Research On The Prequalification Problem In Contractor Selection Process

Selecting an appropriate contractor is essential for the success of any construction project. Contractor prequalification procedure makes it possible to admit for tendering only competent contractor. Prequalification is a multi-criteria decision problem that is, in essence, largely dependent on the uncertainty and vagueness in the nature of construction projects and subjective judgement of the decision maker. Prequalification is a multi-criteria decision problem that is, in essence, largely dependent on the uncertainty and vagueness in the nature of construction projects and subjective judgement of the decision maker. During the prequalification process the decision maker did not pay much attention on contractors’ credit, but in practice many problems are caused by contractors’ bad credit. So, it is important to evaluate the contractors’ credit level and can be as a reference for prequalification. Because of all this above, this dissertation researched the following aspects:1. There are much subjective and uncertainty in the contractors’ credit evaluation process, because of this the contractor credit evaluation model is proposed based on AHP, TOPSIS and fuzzy set theory. In the model the linguistics variables are used to assess the contractors’ credit and then can be changed to triangle fuzzy numbers, according to the criteria weight calculated by AHP, the weighted fuzzy matrix is formed and it can be a base to construct the subjective and negative ideal solution, then the distance of contractor between the ideal solution can be calculated.2. In the engineering practice, the decision makers usually can’t give the absolutely numbers of criteria and criteria weights, and also have subjective preference of contractors. Because of this above, two models were developed which is based on the basic principle of interval number and grey related analysis to solve this problem. It is exists the preference which the decision-makers have on the contractors, and in the first model the criteria weights is given, but in the second model the criteria weights isn’t known absolutely, both in the two models the grey related numbers between the subjective preference and the objective preference are calculated.3. Besides of the contractor credit evaluation, it is very important to forecast the contractors’ level in order to make up the inadequate of contractor credit assess which is lack of long constraints. So the model based on BP neural network is established to forecast to the contactors’ credit level.4. There is few direct connection between the owner’s objectives and the criteria in the prequalification process, but the objectives are so different, so this dissertation proposed the new idea to choose contractor based on various objectives. Because of the above situation, this dissertation constructed the contractor prequalification models based on fuzzy set theory to calculated the relationship between contractor and objective, and choose five parameters to make the sensitivity analysis.