A fuzzy multi-criteria decision-making model for construction contractor prequalification

doi:10.1016/j.autcon.2012.04.004

Automation in Construction

Volume 25, August 2012, Pages 8-19

https://doi.org/10.1016/j.autcon.2012.04.004 Get rights and content

Abstract

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. This paper presents a systematic prequalification procedure, based on Fuzzy Set Theory, whose main differences and advantages in comparison with other models are the use of an algorithm to handle the inconsistencies in the fuzzy preference relation when pair-wise comparison judgements are used and the use of linguistic assessment or exact assessment of performance of the contractors on qualitative or quantitative criterion, respectively. Finally, a case study for the rehabilitation project of a building at Universidad Politécnica de Cartagena is presented to illustrate the use of the proposed model and to demonstrate its effectiveness.

Highlights

► The success of any construction project depends on the right selection of contractors. ► Contractor prequalification can be regarded as a multi-criteria problem. ► Pair-wise comparison usually involves uncertain or incomplete information. ► We present a fuzzy multi-criteria model for the evaluation of contractors. ► We apply the model to a real rehabilitation project.

Introduction

The complexity and adversity of the current construction industry aggravate the various risks and uncertainties faced by contractors, which influence their ultimate performance levels. The adequate selection of suitable contractors is directly related to construction project success and the achievement of specified objectives, therefore contractor selection constitutes a critical decision for any project manager [1], [2].

Several pre-selection procedures such as open tendering, selective/restricted tendering, prequalification or negotiation are currently practiced for contractor selection [3]. In an open tendering process every contractor can apply and after a bids evaluation process, the optimal bid is awarded the contract. When a construction contract involves special expertise and high technology, the adequate selection process is selective or restricted tendering because only the contractors who fulfil project requirements can apply for this procedure. Prequalification is the process of screening contractors where the capabilities below which contractors will be considered are established. Finally, when the contract is too complex, or there is an emergency situation, or when no application is made for the other mentioned procedures a negotiation procedure is implemented.

Traditionally, one of the most frequently procedures used for selecting contractors has been open tendering where the lowest bidder is awarded the contract [4]. However, the lowest bidder is not always the most economic choice in the long term as the client runs the risk of poor performance by that contractor during the project life. In seeking to minimise the aforesaid risks and failures and to enhance the performance levels of contractors a process to evaluate candidate contractors' ability to complete a contract satisfactorily before they are admitted into the bidding process is often applied [5].

Several specific characteristics of the prequalification problem should be taken into account in order to obtain good results in the application of any prequalification model. These critical characteristics are [6]:

1.
Prequalification is a multi-criteria problem. The proposed model should do analysis of the criteria on a simultaneous basis.
2.
Prequalification contains risks inherited from different decision maker's opinion.
3.
Prequalification includes uncertain date given by different contractors.
4.
Prequalification contain subjective judgement made by decision makers.
5.
Prequalification include nonlinear relationships between contractor's attributes and their corresponding prequalification decisions.
6.
The model should be able to adapt the results to suite changes associated between different contractors.
7.
It should be able to deal with qualitative as well as quantitative.

Section snippets

Contractor prequalification models

Contractor prequalification can be regarded as a multi-criteria problem: potential contractors are measured and judged in accordance with a set of criteria. Many multi-criteria techniques are proposed and applied for this problem solution such as multi-criteria decision making (MCDM) [7], [8], [9], multi-attribute analysis (MAA) [10], [11], multi-attribute utility theory (MAUT) [4], [12], case-based reasoning (CBR) [13], [14], cluster analysis (CA) [15] and graph theory (GT) [16]. However, most

Criteria of contractors’ prequalification

A crucial task in contractor prequalification process is to establish a set of decision criteria through which the capabilities of contractors are measured and judged [25].

For contractor prequalification, a wide variety of criteria have been proposed [4], [10], [22], [23], [26]. Criteria for prequalification may vary between projects since the characteristics of them are distinct although there are some common characteristics of contractor prequalification [27], [28]. All the projects have a

Linguistic variable

A linguistic variable is a variable whose values are words or sentences in a natural language [18]. For example, if a linguistic variable is defined as the performance of a goal-oriented entity, its values can be represented by linguistics terms such as very small, small, medium, large and very large. The values defined for a linguistic variable will obviously depend on the problem context.

In those decision-making problems, in which it is relatively difficult to provide exact numerical values

Research methodology

A decision model for contractor prequalification based on Fuzzy Set Theory is offered in this paper. The model involves a multi-criteria evaluation of contractors and the establishment of a classification of all the feasible contractors. The conceptual stages of contractor prequalification are shown in the model presented in Fig. 2.

At the first stage, a set of criteria for evaluating the potential contactors is established by taking into account the nature of construction project. The

Case study

The project manager of the rehabilitation project of a building at Technical University of Cartagena, shown in Fig. 3, wanted to make a list of contractors able to realize this project. To do this he had to evaluate five contractors (A, B, C, D, E).

According to some contractor selection literature, an in-depth discussion to identify the appropriate contractor prequalification criteria was conducted by experienced project manager team. The most relevant criteria included technical capacity,

Conclusions

The success level of any construction project depends significantly on the selection of an adequate contractor. Contractor prequalification is a widely used process to select responsible and competent contractors to undertake the construction contract and deliver optimal results with minimal failures. Contractor prequalification can be regarded as a multi-criteria decision problem since potential contractors are measured and judged in accordance with a common set of criteria.

Most of the

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A fuzzy multi-criteria decision-making model for construction contractor prequalification

Abstract

Highlights

Introduction

Section snippets

Contractor prequalification models

Criteria of contractors’ prequalification

Linguistic variable

Research methodology

Case study

Conclusions

Automation in Construction

International Journal of Project Management

Building and Environment

Building and Environment

Building and Environment

International Journal of Project Management

Building and Environment

Building and Environment

Automation in Construction

Expert Systems with Applications

Building and Environment

International Journal of Project Management

Information and Control

Information Sciences

Automation in Construction

Building and Environment

Automation in Construction

Automation in Construction

Fuzzy Sets and Systems

Information Sciences