
f2 x , [a3, a4]
0, [a4,+∞)
(1)
where a1, a2, a3 and a4are real number, note that f1 x andf2 x are may be linear or convex nonlinear function.
Definition (2) Fuzzy Number, [30]:
A fuzzy number Ais a convex normalized fuzzy set Aof the real line R such that:
1. It exists exactly one x0 ∈ R with μA x0 = 1 (x0is called the mean value of A).
2. μA(x)is piecewise continuous.
III. Proposed Approach
Definition (3) Defuzzification, [31]:
Deffuzification is a mapping from space of fuzzy action defined over an output universe into a space of
nonfuzzy (crisp) actions.
In this paper we proposed a defuzzification method to obtain a Probability Density Function from
Membership Function [32]. If we considered Ai = (ai1, ai2,ai3, al4) is fuzzy trapezoidal number with
membership function defined as (1), where f1 x and f2 x are linear function, (if they are convex nonlinear
functions, we are using any approximated methods to linearized them). Let the function pAi
defined by
pAi
x = ciμAi
(x) is a probability density function associated with Ai, where ci can be obtained by the property
that pi x dx = 1
∞
−∞
as following:
ci =
2
ai4+ai3−ai2−ai1
(2)

Now, we are using the following transformation called Mellin Transform to find the expected value of the
activity fuzzy duration times.
Definition (10) Mellin Transform, [33]:
The Mellin transformμX s of a probability density function f(x), where x is positive, is defined as
μX s = xs−1
f x dx
∞
0
(3)
whenever the integral exist.
Now it is possible to think of theMellin transform in terms of expected values recall that the expected value of
any function g(x) of the random variable X, whose distribution is f(x), is given by
E g x = g x f x dx
∞
−∞
(4)
There for it follows that
μX s = E[xs−1
] = xs−1
f x dx
∞
0
(5)
Hence
E xs
= MX s + 1 (6)
Thus, the expectation of random variable X is E x = MX 2 .
Now, if we let A = (a1, a2, a3,a4) an arbitrary trapizoidal fuzzy number, then the density function f(x)
corresponding to A is as follows:
pAi
x =
2(x−ai1)
(ai4+ai3−ai2−ai1)(ai2−ai1)
, ai1 ≤ x < ai2
2
ai4+ai3−ai2−ai1
, ai2 ≤ x ≤ ai3
2 ai4−x
ai4+ai3−ai2−ai1 ai4−ai3
, ai3 < x ≤ ai4
0, otherwise.
(7)
The Mellin transform is the obtained by:
ℳAi
s = xs−1
pAi
x dx
∞
0
=
2
ai4+ai3−ai2−ai1 s2+s
ai4
s+1
−ai3
s+1
ai4−ai3
−
ai2
s+1
−ai1
s+1
ai2−ai1
(8)
and
E Ai = ℳA 2 =
1
3
ai1 + a2i + ai3 + ai4 +
ai1ai2−ai3ai4
ai4+ai3−ai2−ai1
(9)
Therefore, all the fuzzy durations times are transformed into crisps values as expected values. Now, we
implemented the following proposed standard Critical Path Method "CPM. For each activity, an expected
duration time tij is calculated using (6).defined, where tij is the time required for the completion of activity (i,j).
A critical path is a longest path, and an activity on a critical path is called a critical activity. Let ESi and LFi
be the earliest start time event i, and the finish time event i, respectively. Let Dj be a set of events obtained
from event j and i< j. We then obtain ESjusing the following equations:
ESj = maxi∈Dj
ESi + tij and ES1 = LS1 = 0 (10)
Similarly, let Hi be a set of events obtained from event i and i< j . We obtain LFi using the following equations:
LFi = minj∈Hi
[LFj − tij] and LFn = EFn (11)
The interval [ ESi, LFj] is the time during which the activity (i,j) must be completed. Now, we can calculate the
float time Tij of the activity (i,j) can be computed as follows :
Tij = LFj − ESi − tij (12)
Hence we can obtain the earliest event time, latest event time, and the total float of every activity by using the
above last three equations, and the critical events are identified corresponding to their zeros values of total float
times.
IV. Case Study
We are considering a project network, figure (1), taken from [34] as a case study.As shown in table (1); the 30
activities are listed with their fuzzy operation times.
In order to solve such problem, the proposed method is implemented to convert the fuzzy time number to crisp
time number, explained in the table (2). Performed the standard critical path method CPM, to obtain the
following critical path 1⇒2⇒3⇒4⇒5⇒6⇒7⇒9⇒10⇒11⇒12⇒16⇒24 of the fuzzy network project.

Figure (1): Project Network
Table (1): ProjectConstruction
ActivityItem Activity Description PrecedenceItem Fuzzy Operation Time (per day)
P1 Concrete works foundation - (25,28,30,35)
P2 Insulation works P1 (3,4,4,5)
P3 Parking area + Roads + Landscape P2 (25,29,30,35)
P4 Back filling works P3 (3,7,12,15)
P5 Sub-base P4 (5,6,6,10)
P6 Steel structure erection P5 (26,30,35,40)
P7 Under ground drainage system P5 (7,10,10,13)
P8 Water tank - civil works - (15,21,21,25)
P9 Steel structure testing P6 (2,3,4,5)
P10 Roofing works P6 (9,10,12,15)
P11 Water tank – finishing P8 (6,7,8,10)
P12 HVAC works - 1st
fix P9 (12,14,14,16)
P13 Fire fighting works 1st
fix P9 (7,9,11,12)
P14 Electrical system works - 1st
fix P12, P13 (5,6,7,10)
P15 Flooring P14 (7,9,11,12)
P16 HVAC work-2nd
fix P9 (12,14,14,16)
P17 Fire fighting works – 2nd
fix P9 (7,9,11,12)
P18 Cladding works P9 (15,24,25,30)
P19 Electrical system works - 2nd
fix P16,P17 (5,6,7,10)
P20 Water tank – MEP P11 (9,11,12,14)
P21 Finishing works P15 (15,18,18,20)
P22 HVAC works - 3rd
P9 (12,14,14,16)
P23 Fire fighting work - 3rd
fix P9 (7,9,11,12)
P24 Electrical system works -3rd
fix P22,P23 (5,6,7,10)
P25 Plumbing works - 1st
fix P14 (5,6,6,8)
P26 Plumbing works – 2nd
fix P19 (5,6,6,8)
P27 Plumbing works - 3rd
fix P24 (5,6,6,8)
P28 Water tank testing P20 (1,2,2,3)
P29 Testing and commissioning P28 (1,2,2,3)
P30 Snag list and Initial handling P29 (5,7,7,9)
Table (2): Corresponding crisps expected duration times
Activity Item Crisp Duration Time (in days)
P1 27
P2 4
P3 28.625
P4 11.125
P5 5.625

P6 30.625
P7 10
P8 22.5
P9 3.5
P10 10
P11 7.375
P12 14
P13 10.375
P14 6
P15 10.375
P16 14
P17 10.375
P18 26
P19 6
P20 10.75
P21 16.875
P22 14
P23 10.375
P24 6
P25 6.125
P26 6.125
P27 6.125
P28 2
P29 2
P30 7
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Biography
Prof.Dr.Alaulden N. Ahmed is specialist in different areas of Operation Researches. He
received his Ph.D in 1986 from UK. He supervised of more than 50 M.Sc. students and 5
Ph.D students.
Mr.Saad M. Salman,published more than 10 papers in optimization field.

Critical Paths Identification on Fuzzy Network Project

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