ANN & Taguchi Method Predict Project Duration

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International Journal of Production Technology and Management (IJPTM)
Volume 13, Issue 1, January-December 2022, pp.44-55, Article ID: IJPTM_13_01_006
Available online at http://iaeme.com/Home/issue/IJPTM?Volume=13&Issue=1
ISSN Print: 0976- 6383 and ISSN Online: 0976 – 6391
DOI: https://doi.org/10.17605/OSF.IO/GB53A
© IAEME Publication
AN ARTIFICIAL NEURAL NETWORK-BASED
APPROACH COUPLED WITH TAGUCHI'S
METHOD FOR PREDICTING THE TOTAL
AVERAGE DURATION OF PROJECTS
Bendada Larbi1, Brioua Mourad2, Djeffal Selman3, Morakchi Mohamed Razi4
1
Department of mechanics, Laboratory of CMASMTF,
University of Larbi Ben M’hidi Oum-El-Bouaghi, Algeria
2
Department of mechanics, Laboratory LRPI, University of Batna 2, Algeria
3
Department of mechanics, Laboratory of CMASMTF,
University of Larbi Ben M’hidi Oum-El-Bouaghi, Algeria
4
Department of electrical engineering, Laboratory LGE University of M’sila, M’sila, Algeria
ABSTRACT
Nowadays, project duration prediction has become of crucial importance for
managers since it points out the expectancy-life of project realization. To this end, the
Neural Network-based approach coupled with the Taguchi method is used to predict
the necessary time, which allows the fulfillment of the targeted project within the
prescribed span without delay. Accordingly, the whole process for modeling the
targeted problem is described, in which the modeling and simulation of the activities
network are introduced for calculating the total average time of project. Then, the
neural network approach is adopted to predict the total time for finishing the considered
project within the deadlines, where the neural network’s input variables are composed
of success probability, improvement and learning factors. While, the output variable is
the total average project duration, which is the critical data during design phase. After
that, the well-known Taguchi method is purposefully used to optimize the already
obtained target by neural network. Finally, Simulation analysis through MATLAB are
used to show the efficiency of the proposed approach regarding the workability of the
approach when it comes to estimating the deadline of the targeted project.
Key words: Project, prediction, performances assessment, artificial neurons network,
Taguchi.
Cite this Article: Bendada Larbi, Brioua Mourad, Djeffal Selman and Morakchi
Mohamed Razi, An Artificial Neural Network-Based Approach Coupled with Taguchi's
Method for Predicting the Total Average Duration of Projects, International Journal of
Production Technology and Management (IJPTM), 13(1), 2022, pp. 44–55.
http://iaeme.com/Home/issue/IJPTM?Volume=13&Issue=1

Bendada Larbi, Brioua Mourad, Djeffal Selman and Morakchi Mohamed Razi
1. INTRODUCTION
Recently, growth, industrial market competition and manufacturing systems complexity have
generated a very difficult environment to manage and plan a new product launching. Creative
companies are under great pressure to create and maintain competitive advantages by reducing
product development time while maintaining a consistently high level of quality is considered
as a challenge to companies and put their employers under pressure [1] .
A mega-project of complex designs or constructions involves the execution of a very large
number of tasks through specialist participation from different disciplines. As the process
designs’ complexity increases, iterations attempt become a reality to eliminate the unnecessary
procedures for realizing the considered project, project managers must consider each possible
challenge and failure of each task during project progress. Therefore, a project must be realized
in within a short time.
The previously established methods to successfully deal with design activities modeling. ,
for instance GANTT and PERT diagrams are commonly used to plan design processes yet in
some cases they are less efficient, because these formalisms are too limited in the support of
design activities modeling with their iterations [2] [3]. To emphasize, iterations refer to the
possibility of redoing the actual task’ step or its precedent ones, where it is not feasible to re-
do the previous step of making a specific project. These design activities play a predominant
particular role, especially since several models have been developed to characterize repetitive
(iterative) design processes duration. These models include iterative sequential activities,
iterative parallel tasks, and coupled models. Among researches that addressed these models,
include the following works, as [4]. The design iteration makes the product development
process more complex and difficult to analyze which stimulate researchers to considerably
consider iterations as the basic features of the product development process. To study the
parameters’ impact on product development duration within the product development process
model, iteration is considered as a solution to figure out this problem. Based on the meta-model
construction in relation to the intervention model and using the response surface method with
a composite plan, the model has satisfied all test indices, worked efficiently. Interestingly, the
main influencing factors can be used for adjusting the product development process [4]. In [5],
the authors have adopted data from conventional back-propagating and general regression
neural networks from 112 construction projects in Hong Kong. They have examined recovery
work influence on various project performance indicators, as project costs, time overruns and
contractual claims. The results of this research is used to develop appropriate and intelligent
decision support systems and frameworks to improve construction projects performance. In the
work developed by [6], a procedure to estimate software projects duration is proposed by
applying the machine-learning techniques, namely Bayesian regulation training and Levenberg-
Marquardt training algorithms have been used in feed forward and radial-based neural
networks. These approaches are applied on data which are already obtained from already
established literature. After training models using both training algorithms, the authors
concluded that Bayesian regulation-based training offers better results than Levenberg-
Marquardt-based training. However, a very few works have been developed to accurately
predict the deadlines of a specific project using Neural network coupled with Taguchi method.
Within the same context, in this paper a neural network approach plus Taguchi method is used
to estimate the project’s deadlines. To emphasize, project average total duration values obtained
during simulation are modeled using artificial neural networks. The input variables are
composed of success probability, improvement and learning factors. In addition, output variable
is the average total project duration, which is the critical data during design phase. In [7], the
authors have developed an Adaptive Neural Network (CSANN) to figure out the job-shop
scheduling problem which paves the way to obtaining a control strategy in an effective material

An Artificial Neural Network-Based Approach Coupled with Taguchi's Method for Predicting the
Total Average Duration of Projects
for coping with the complexity of job-shop scheduling problem. In [8], the researchers’ purpose
is to obtain the optimal process parameters for hard turning of AISIM2 Steel by using CBN
Inserts and that is doable through the use of ANN tool in MATLAB environment to predict
material removal rate of AISI M2 Steel. In [9], authors discuss an ANN approach and illustrates
how they have played an important role in analysis and optimization performance parameters
of various IC engines. In [10], the researchers established a neural network-based model, in
which the experimental results are the training data to construct the artificial neural network
model which predicts the compressive strength of concrete containing Industrial By products.
In [11], researchers proposed a mathematical Artificial Neural Networks that is applied on
Multilayer Feedforward neural network with Backpropagation under Gauss Newton algorithm
to overcome the complexity of conventional model. As well as In [12], this research paper
focuses on some of the major prediction techniques used in the construction industry, in which
the HPA data analysis method coupled with ANN is used to provide support for risk
management processes. On the basis of the results, the HPA method displayed total variance in
common questionnaire items of each software risk factor to measure the weight of the factor
accurately. The HPA outcomes were juxtaposed on the ANN input representation enhance the
training and testing results and the comparison showed that the method can be utilized
effectively for the identification of risk effects in the entire phases of the project. The employed
method categorized the top risks into human skills, knowledge and the experience staffing level
and difficulties in applying software management and organization representing software cost
risks of the total risk classification. Within the same context in this paper, Neural Network-
based approach coupled with Taguchi method is used to predict the necessary time, which
allows the fulfillment of the targeted project within the prescribed span without delay.
The rest of this paper is organized as follows : in section 2, we present a brief overview on
the activities networks modeling, their parameters and the existing simulation approaches for
predicting project’s deadlines. Section 3 is devoted to the model used in this study, namely
project average total duration modeling via artificial neural networks (ANN). Section 4 is
dedicated to Taguchi method developed in this study. Results from this optimization are
discussed. Finally, we conclude by a review of the possibilities offered by artificial neural
network method (ANN) to calculating the predicted deadlines’ projects, and its efficiency can
be assessed through MSE values. The resulting errors are minimized using Taguchi method.
2. ACTIVITIES NETWORK MODELING
In this section, the process modeling process of activities network is presented that contains a
set of sequential activities with feedback loops and iterative cycles. The model presents various
scenarios for moving from one activity to another and challenging previous activities.
Figure 1 Activities sequencing with reconsideration
2.1 Model and Process Parameterization
To properly illustrate the proposed approach in this paper, an example of an activity network
comprising 100 serial activities is thoroughly discussed.

2.1.1 Activities Duration
Basically, some temporal indicators based on durations can be used, such as activities duration
sum, or average or the variance of these values. In this addressed problem, any activity is not
defined by a fixed duration, but by an indicative duration between two terminals [11]. A variety
of distributions or probability density functions (PDFs), including Beta distribution, are used to
represent the activity duration uncertainty. Generally, these distributions present positive
asymmetry due to expansion work tendency to fill available time - and since human nature
tends to have much more time of relaxation -so it is likely that activities last for a while, even
if it's feasible. Positive asymmetry can be represented by a triangular distribution, which is
simple to understand and construct, requiring only three data points per activity: optimistic,
probable, and pessimistic [12]. Our model has selected the triangular probability distribution to
represent activities duration since this distribution is simple and familiar to many project
managers [1] Similar distributions based on duration probabilities distribution according to a
triangular law can be mathematically explained as follows :
Each activity duration = a + (b – a) * (rand(n1,1) + rand(n1,1)) / 2 with a = min =1 and b =
max = 20, n1= 100
2.1.2 Success of an Activity
Any disturbance may lead to infeasibility or a result that does not comply with the activity
specifications. To integrate the disturbances effect in the simulation, we assign to each activity
a probability of achievement or success, noted (PS). Depending on each activity behavior, this
probability can be represented by different distribution functions or simply by discrete values.
2.1.3 Failure of an Activity
We consider that any failure leads to a reconsideration/questioning. If an activity fails in its
mission (success probability not achieved), a second probability (PRC) allows to choose a
reworking path.
2.1.4 Probability of Stepping Back in Loup
As it is shown in saquared matrix, that contains 100*100 in which the matrix’ diagnol takes
value 1. Aditionally the inferior part. For instance, line 6 *1 refer to 1/6 and 6*2 refers to the
previously obtained value multiplied by 2 and so forth.
PRC 1 2 3 4 5 6 7 8 9 10 … 99 100
1 1 0 0 0 0 0 0 0 0 0 0 0
2 0,5000 1 0 0 0 0 0 0 0 0 0 0
3 0,3333 0,6667 1 0 0 0 0 0 0 0 0 0
4 0,2500 0,5000 0,7500 1 0 0 0 0 0 0 0 0
5 0,2000 0,4000 0,6000 0,8000 1 0 0 0 0 0 0 0
6 0,1667 0,3333 0,5000 0,6667 0,8333 1 0 0 0 0 0 0
7 0,1429 0,2857 0,4286 0,5714 0,7143 0,8571 1 0 0 0 0 0
8 0,1250 0,2500 0,3750 0,5000 0,6250 0,7500 0,8750 1 0 0 0 0
9 0,1111 0,2222 0,3333 0,4444 0,5556 0,6667 0,7778 0,8889 1 0 0 0
10 0,1000 0,2000 0,3000 0,4000 0,5000 0,6000 0,7000 0,8000 0,9000 1 0 0
… 0 0
99 0,0101 0,0202 0,0303 0,0404 0,0505 0,0606 0,0707 0,0808 0,0909 0,1010 1 0
100 0,0100 0,0200 0,0300 0,0400 0,0500 0,0600 0,0700 0,0800 0,0900 0,1000 0,9900 1
1 2 3 4 5 6 7 8 9 10 … 99 100
Figure 2 Reconsideration probabilities matrix (PRC) of one-hudred activities

2.2 Process Parameters
Table 1 Simualtion sequences of project avrerage total duration
Level Success probability Improvment factor Learning factor
(PS) (Coef_AM) CofA
1 0,795 0,00 0,6
2 0,825 0,05 0,8
3 0,950 0,10 1,0
2.3 Data Standardization
Before applying ANN to the data, input and output learning values must be normalized, as most
learning algorithms are sensitive to data scale. In general, ANN technique main advantage over
traditional methods is that it does not require describing explicitly in a mathematical form [2]
the data on considered underlying process complex nature.
In this study, the input and output data were scaled using the normalization equation as
follows:
𝒁𝒊 =
𝑿𝒊−𝑿𝒎𝒊𝒏
𝑿𝒎𝒂𝒙−𝑿𝒎𝒊𝒏
(1)
Where Zi the standard or transformed data series, Xi is the original data series, Xmin and Xmax
are the minimum and maximum of the data set, respectively.
After training and test results, output values are normalized to obtain the output in the data
original scale. The algorithm for the Multilayer Perceptron Neural Network Model (MLP) is
implemented using MATLAB software.
3. ARTIFICIAL NEURONS NETWORK
A neural network is biologically inspired by human nervous system operation. A neural network
is widely applied in different fields of application because of its learning ability, ie ability to
extract rules and learn data and to create a network model that can be used for classification,
pattern recognition and data forecasting. The most promising feature of the neural network that
other classification techniques do not have is that it helps to simulate the network and create a
model that can be used more and applied to new data that has not previously exposed to the
network.
MATLAB is a data operating tool that provides a neural network toolbox (NNT) for neural
network modeling. The NNT consists of an NN tool that helps create a neural network model
for training and testing data to classify data, find hidden models, group and predict future
applications and benefits. There are several other neural network tools like SPSS, but MATLAB
NN Tool is very popular due to the large number of support features it offers.
3.1 Project Average Total Duration Modeling Using Artificial Neural Networks
(RDN)
RdN Technique has emerged as a powerful modeling tool that can be applied to many scientific
and/or technical applications, such as: model reorganization, classification, data processing and
process control. An artificial neural network simulates the computational human brain
mechanism to implement the behavion [13].
Simulation results are used to develop an ANN model to predict the average total project
duration (DTMP) via Matlab Neural Network Toolbox. There are three inputs and one output
in the ANN model. The input variables represent the success probability (PS), improvement

coefficient (Coef_AM) and learning coefficient (CofA), while output variable is the project
average total duration (DTMP). Total of 27 results obtained from simulations are used in the
network formation (Table 3). The chosen network structure is illustrated in Figure 3. The
neurons are arranged in the form of layers in the forward feeding ANN, and the nodes output
of a layer are introduced to the next layer by weights iput. Two hidden layers are used as well
as ANN formation.
Figure 3 Artificial neuronal network architecture adopted for our work
Several functions are used in a neural network framework, where we can find for example
functions of linear transfer, sigmoid and sigmoidal tangent, as indicated via equations below
[14].
Purelin (x) = x (2)
logsig (x) =
1
1+𝑒−𝑥 (3)
tansig (x) =
2
1+𝑒−2𝑥
− 1 (4)
Where x is the network input
The main concern for an ANN structure designer is to determine the appropriate hidden
layers number and neurons number in each layer.
Network performance is determined by the MSE predicted values that should be broadly
minimized. MSE parameter is calculated via equation (5). Table 4 shows DTMP predicted
values obtained via ANN’s test data. MSE value, obtained from ANN, is calculated from the
following equation:
𝑀𝑆𝐸 =
1
𝑄
∑ 𝑒
𝑄
𝑘=1 (𝑘)2
=
1
𝑄
∑ [𝑡(𝑘) − 𝑦(𝑘)]2
𝑄
𝑘=1 (5)
Where e(k) is the error between target and ANN output, t (k) is the target, y (k) is the output
value per ANN and Q is the total data number [15].
H3
X3
X1 H1
H2 Project average total duration
X2
Learning factor
Improvement factor
Success probability
Output layer
Hidden layer
Input layer
Y

Figure 5 Validation Performance validation from neuron networks model
4. TAGUCHI METHOD
Taguchi method is a well-known technique that relies on a systematic and efficient method for
process optimization and a powerful tool for designing high quality systems. Taguchi approach
application in experiments design is easy to adopt and apply for users with statistics limited
knowledge. For this, it has gained great popularity within engineers and scientists community.
It is an engineering methodology to obtain product and process status, which are insensitive to
different causes of variation, and which produce high quality products with low development
and manufacturing costs. The signal-noise ratio (S / N) and orthogonal network are two main
tools used in the design.
4.1 Taguchi Analysis
The signal-noise (S/N) characteristics can be divided into three categories when the
characteristic is continuous. The S/N ratio is as follows:
a) For rating value:








= 2
2
log
10
S
y
SNT
(6)
b) For minimum value:






−
= 
=
n
i
i
S y
n
SN
1
2
1
log
10
(7)
c) For maximum value:








−
= 
=
n
i i
L
y
n
SN
1
2
1
1
log
10
(8)
Where:
S/N : Signal-noise ratio,
n : Simulation number,
y : Obtained value of project average total duration

Table 5 Transfer functions combination
A B C
Trans_F Numb_N Train_F
Level Transfer Function Neurons number Training function
1 LOGSIG 9 TRAINLM
2 PURELIN 10 TRAINGDM
3 TANSIG 18 TRAINBR
The combination of three transfer functions (LOGSIG, PURELIN and TANSIG), numbers
of neurons (9, 10 and 18) and learning functions (TRAINLM, TRAINGDM and TRAINBR)
provided by NNTool, MATLAB software, allowed the obtention of the MSE predicted values.
To better illustrate the models quality simulation, we will present modeling and simulation
results, to better analyze models robustness as well as their predictive power.
Figure 6 Total duration average values for each parameter at different levels
Table 7 Average values of average total duration with different levels
Process parameters
Levels A B AB C AC BC ABC
1 0,0271 0,0065 0,0007 0,0034 0,0004 0,0040 0,0058
2 0,0075 0,0255 0,0273 0,0246 0,0292 0,0245 0,0264
3 0,0028 0,0054 0,0093 0,0093 0,0078 0,0088 0,0051
Minimum 0,0028 0,0054 0,0007 0,0034 0,0004 0,0040 0,0051
Maximum 0,0271 0,0255 0,0273 0,0246 0,0292 0,0245 0,0264
Estimation of factor main effects on the MSE

Figure 7 Average values of S/N Ratio average values for each parameter at different levels
Table 8 S/N Ratio average values at different levels
Process parameters
Levels A B AB C AC BC ABC
1 59,37 61,88 67,90 63,32 77,86 64,66 64,73
2 62,12 59,89 57,94 55,91 47,27 61,82 51,77
3 57,29 57,02 52,96 59,55 53,66 52,31 62,28
Minimum 57,29 57,02 52,96 55,91 47,27 52,31 51,77
Maximum 62,12 61,88 67,90 63,32 77,86 64,66 64,73
Figure 8 Effect classification
11%
10%
10%
10%
10%
9%
9%
4%
4%
3%
3%
3%
2%
2% 2%
2%
2%1%
1%
0% 0%
Contribution of variability
AC2
AB2
A1
ABC2
B2
C2
BC2
C3
AB3
BC3
AC3

Figure 9 Classification of effects after selection
5. RESULTS ANALYSIS
Standardized simulations results are presented in Table 3. From this table, neural networks were
used to calculate MSE, and then Taguchi method was used to observe the impact of different
transfer functions, neuron numbers and error learning functions (MSE) during neural network
model.
Since the main objective is to determine each factor optimal level and to minimize project
average total duration, this implies maximizing S/N ratio [16]. MSE and S/N ratio values for
each parameter at different levels are calculated and recorded in Table 6.
Values given in Table 6 are reported in Figures 6 and 7 which represent the project total
duration average values and S/N ratios for each parameter at different levels. By analyzing Fig.
6 and 7, it is clear that average total duration is at minimum at A3, B3 and C1 parameters levels,
and S/N ratio is at maximum at A2, B1. and C1 parameters same level. As a later S/N ratio
means a better quality characteristic of design process, optimal combination of control factor
levels is therefore determined as A2B1C1.
6. CONCLUSION
Artificial neural network is considered as a reliable approach for prediction in engineering
fields. Withint the same context, in this study, the values of the average total project duration
obtained by simulation are modeled successfully using artificial neural networks. The
predictive model developed has demonstrated the ability to model with satisfactory accuracy
the average total duration of the project. It has been demonstrated by taking into consideration
a number of parameters that consist of the probability of success, the coefficient of
improvement and the learning coefficient. The output variable is expressed as the average total
project duration that can be encountered in the design domain. Once the predicted values of the
mean squared error are calculated, these values are used as the database for the Taguchi method.
Basically, Tagichi method is used to minimize the generated errors from neural network. The
future work resodes in studying of the influence of other parameters on the performance of the
networks of activities and the consideration of other transfer functions.
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
Contribution of variability
Contribution de la
variabilité
Variability
contribution

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