Application Of Analytic Hierarchy Process And Artificial Neural Network In Bid Decision Making

Bhagyashree et al., International Journal of Advanced Research in Innovative Discoveries in Engineering and Applications[IJARIDEA]
Vol.2, Issue 2,27 April 2017, pg. 9-14
9
© 2017, IJARIDEA All Rights Reserved
Application Of Analytic Hierarchy Process And
Artificial Neural Network In Bid Decision Making
Bhagyashree1
,B Raghavendhra K Holla2
, Kiran Kumar Shetty M3
, A S Vasudev Rao4
1
Assistant Professor, Department of Civil Engineering,
Manipal Institute of Technology, Manipal University, Manipal, India
2
Assistant Professor(Sr. scale), Department of Civil Engineering,
Manipal Institute of Technology, Manipal University, Manipal, India
3
Professor,Department of Civil Engineering, Manipal Institute of Technology,
Manipal University, Manipal University, Manipal, India
4
Sr.Professor,TAPMI, Manipal University , Manipal
Abstract— An appropriate decision to bid initiates all bid preparation steps. Selective bidding will reduce the
number of proposals to be submitted by the contractor and saves tender preparation time which can be
utilized for refining the estimated cost. Usually in industrial engineering applications final decision will be
based on the evaluation of many alternatives. This will be a very difficult problem when the criteria are
expressed in different units or the pertinent data are not easily quantifiable. This paper emphasizes on the
use of Analytic Hierarchy Process(AHP) for analyzing the risk degree of each factor, so that decision the can
be taken accordingly in deciding an appropriate bid.AHP helps to decide the best solution from various
selection criteria.The study also focuses on suggesting a much broader applicability of AHP and ANN
techniques on decisions of bidding.
Keywords— Analytic Hierarchy Process(AHP), Artificial Neural Network(ANN), Consistency Index(CI),
Consistency Ratio(CR), Random Index(RI), Risk degree.
I. INTRODUCTION
Contractors in the construction industry earncontracts either through direct negotiation or
competitive bidding. Usually competitive bidding is employed, wherethe main award criteria
are technicalability and lowest bid price. In the present competitive construction industry
scenario, the important decision that has to be made by any contractor who is competing in
the market is to decide on-which price to bid for when a serious invitation is received.
Moreover, this decision will require simultaneous assessment of large number of alternative
factors. The decision to bid/no bid requires an understanding of factors affecting the decision
[1].
Various factors affecting bid decision will be discovered and then analyzed to investigate
theirrelativeinfluence and significance. A questionnaire survey is carried out to recognize the
importance and interrelationship among thefactors and analyse them using AHP and rank the
factors. AHP creates a hierarchy or ranking of all the decision parameters bycomparing each
pair of items which will be depicted as a matrix. Paired comparisons incorporate weighted
scores that can gauge the significance of parameters. The aspiration is to examine how bid/no
bid decisions are accessed by various strategies of contractors.The purpose of applying AHP
and ANNis to optimize decision making, when a contractor is confronted with a fusion of
qualitative, quantitative and conflicting factors under consideration [2].
II. LITERATURE REVIEW
An easy way to comply with the conference paper formatting requirements is to use this
document as a template and simply type your text into it. Alexander et al. (2012) [2] studied
decision-making using the Analytic Hierarchy Process (AHP). AHP makes use of the
ingenuity of decision makers to form a disintegration of the problems into hierarchies. For
decision alternatives, the hierarchy can be employed to formulate ratio-scaled measures and a

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relative value which the alternate parameters have against the goals of the organization and
project overall risks. To sort out the factors, AHP uses matrix algebra to get a mathematically
optimal solution.
In AHP, preference from 1 to 9 was used to form the comparison matrices. The scale of
preference for comparison of pairs of two parameters will range from a highest value of 9 to
1/9 (0.111 in decimal form). The measure of consistency was given by the largest Eigen
value. After forming the Comparison matrix, AHP computes an eigenvector which denotes
the relative ranking of significance. An example of up gradation of smart phones was
discussed in the study. AHP was applied for final selection of mobile phones for which three
smart phones were dealt with. Cost, display resolution, battery life and internal storage were
the four factors examined. AHP was utilized to choose the best smart phone based on the
attributes desired.
Triantaphyllouet al. (1995) [3]carried out a research on challenges in application of
Analytic Hierarchy Process for Decision Making in Engineering Applications. In most of the
engineering problems the terminal decision depends upon the examination of a set of
substitutes with various decision parameters. This is a challenging task; henceforth Analytic
Hierarchy Process bestows an efficient way for quantifying the pertinent data. In this research,
the three alternative computer systems were examined in terms of decision parameters such
as hardware expandability, hardware maintainability, financing available, and user friendly
characteristics of the operating system. The AHP deals with solving complex Multi Criteria
Decision Making (MCDM) problems in engineering. This paper suggests that taking the
recommendations made by the AHP literally should be avoided since closer the final priority
values are with each other, the more carefulness is required by the user. The observations in
this research conclude that MCDM methods should be used only as decision support toolsbut
not for extracting the last solution.
III. RESEARCH METHODOLOGY
The findings from the review of the previous investigationled the identification of the most
important factors which affects bidding process. A list of major factors affecting the bidding
process was prepared by consulting the experts for their opinions. The same has been used
during the development of the model.
The questionnaire pattern contained two sections. The first section involved the experts’
opinion and the most important 42 factors were finalized which sheds light on real
competitive bidding situations. The developed questions were categorized with a scale from 0
to 6 where 0 represented“extremely low” and 6 represented “extremely high”, the companies
were asked to reply for ‘bid/no bid’ situations as well as to decide percentage mark up. The
second section involved experts’opinions for the factors that were not accounted for and
which should have been incorporatedfor the development of construction industry. The 42
factors were grouped under 13 groups by conducting exploratory factor analysis in SPSS
software [4].Then it was analyzed in Microsoft Office Excel for AHP.
The detailed procedure of AHP is given below:
1. Mean of all the responses (sample size 47) was taken. It was used for development of
matrices.
2. Pair wise comparison was conducted for these means.
3. Based on the type of grouped factors, the 13 major groups were named under different
heads (grouped according to factor analysis in SPSS software). These constitute the
judgment matrix decision variables.
4. Pair wise comparison matrix for each criterion (i.e. 13*13 matrix) was developed.

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5. Similarly comparison matrices for various factors under 13 groups were developed.
First group consisted of 6 factors (6*6 matrix); second group consisted of 6 factors
(6*6 matrix); third group consisted of 5 factors (5*5 matrix) and so on.
6. Finally the matrices are evaluated and checked for the consistency of judgments.
Probability was attached to each of the factors according to their importance and risk
degree for all the 42 factors was calculated and they were ranked according to the
increasing values.
Risk =Pij*Wij
Where Pij is the probability attached to a factor. Wij is the influence to the overall target of
project . Also, Project overall risk =Σ (Pij*Wij)
7. A graph of risk in degree Vs risk factor was plotted and then all the factors were
ranked according to the hierarchy of their risk degree.
The procedure for development of matrices is as enumerated below:
Development of a pairwise comparison matrix for each criterion:
a) Here the criteria in the row are being compared to the criteria in the column.
b) Normalization of the resulting matrix: Next step is to normalize the matrix by totaling
the numbers in each column.Each entry in the column is divided by the column sum
to get its normalized score. The sum of each column should be 1.
c) Average of the values obtained in each row is taken to get the corresponding rating.
d) Lastlythe consistency ratio is to be calculated and verified.
The intention of carrying outconsistency analysis is to ensure whether the original
preference ratings are consistent enough. The steps to arrive at the consistency ratio are:
a) Consistency Index (CI) can be calculated using the belowshown formula
CI=(λmax–n)/(n–1) (1)
b) Calculation of Consistency Ratio can be done using the formula given below (where RI
indicates random index)
CR=CI/RI (2)
For the calculation of consistency measure, the help of Excel’s matrix multiplication
function =MMULT () can be adopted. The weighted average for every decision alternative
should be calculated. Finally the one which has the highest score will be chosen. Usually,
Consistency Ratio of 0.1 or below is treated as acceptable value [5] [6].
A. Findings of AHP:
a) With the help of AHP, the degree of consistency can be measured and if it is found
unacceptable, pair wise comparisons can be revised.
b) ConsistencyIndices and the consistency ratiowill be zero which indicates perfect
consistency, since the consistency measures will equal n.

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c) If consistency ratio is very huge (> 0.10), then it is not consistent enough (error more
than 10% is not acceptable) and hence the comparisons need to be revised.
B. Data analysis using Artificial Neural Network:
For creating a supervised neural network model, initially the input and the target data has to
be finalized. From the total responses obtained from the construction organizations, 70% of
the responses were selected randomly to train the network. The remaining 30% of the
responses were kept for testing. For fixing the target data the losing bid strategy was
considered as zero and winning bid strategy was considered as 1.
The value for the target data was computed based on the weighted responses obtained. To
decide the values for the target data, the obtained responses were multiplied with the weights
obtained from AHP analysis. Then the sum of the weighted responses of each respondent was
calculated. The mean and standard deviations were obtained for all such weighted responses.
Further the response from each respondent was compared with the range of values (mean -
standard deviation) to (mean + standard deviation). The sum of any responses well within this
range was considered as winning strategy and assigned with the value ‘1’ otherwise ‘0’.
The feed-forward back propagation learning algorithm was used as network type, TRAINLM
as training function, MSE as performance function and LEARNGDM as learning function.
Two numbers of layers were used for the network. A TANSIG transfer function is used for
different numbers of neurons at an increment of 5 ranging from 5 to 50. A fixed number of
training iterations (say 10000 epochs) was used for each stage of training. The iterations are
continued till network attains minimum gradient.
The comparison of actual bid decisions with the values got from analysis using ANN tool
was done. The analysis is done in three separate stages with 10000, 1 lakh and 10 lakh epochs.
Each stage is subdivided into 10 different number of neurons from 5 to 50 neurons.
Later the results were also verified for 55 to 100 neurons (for 1000 epochs, 10000 epochs,
100000 epochs, 1000000 epochs, 10000000 epochs) to match the target value. Then for
verification, analysis was carried out by taking 45 companies responses for training and 1
company’s response for testing (1 test for winning target and another for losing target) in
ANN. Test was conducted for 10000 to 1000000 epochs with 5 neuron to 50 neurons.
IV. RESULTS AND DISCUSSIONS
Risk degree for all the factors and project overall risk is calculated. Then risk degree Vs risk
factor graph is plotted to visualize the variation of various factors affecting bid / no bid
decision. According to study conducted in this paper the factor “How important are
subcontractors for a given project?” has got the highest risk (risk degree=0.047) and “What
is the importance of site clearance?” has got least risk(risk degree=0.003). Henceforth it helps
in deciding for bid of a particular project by evaluating all the factors.
A. Network training with 10000 epochs:
The network is showing best result of 100% when it is simulated by input of 32 companies
with randomly selected 14 companies tested for 10 to 50 numbers of neurons used in training
process. Here when the result got from ANN network is compared with the actual results, 14
out of 14 results were true. Similarly analysis was carried out for 100000 epochs and
1000000 epochs and the target value was reached. [7] proposed a principle in which the
division is the urgent stage in iris acknowledgment. We have utilized the worldwide limit an
incentive for division. In the above calculation we have not considered the eyelid and

13
eyelashes relics, which corrupt the execution of iris acknowledgment framework. The
framework gives sufficient execution likewise the outcomes are attractive. Assist
advancement of this technique is under way and the outcomes will be accounted for sooner
rather than later. Based on the reasonable peculiarity of the iris designs we can anticipate that
iris acknowledgment framework will turn into the main innovation in personality
verification.In this paper, iris acknowledgment calculation is depicted. As innovation
advances and data and scholarly properties are needed by numerous unapproved work force.
Therefore numerous associations have being scanning routes for more secure confirmation
strategies for the client get to. The framework steps are catching iris designs; deciding the
area of iris limits; changing over the iris limit to the binarized picture; The framework has
been actualized and tried utilizing dataset of number of tests of iris information with various
complexity quality.
Also the network is showing best result of 100% when it is simulated by input of 45
companies with randomly selected 1 company tested for 5 to 50 numbers of neurons used in
training process.Results of ANN with varying number of neurons and epochs are shown
below.
TABLE I
RESULTS OF ANN WITH VARYING NUMBER OF NEURONS AND EPOCHS
V. CONCLUSION AND FUTURE SCOPE OF WORK
• The potential of the Fuzzy logic (AHP) and Artificial Neural Networks (ANN) on
predicting “bid/No bid” was examined in this study. Several ANN inputs, structures and
training possibilities were assessed. The results of the ANN models emphasize the usefulness
of ANN application in the prediction of bidding situation.
Neurons
Epochs
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Percentage of predicting accuracy of models(%)
1000 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
10000 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
100000 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
1000000 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
10000000 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

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• Also, the weighted responses computed using risk degree obtained from AHP helped
in analysis of ANN to distinguish various factors judicially which aids in decision making
process.
• From the study carried out, it can be concluded that the neural network model
developed for predicting the strategy to decide bid/no bid shows the degree of accuracy up to
92.85 % in reaching target value.
• The validity of the model was verified with the collected data. However, the success
rate of winning the contract by right decision making has to be verified in real time situations
• Further, the study can be improvised by using other soft computing techniques like
Genetic Algorithm (GA), Neuro-Fuzzy Techniques (NFT), k-Nearest Neighbor (k-NN),
Locally Informative k-NN (LI-KNN), Globally Informative k-NN (GI-KNN), Particle Swarm
Optimization (PSO) and Polynomial Neural Network (PNN).
REFERENCES
[1] Vignesh Shenoy B, “Bid Decision Making Using Artificial Neural Network”, an unpublishedthesis
submitted to Manipal University, 2014.
[2] Melvin Alexander, “Decision-Making using the Analytic Hierarchy Process (AHP) and SAS/IML®”,
2012
[3] Evangelos Triantaphyllouet al., “Using the Analytic Hierarchy Process for Decision Making in
Engineering Applications: Some Challenges”, 1995
[4] Professor Andy P Field,Discovering Statistics Using Spss, Chapter 17: “Exploratory factor analysis”,
Discovering Statistics Using Spss , pp 1-36.
[5] Saaty, T.L.,Vargas, L.G. ,Prediction, Projection and Forecasting. Kluwer Academic Publishers, Dordrecht,
1991, pp. 251, 1991.
[6] Khwanruthai Bunruamkaew (D3), “How to do AHP analysis in Excel”,Division of Spatial Information
Science, Graduate School of Life and Environmental Sciences, University of Tsukuba, March 1st, 2012.
[7] Christo Ananth,"Iris Recognition Using Active Contours",International Journal of Advanced Research in
Innovative Discoveries in Engineering and Applications[IJARIDEA],Volume 2,Issue 1,February
2017,pp:27-32.
[8] Jamshid Parvar, David Lowe, Margaret Emsley, and Roy Duff., “Neural networks as a decision support
system for the decision to bid process”.

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