CONTRACTOR SELECTION OF CONSTRUCTION IN A COMPETITIVE ENVIRONMENT 1Edmundas

Contractor selection is a vital part of the project management cycle and deals with risk and risk management. This paper presents contractors’ assessment and selection based on the multi-attribute methods in a competitive and risky environment. The model is based on a multi-attribute evaluation of contractors, the determination of their optimality criterion values according to Hodges-Lehmann rule. The proposed model could be applied to assessment of construction operations. The attributes of contractor evaluation are selected taking into consideration the interests and goals of the stakeholders as well as factors that infl uence the process of construction effi ciency. The model is based on metric scores. A background and a description of the proposed model are provided and a few key fi ndings from the data analyses are presented.


Introduction
The effi ciency of construction process is often associated with the successful choice of a contractor. Various contractors' selection procedures are applied in practice. A single attribute cannot give a full expression of goals purposed by various stakeholders. Contractors choice was analyzed by Skitmore (1989), Hatush and Skitmore (1998), Olson (1998), Fong and Choi (2000), Andruškevičius (2005), Cheng et al. (2006), Turskis (2008). The importance of contractor selection is mostly underestimated and neglected in construction (Kumaraswamy and Matthews 2000;Ng and Wan 2005). It is hard to analyze many trade-offs involved in decision making, especially in times with so many uncertainties presented by environmental considerations. Insuffi cient time for execution, complicated procedures or poor information channels may be the reasons of problems in the selection of contractors. Contractor evaluation has been recognized as a particularly complex task due to its ambiguity and diffi cult formalization (Albino and Garavelli 1998;Schieg 2007). It is usually based on intuition and past experience and carried out by the general contractor management (Albino and Garavelli 1998;Luu and Sher 2006). There have been no generalized sets of rules for the evaluation process. The importance of non-price factors is well recognized in the literature. Various scientists offer different models for a contractor's evaluation. Multi-attribute decision aid provides several powerful and effective tools (Triantaphyllou 2000;Figueira et al. 2005;Peldschus and Zavadskas 2005;Antuchevičienė et al. 2006;Kaklauskas et al. 2006;Kaplinski and Janusz 2006;Mitkus and Trinkūnienė 2006;Su et al. 2006;Turskis et al. 2006;Zagorskas and Turskis 2006;Ginevičius and Podvezko 2007;Kaklauskas et al. 2007;Viteikiene and Zavadskas 2007;Banaitiene et al. 2008;Ginevičius 2008; for confronting sorting problems. In Turkey, a twostage procedure is used, but at the end, the lowest price determines the selection (Topcu 2004). In Lithuania, the ''lowest bidder'' is selected as in Canada and the USA . Hence, it may be concluded that price attribute is decisive in contractor selection. Lately the ''lowest bid'' selection practice has been criticized because it involves high-risk exposure of the client. The selection based on the low price basis can be one of the reasons for project completion delays, poor quality and/or fi nancial losses, etc. Hatush and Skitmore (1998), Topcu (2004) stated, that in seeking to minimize risk, the pre-qualifi cation procedure is often chosen. Topcu (2004) proposed a multi-attribute decision model based on time, price and quality attributes evaluation for eligible contractor selection. Hatush and Skitmore (1998) suggested determining the client's needs and aims of a particular project to set the proper contractor evaluation attributes. The proposed attributes involve price, time, quality parameters, uncertainty level, fl exibility to make changes, the allocation of risks and the ability of a contractor to cope with the levels of complexity that are involved. Hatush and Skitmore (1998) proposed the application of the multi-attribute utility theory for contractor selection. By applying an additive model, they compared four contractors against different attributes.

Methodology
The problems of rational construction variants selection are solved under various conditions, which are characterized by many effi ciency attributes (Ginevičius et al. 2007;Zavadskas et al. 2007;Banaitiene et al. 2008;. The attributes for each of the variants being compared (projects, strategies, alternatives) are calculated or set by means of experiments, then upon assessing environmental conditions, these attributes are characterized by the information available. Decisions may be made under totally defi nite conditions (for a determined problem), upon evaluating one or several effi ciency attributes.
All these procedures are aimed at selecting a qualifi ed contractor on a competitive basis, but in reality a decision is usually based on a single criterion (Hatush and Skitmore 1998). Siskos et al. (2000) described their methodological approach based on the principles of multi-attribute modelling and the application of the original preference disaggregation method as used in MUSA (Multi-criteria Satisfaction Analysis) for data analysis and interpretation.
The contractor pre-qualifi cation process involves the establishment of a standard for measuring and assessing the capabilities of potential contractors (Ng et al. 1999). Jaselskis and Russel (1992), Crowley and Hancher (1995), Russel (1996), Kumaraswamy (1996) have identifi ed commonly used attribute for prequalifi cation and bid evaluation and have proposed methodologies for contractor selection. Zavadskas and Kaklauskas (1996) selected 25 attributes for contractor selection and applied COPRAS method. Hatush and Skitmore (1998) have initiated the use of systematic multi-attribute decision analysis techniques for contractor selection and bid evaluation based on additive multi-attribute utility function model. Banaitienė et al. (2008) performed an analysis of criteria for contractors' evaluation. Dikmen et al. (2007) after conducting a thorough research, selected 44 candidate factors affecting the bid mark-up decisions as factors having potential impact on bid mark-up size for a project.
Every problem to be solved is represented by a matrix, which contains variants (rows) and attributes (columns). The variants represent a set of situations for a problem that really exists. All variants considered are evaluated using the same attributes. The evaluation results are put in a matrix Usually the attributes have different dimensions. That is why their effectiveness cannot be compared directly. An exception is the application of evaluation numbers without any dimensions according to a points system. This, however, involves subjective infl uences to a great extent. Hence, it should only be used in exceptional cases. In order to avoid the diffi culties due to different dimensions of the attributes, the ratio of the optimal value is used (Figueira et al. 2005;Zavadskas and Vilutienė 2006;Ginevičius and Podvezko 2007;Ginevičius 2008.). In this way the discrepancy between different dimensions of optimal values is also eliminated. There are various theories about the ratio of the optimal value. Note that the decision for a theory may affect the solution. However, the values are mapped either on the interval [0; 1] or on the interval [0, infi nity) by the transformation. Only those well-known theories of transformation are used that are appropriate for both problems of maximisation and minimization.
The linear normalization uses a scale of the existing values Weitendorf (1976). The calculated values are dependent on the size of the interval and thus change if the interval is altered. (1) The calculation of the relative deviation is a well performing linear transformation.

Hodges-Lehmann rule.
With this rule (Hodges and Lehmann 1952) confi dence in the knowledge of the probabilities of the strategies of the opponent can be expressed by the parameter λ: (3) K i -optimality criterion; λ -risk assessment factor; q i -attribute weight.

Model of the contractor selection
The model is described by discrete values: construction time, quality of performed projects, bid estimates, communication level with stakeholders, and capacity infl uence based on the different stakeholders sectors risks and uncertainties. The model of problem is presented in Fig. 1. The subject of investigation is a ninestoreyed administration and business complex building in Vilnius city. Each contractor is described by 8 at-tributes. Attributes and their weights were determined on the basis of performed questionnaires. The attributes of contractor selection are as follows: x 1 -bid estimates [million €]. For the contractor, a bid estimate submitted to the stakeholder either for competitive bidding or negotiation consists of direct construction cost including fi eld supervision, plus a mark-up to cover general overhead and profi ts. The direct cost of construction for bid estimates is usually derived from a combination of the following approaches: • Subcontractor quotations; • Quantity take-offs; • Construction procedures. x 2 -construction duration [months]. Most contracts are quite specifi c regarding the amount of construction time allowed to complete the work, and many provide for the payment of "liquidated damages" by the contractor to the owner for failure to complete on time or, in some cases, to complete portions of the work that interface with other contract schedules where multiple prime contracts have been executed.
The work covered in a construction contract includes a stated guarantee period. The contractors according to valid regulations and rules must give construction works certain guarantees:

guarantee period for screen works [year], must be not less than 10 years. x 4 -guarantee period for fi nishing works [year],
must be not less than 5 years. The contractor is responsible for the quality workmanship, the quality of the materials used, and for performance of the contract only. x 5 -experience of fi rm in construction [year]. This attribute assesses contractor's activity in construction sector. x 6 -total amount of works performed by contractor [rate], the contractor must at the few, like a fi fty percent work fi ll theirs intensity.

communication level with stakeholders [point],
is very important all through aconstruction period and after fi nishing construction work. The algorithm for the ranking of alternatives by applying Hodges-Lehmann rule is shown in Fig. 2. In order to establish the indicators importance, a survey has been carried out and 20 experts have been questioned. The experts, basing their answers on their knowledge, experience and intuition, had to rate indicators of effectiveness starting with the most important ones. The rating was done on a scale from 1 to 8, where 8 meant "very important" and 1 "not important at all". The importance of indicators was established according to the rating methods  of these experts and also demonstrated the priorities of the user (stakeholder).
If we scrutinize initial decision-making matrix (Table 1) we can fi nd that no one alternative has all optimal attribute values. The best price is in alternative 7, the shortest construction duration is in alternative 9, and so on.
In Table 2 weighting normalized decision-making matrix is presented. The results of the assessment are presented in Table 3. According to the solution results  it is clear, that according to all risk levels the best alternative is the 15 th alternative, with one exception (risk level 0.00). When there is no risk the best alternative is the 1 st alternative. In very risky environment and situation the risk level equals to 1.00. In this case the best alternative is the 9 th alternative.
The best 15 th alternative was selected according to the calculated optimality criterion values at different risk level.

Conclusions
In construction process an important role is the best contractor and subcontractor selection.
Traditional selection of contractors such as choosing those with whom the stakeholder had already done business can lead to ineffi ciencies in projects and poor project performance.
A contractor's assessment and selection always deals with risk and a single attribute -price can be used in certain cases only.
In competitive and risky environment contractor selection must be performed according to multiple attributes.
The application of the model offered at this paper may reduce the risk involved in the selection of a contractor and can lead to the elimination of unqualifi ed contractors during the bidding process.
The selection of contractor can be with different risk level. Hodges-Lehmann rule allows stakeholders to select contractor taking into account different risk levels.
Knowing the risk level stakeholders can effectively manage the risk.
This model can be applied to select alternatives in construction under risky environment.