BIDDING
In competitive tendering, bidding is a complex decision making process full
of uncertainties, it contains two successive phases: Bidding and pricing decisionmaking.
Bidding decision is when the contractor through extensive research on engineering
contracting market, extensive collection of bidding information and carefully
selected, determine the process suitable for the company's bidding project (Hu
and Chen, 2008). Pricing decisionmaking is when the contractor decided
to bid, after a series of calculation and evaluation analysis and determine
the quotation process starting from the basic goal is to win the bid and profitable
(Yu, 2009). Bidding quotation includes three aspects:
First, the bid or not. According to the project subject to bidding to decide
whether to bid; second, determine the price. If the bidding and quotation; third,
the bidding strategy and skill. However, reasonable prices play a decisive role
in engineering bidding. Mainly displays in: The bidding is the foundation of
contract negotiation, the bidding decides the project can profit or loss, the
risk of project implementation, the nature of project change, the bidding can
reserve claim opportunity, the damage of bidding in the project practice is
irreversible, the bidding is an important factor in the success of the project
(Cai et al., 2010).
At present, the definition of a widely quoted, the equation is:
Cost estimate price refers to the basis the construction cost of construction enterprise quota estimation.
Kim et al. (2004) Markup refers to the ratio
of project profit and risk premium and accounts for the cost. Profit for owners
is the allowed profit, the bidders are calculated profit. Risk premium for contractors
is an undetermined number, if not all the expected risk occurs, there may be
residual risk premium reserve, this part of the surplus and profit plan together
is the surplus, if the risk premium underestimated, the profits are used to
subsidize. In the bidding, risk premium is closely linked for the contractor
whether win the bid and the profit level after the bid, therefore, determine
the markup scientifically is an important decision of the contractor:
ANALYSIS OF FACTORS INFLUENCING MARKUP
Factors influencing markup is divided into three categories according to the
characteristics of the project submitted to the markup: Environmental factors,
factors of enterprises and factors of project, these three factors can be subdivided
into 20 factors (Setyawati et al., 2002) (Fig.
1).
In the above influencing factors, some factors are quantifiable factors, such
as the demand for funds, duration, management fees and the rate of return but
some of these factors is difficult to quantify, such as location, quality of
workers (He et al., 2012). Using the rating
score way to quantify those elements that are not easily quantifiable, as shown
in Table 1.

Fig. 1: 
Factors influencing markup 
Table 1: 
Index of factors 

GENETIC ALGORITHMBP NETWORK DESIGN
Genetic algorithm is a global optimization algorithm based on Optimization
but there is insufficient local optimization problem (Luo,
2005). In the early stage of searching for the optimal solution in the solution
space based on the genetic algorithm, the speed of convergence is fast. But
when it is close to the optimal solution, the solution would not converge for
long due to the stochastic crossover operation and there is error bisect (Zhou
et al., 2012). While the speed of convergence of searching for the
optimal solution in the solution space is slow based on BP network, when it
is close to the optimal solution, because gradient descent method is used and
there is a direction, the local optimal searching is more efficient than genetic
algorithm. Therefore, the combination of the genetic algorithm and BP network
is meaningful. Not only can play the generalization ability of neural network
and it converges very fast and has a strong learning ability. There are two
ways of genetic algorithm and network, one is used for network training, meaning
to learn the connection weights between network layer, the other is to learn
the topological structure of network. It is the former that the genetic network
is applied which optimize network connection weights by genetic algorithm.
Genetic algorithm design:
• 
Initial population: The original population size is
set to 60. The experiments show that we can not only guarantee the search
efficiency but also achieve global optimization search 
• 
Encoding: A real number coding scheme is selected that each connection
weights is directly represented by a real number. The advantages of real
number coding scheme is that it is very intuitive and does not appear the
lack of precision 
• 
Fitness function: One important performance of BP network is the
error sum of square between network's output value and desired output. The
smaller the error sum of square is, the better the network performance is.
So fitness function can be defined as: 

In which the denominator is the error sum of square between
network's output value and desired output 
• 
Genetic operator: The genetic algorithm consists of
three basic operators: Selection, crossover and mutation 
BP neural network structure design:
• 
Network layers: Choose a threelayers BP network based
on Kolmogrov theory 
• 
No. of input layer nodes: Due to the correspondence of the number
of input layer nodes and markup influence factors, the input node number
is determined to be 20 
• 
No. of output layer nodes: The output result is required to be
the markup of proposed tender, in which the number of output layer nodes
is 1 
• 
No. of hidden layer nodes: Through experimental methods, considering
the network training speed and generalization ability, the number of hidden
layer nodes is 14 
• 
Transmission function: The transmission function of the hidden
layer nodes should be set to the igmoid type. The activation function of
the outputlayer node should be set to linear activation function BP network
structure is shown in Fig. 2 

Fig. 2: 
BP network structure 
ESTABLISHMENT AND IMPLEMENTATION OF MODEL
Implementation of software environment: According to the characteristics
and scale of the mode, MATLAB is selected as the software environment of implementation
of the model. The genetic algorithm toolbox (GOAT) was developed in the MATLAB
environment by Christopher R. Houck, Jeffery A. Joines and Michael G. Kay of
North Carolina State University, based on genetic algorithm theory. A threelayers
genetic BP neural network model can be constructed only by calling several simple
initialization statements in MATLAB. Programming ideas are shown in Fig.
3.
Model checking: Collecting various factors of thirty past successful tender offer programs from an international contracting company and quantify the factors with the quantitative method raised in the Table 1. Quantitative results are shown in Table 2 and 3. Thirty training samples quantized from the programs are divided into two groups: 25 for training artificial neural network and 5 used to verify the artificial neural networks.
Based on the geneticBP neural network program designed in MATLAB environment, the above samples are trained, simulated and tested. Simulation process and simulation results are as follows:
Table 4 is the simulation results from the neural network trained by genetic neural network algorithm. Analyzing from the error of the results, the error is in line with the requirements.

Fig. 3: 
Programming ideas 
Table 4: 
Simulation results (%) 


Fig. 4: 
Operation trajectory genetic algorithm 
Figure 4 is the computation trajectory of the part of genetic algorithm in the genetic neural network algorithm. From the figure we can see, the fitness value of chromosome after the 80th generations is basically stable at about 0.5.
Figure 5 is the secondary training process of the neural network in genetic neural network algorithm. The training process indicates that the sample is only trained 7 steps to achieve the accuracy requirements.
CONCLUSION
On the basis of the constitution of engineering project bidding, the key of bidding process is to determine the makeup and analysis the influencing factors of makeup. According to the complexity, uncertainty and other characteristics of the decision of engineering project bidding, the idea that combining advantage with the BP neural network and genetic algorithm is raised to establish the makeup decisionmaking model of geneticBP neural network. From the view of the implementation of the model, good results has achieved in the aspects of decisionmaking accuracy rate, the reasoning speed and selflearning of decisionmaking system.