# Bidding decision making for construction company using a multi-criteria prospect model/Statybos imones apsisprendimas dalyvauti konkurse naudojant daugiakriterini perspektyvu modeli.

1. IntroductionIn the construction industry contractors typically earn construction contracts through either direct negotiation or competitive bidding. Government agencies and private sector clients most often employ competitive bidding, which commonly use the lowest bid price as the main award criterion. Usually the bid price includes cost of construction and a markup, the scale of which is typically determined using a percentage of construction costs. Size of markup impacts upon the profit, which serves as the primary motivator for a contractor to win and execute a contract (Dikmen et al. 2007). Research in the area of competitive bidding strategy models has been conducted since the 1950s (Friedman 1956). Numerous models have been developed, some of which were designed specifically for the construction industry. Despite the number of competitive bidding strategy models that have been developed, few of these are used in practice, largely as they do not address the practical needs of construction contractors (Hegazy and Moselhi 1995; Shash 1995). Therefore, there is a perceived need for models designed in line with actual construction contractor practices. In the bid process, once a determination is made to bid, the next step is to select an appropriate markup (Egemen and Mohamed 2008). A successful contractor is the one that selects the most optimal bid markup that secures both the contract and contract profitability (Shash and Abdul-Hadi 1992). Bid markup decisions currently follow no accepted standards or formal procedures, but rather consider contractor experience, intuition, and personal preferences, which are not conducive elements for building an effective approach for achieving the optimal bid markup (Chua and Li 2000).

Cumulative prospect theory was proposed by Tversky and Kahneman (1992). Different from the classical theory, CPT adopted a concave-shaped utility function (UF) for gains and convex for losses and an inverse S-shaped probability weighting function (PWF) to describe individual preferences for choosing between risky prospects. Wakker and Deneffe (1996) proposed a tradeoff (TO) method to improve probability distortions and misconceptions in utility elicitation. Many studies (Wu and Gonzalez 1996; Gonzalez and Wu 1999) have worked to elicit the PWF for particular subjects. Abdellaoui (2000) used TO method concepts to propose a parameter-free method to elicited subjects' UF and PWF. Bleichrodt and Pinto (2000) also leveraged the concept to propose a parameter-free method somewhat different from Abdellaoui's study, which they applied successfully to medical decision making. Determining the relative weight of influencing factors is important in multi-criteria decision making (MCDM). For uncertain events, the decision maker will find it difficult to form a judgment by relying on exact numerical values. FPR is a useful tool to express decision maker's uncertain preference information and define the relative weight of influencing factors. Significant attention has been given to fuzzy preference relations in previously studies (Orlovsky 1978; Nurmi 1981; Tanino 1984; Kacprzyk 1986; Chiclana et al. 1998, 2001, 2003; Fan et al. 2002; Xu and Da 2002, 2005; Herrera-Viedma et al. 2004). Wang and Chang (2007) adopted FPR to forecast the probability of successful knowledge management.

The usual practice is to make bid decisions based on 'intuition', which can be described as a derivation of 'gut feelings', experience and guesswork (Ahmad 1990). This research combined FPR, CPT and MCDM to propose a Multi-Criteria Prospect Model for Bid Decision making (BD-MCPM) to help construction company decision makers derive optimal bid decisions. The proposed model incorporates three phases. Phase I identifies the factors that affect bidding decisions (i.e., bid/no bid and markup scale). Phase II introduces FPR to determine bid/no bid. Phase III uses FPR and CPT to calculate CPT values for given markup scale, then selects the markup scale with the highest CPT value.

2. Literature review

2.1. Currently available decision making models

Contractors currently make bidding decisions using several relevant models. Early mark-up scale estimation models (e.g., Friedman 1956; Gates 1967; Carr 1982) employed probability theory to predict the probability of winning a particular contract. However, as the bidding decision is a complex decision-making process affected by numerous factors, probability theory is unable to describe interactions between factors.

Researchers have recently introduced bidding decision support systems based on artificial intelligence (AI), which permit consideration of identified factors of importance. Such systems include the expert system (ES) (Ahmad and Minkarah 1988; Tavakoli and Utomo 1989), case-based reasoning (CBR) (Chua et al. 2001), neural network (NN) (Li 1996; Moselhi et al. 1993; Hegazy and Moselhi 1994; Dias and Weerasinghe 1996; Li and Love 1999; Li et al. 1999; Wanous et al. 2003), analytical hierarchy process (AHP) (Seydel and Olson 1990; Cagno et al. 2001), and fuzzy set theory (Eldukair 1990; Fayek 1998; Lai et al. 2002; Lin and Chen 2004). The ES is one of rule-based systems. The process of bid decisions are highly unstructured, uncertainty, and subjectivity. It's too complicated to creating a set of clear rules that would be suitable for all/most cases. CBR requires a reasonably large set of cases data from which to draw knowledge to avoid generating inaccurate results. NN, also called artificial neural network (ANN), is similar to the CBR, with an important exception that the inference process is concealed from the decision maker. For such reasons, NN-derived conclusions are sometimes not particularly convincing to decision makers. AHP is a decision-making approach that structures multiple-choice criteria into a hierarchy and assesses relative importance of each. Unfortunately, AHP employs a complicated process to obtain consistent assessment results, which makes it unwieldy in practice.

The complexity and hard-to-define nature of competitive situations necessitates that most bid decisions rely heavily on decision maker intuition, experience and guesswork (Ahmad 1990). Fuzzy set theory provides a useful tool to handle decisions in which phenomena are imprecise and vague. Eldukair (1990) integrated fuzzy set theory with a multi-criteria model to select bidding cases. Subsequently, Fayek (1998) and Lai et al. (2002) used fuzzy set theory to choose optimal mark-up scales. Lin and Chen (2004) proposed an approach using fuzzy set theory to obtain a linguistics suggestion result for a bid/no-bid selection.

Fuzzy preference relations (FPRs), which integrate fuzzy logic and AHP concepts, greatly improve on AHP in terms of relative weight evaluation. In BD-MCPM, the FPR is used to determine the relative weights of influencing factors, and the CPT is used to evaluate the PDM's preference. The BD-MCPM handles factors marked by relatively higher levels of vagueness to make complicated bidding decisions and determine PDM risk preference. Results conform to actual bid decisions generated based on decision maker intuition, experience and guesswork. Therefore, BD-MCPM can assist decision makers to identify projects with the greatest profit potential and set an optimal mark-up scale.

2.2. Fuzzy preference relations

Most decision processes are based on preference relations (PR), the most common representation of information in decision making. In PR, an expert assigns a value to each pair of alternatives that reflects the degree of preference for the first alternative over the second. Many important decision models have been developed using mainly two preference relation types: (1) Multiplicative Preference Relations (MPR) and (2) Fuzzy Preference Relations (FPR).

Most decision processes are based on preference relations, the most common representation of information in decision making. An expert assigns a value to each pair of alternatives that reflects degree of preference B as an alternative over others. Many important decision models have been developed using mainly: (1) multiplicative preference relations and (2) fuzzy preference relations (Herrera-Viedma et al. 2004).

A multiplicative preference relation on a set of alternatives X is represented by matrix A, with A usually assumed a multiplicative reciprocal:

A = [[a.sub.ij] [subset] X x X; (1)

[a.sub.ij] x [a.sub.ji] = 1 [for all]i, j [member of] {1, ..., n}. (2)

The [a.sub.ij] indicate the preference ratio of alternative [x.sub.i] to [x.sub.j]. Saaty (1980, 1994) suggested measuring [a.sub.ij] using a ratio scale 1-9. When [a.sub.ij] = 1 indicates indifference between [x.sub.i] and [x.sub.j], and [a.sub.ij] = 9 indicates that [x.sub.i] is absolutely preferred to [x.sub.j], then [a.sub.ij] [member of] [1/9,9].

Fuzzy preference relation B on a set of alternatives X is a fuzzy set on the product set X x X, characterized by membership function [[micro].sub.B]: X x X [right arrow][0,1]. Therefore:

B = [b.sub.ij]] [b.sub.ij] = [[mu].sub.B] ([x.sub.i], (x.sub.j]) [for all]i, j [member of]{1, ..., n}; (3)

[b.sub.ij] + [b.sub.ji] = 1 [for all]i, j [member of] { 1, ..., n}, (4)

where [[mu].sub.] is a membership function and [b.sub.ij] is the preference ratio of alternative [x.sub.i] over [x.sub.j]. A [b.sub.ij] at 0.5 denotes that [x.sub.i] and [x.sub.j] are indifferent, and a [b.sub.ij] at 1 represents that [x.sub.i] is preferred absolutely to [x.sub.j].

The method (Herrera-Viedma et al. 2004) to transformation multiplicative preference relations A to fuzzy preference relations B and obtain relative weights presents the following:

(1) Get (n-1) values {[a.sub.12], [a.sub.23], ..., [a.sub.(n-1].sub.n]} of multiplicative preference relations A;

(2) Diagonal elements of A are values at 1.0, using the equation (5) to calculate the remaining elements in the upper right part of the diagonal:

[a.sub.i(j-1)] x [a.sub.(i+ 1).sub.j]/[a.sub.(i+1)(j-1)]. (5)

Elements in the lower left part of the diagonal in were calculated using the equation shown below:

[a.sub.ij] = 1/[a.sub.ji]; (6)

(3) Let Z = max[A], then transfer multiplicative preference relations A to a consistently MPR matrix C with a normal to interval [1/9, 9] with equation (7):

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]; (7)

(4) Apply equation (8) to transform the consistent MPR matrix C to FPR matrix B:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (8)

2.3. Cumulative prospect theory

Consider a prospect (mutual fund) X with outcomes [x.sub.1] [less than or equal to] ... [less than or equal to] [x.sub.k] [less than or equal to] 0 [x.sub.k+1] [less than or equal to] ... [less than or equal to] [x.sub.n] that are associated with probability [p.sub.1], ..., [p.sub.k], [P.sub.k+1], ..., [p.sub.n]. Cumulative prospect theory predicts that people will choose prospects based on the value (Tversky and Kahneman 1992):

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (9)

where [lambda] > 0 is a loss-aversion parameter and [pi] are decision weights calculated based on "cumulative" probabilities associated with the outcomes. In particular, prospect theory assumes a probability weighting function [w.sup.+] : [0; 1] [right arrow] [0; 1] for gains and a probability weighting function [w.sup.-] : [0; 1] [right arrow] [0; 1] for losses. In CPT the utility function v(x) is unchanged from the original PT (Tversky and Kahneman 1992; Tversky and Fox 1995; Gonzalez and Wu 1999), which is concave for gains and convex for losses, with the loss function assumed to be steeper than the gain function ([beta] > 1):

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (10)

The decision weights employed in CPT are given by Tversky and Kahneman (1992):

[[pi].sub.1] = [w.sup.-] ([p.sub.1] and [[pi].sub.n] = [w.sup.+] ([p.sub.n]); (11)

[[pi].sup.-.sub.i] = [i.summation over (j=1)] [w.sup.-]([p.sub.j]) - [i-1.summation over (j=1)] [w.sup.-] ([p.sub.j]) for 2 [less than or equal to] i [less than or equal to] k

and

[[pi].sup.+.sub.i] = [n.summation over (j=i)] [w.sup.+] ([p.sub.j]) - [n.summation over (j=i+1)] ([p.sub.j]) for k + 1 [less than or equal to] i [less than or equal to] n - 1, (12)

where probability weighting functions [w.sup.-] and [w.sup.+] are defined for probabilities associated with losses and gains, respectively, which may be experimentally estimated using the following formulae (Tversky and Kahneman 1992; Camerer and Ho 1994; Wu and Gonzalez 1996):

[w.sup.-] (x) = [x.sup.[delta]]/[([x.sup.[delta]] + (1 - [x.sup.[delta]])).sup.1/[delta]]

and

[w.sup.+] (x) = [x.sup.[gamma]]/[([x.sup.[gamma]] + (1 - [x.sup.[gamma]])).sup.1/[gamma]]. (13)

For only gain conditions, equations (9) will transform to:

V[[p.sub.1], [x.sub.1]; [p.sub.2], [x.sub.2]] = 2[summation].sub.j=1] [[pi].sup.+].sub.j] x v([x.sub.j]) = [[pi].sup.+.sub.1] x v([x.sub.1]) + [[pi].sup.+.sub.2] x v([x.sub.2]). (14)

3. Constructing a multi-criteria prospect model for bidding decisions

3.1. Multi-criteria prospect model for bidding decision

This study adopted BD-MCPM, which combined FPR and CPT, to modeling the construction company's bidding decision processes using the three phases shown in Fig. 1.

3.2. Phase I--preparation

The bidding decision process generates two decisions: whether to submit or not submit a bid (bid/no bid) and, if so, the scale of the markup component of the bid (markup) (Egemen and Mohamed 2008). Many factors affect decision making in each phase. Phase I should first identify the key factors that influence a bidding decision and, based on such factors, collect and organize relevant project data/information.

[FIGURE 1 OMITTED]

3.2.1. Identify the key factors of influence in a bid decision

Many studies designed to identify the factors that influence bidding decisions have been conducted in recent years. Some have adopted a contractor perspective. Others have focused on conditions limited to a particular, localized situation. Still others have taken a multinational perspective. All have worked to identify key factors of influence at work on local contractor bid decisions. The purpose of Phase I in the BD-MCPM was to identify, respectively, the key factors influencing bid/no bid and markup decisions. The identification process was a two step process, which first reduced the total potential number of factors by identifying and choosing only those referenced consistently in the literature in order to identify a shortlist of 'pre-adapted' factors. The second step incorporated these pre-adapted factors into a questionnaire, which was send to local contractors who were asked to assess the importance of each factor on a scale from 1 to 9 (1:very unimportant, 9:very important). Each factor was then assigned an importance score based on an average of submitted scores. Table 1 shows 44 factors identified in the literature as affecting bid/no bid decision making (Cook 1985; Skitmore 1985; Marsh 1989; Cooke 1992; Odusote and Fellows 1992; Shash 1993; Wanous et al. 2000; Chua and Li 2000; Han and Diekmann 2001; Lewis 2003). Sixteen of these factors were prioritized as they were mentioned in five or more of the referenced articles. Ten of these prioritized factors received average scores of importance equal to or greater than 5, and were ranked from highest to lowest.

Similarly, Table 2 shows the eight factors identified in the literature as affecting markup decisions (Odusote and Fellows 1992; Dozzi et al. 1996; Li 1996; Dulaimi and Shah 2002). A shortlist of those that were mentioned in two or more articles was then made, and those from the shortlist with average earned scores of importance equal or greater than 5 were ranked.

3.2.2. Case collection

A case study to test the ability of the BD-MCPM model to solve the above problem was conducted to illustrate the effectiveness of the approach in practice. The background of research participants were considered to be homogeneous in the sense that they were all qualified professionals in construction field with previous knowledge of bidding strategies and bidding procedures. Table 3 presents a summary of data collected on three actual projects.

3.3. Phase II--deciding to bid or not to bid

The goal of Phase II was to make a decision whether or not to bid on a particular project. The 10 key factors that affect the bid/no bid decision were identified in Section 3.2.1. By assessing the relative weights and risk scores for these factors, a bid/no bid score may be obtained, which can then be used to make the decision whether to bid or not.

3.3.1. Determining the relative weight of influencing factors for bid/no bid decisions

The seven steps employed to determine the relative weight of identified factors of influence are described as follows:

Step 1: Define linguistic variables. This study used 9 linguistic terms {AM: Absolutely more important, VM: Very strongly more important, SM: Strongly more important, WM: Weakly more important, EQ: Equally important, WL: Weakly less important, SL: Strongly less important, VL: Very strongly less important, AL: Absolutely less Important} associated with real number {5, 4, 3, 2, 1, 1/2, 1/3, 1/4, 1/5} to compare corresponding neighboring factors.

Step 2: Obtain questionnaire input. Ten factors of influence [[BF.sub.i](/=1,2,...,10)] were considered in making the bid / no bid decision. Via a questionnaire survey or interviews, the kth evaluator assessed the relative intensity of importance of the two adjoining factors [BF.sub.i] and [BF.sub.j] to obtain 9 grades of importance [[a.sup.k.sub.ij] (i = 1,2,...,9, j = i + l), where [a.sup.k.sub.ij] = 1 means indifference between two factors, [a.sup.k.sub.ij] = 2,3,4,5 shows that factor [BF.sub.i] is relatively important to factor [BF.sub.j], [a.sup.k.sub.ij] = 1 / 2,1 / 3,1 / 4,1 / 5 and indicates that factor [BF.sub.i] is less important than factor [BF.sub.j]. Table 4 presents the relative importance of bid/no bid decision factors assessed by evaluator 1.

Step 3: Construct the MPR matrix. To construct the kth evaluator's MPR matrix [A.sup.k], we first translated the linguistic terms of questionnaire results into real numbers [a.sup.k.sub.ij] to fill proper diagonal elements, using Eqs (5) and (6) to calculate the remaining elements of MPR matrix.

For example, from Table 4 we can obtain a set of 9 values {[a.sup.1.sub.1 2] = 4, [a.sup.1.sub.2 3] 3=1, [a.sup.1.sub.4 5] 4=1/4, [a.sup.1.sub.4 5]=1/3, [a.sup.1.sub.5 6] = 1, [a.sup.1.sub.6 7] =1/3, [a.sup.1.sub.7 8] = 2, [a.sup.1.sub.8 9] = 1/2, [a.sup.1.sub.9 10] =3 }, the MPR matrix of evaluator 1's may be constructed as follows:

[A.sup.1] = 1 4 4.00 1.00 0.33 0.33 0.11 0.22 0.11 0.33 0.25 1 1 0.25 0.08 0.08 0.03 0.06 0.03 0.08 0.25 1.00 1 1/4 0.08 0.08 0.03 0.06 0.03 0.08 1.00 4.00 4.00 1 1/3 0.33 0.11 0.22 0.11 0.33 3.00 12.00 12.00 3.00 1 1 0.33 0.67 0.33 1.00 3.00 12.00 12.00 3.00 1.00 1 1/3 0.67 0.33 1.00 9.00 36.00 36.00 9.00 3.00 3.00 1 2 1.00 3.00 4.50 18.00 18.00 4.50 1.50 0.50 0.50 1 1/2 1.50 9.00 36.00 36.00 9.00 3.00 1.00 1.00 2.00 1 3 3.00 12.00 12.00 3.00 1.00 0.33 0.33 0.67 0.33 1

Let Z = max[[A.sup.k], a consistently MPR matrix [C.sup.k] with a normal to interval [1/5, 5], the transform function show in Eq. (15) will change to Eq. (16) :

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (15)

For example, the maximum value of evaluator 1's [A.sup.1] MPR matrix was 36. Applying Eq. (15), a consistently MPR matrix [C.sup.1] may be obtained as follows:

[C.sup.1] = 1.00 1.86 1.86 1.00 0.61 0.61 0.37 0.51 0.37 0.61 0.54 1.00 1.00 0.54 0.33 0.33 0.20 0.27 0.20 0.33 0.54 1.00 1.00 0.54 0.33 0.33 0.20 0.27 0.20 0.33 1.00 1.86 1.86 1.00 0.61 0.61 0.37 0.51 0.37 0.61 1.64 3.05 3.05 1.64 1.00 1.00 0.61 0.83 0.61 1.00 1.64 3.05 3.05 1.64 1.00 1.00 0.61 0.83 0.61 1.00 2.68 5.00 5.00 2.68 1.64 1.64 1.00 1.37 1.00 1.64 1.97 3.66 3.66 1.97 1.20 1.20 0.73 1.00 0.73 1.20 2.68 5.00 5.00 2.68 1.64 4.64 1.00 1.37 1.00 1.64 1.64 3.05 3.05 1.64 1.00 1.00 0.61 0.83 0.61 1.00

Step 4: Transform the consistent MPR matrix to a fuzzy preference relation matrix. The consistent MPR matrix [c.sup.k.sub.ij [member of] [1/5,5], the transform function shown in Eq. (8) will change to Eq. (16) shown below:

[b.sub.ij] = g ([a.sub.ij]) = (1 + [log.sub.5] [c.sub.ij])/2. (16)

Applying Eq. (16), the evaluator 1's FPR matrix [B.sup.1] may be obtained as follows:

[B.sup.1] = 0.50 0.69 0.69 0.50 0.35 0.35 0.19 0.29 0.19 0.35 0.31 0.50 0.50 0.31 0.15 0.15 0.00 0.10 0.00 0.15 0.31 0.50 0.50 0.31 0.15 0.15 0.00 0.10 0.00 0.15 0.50 0.69 0.69 0.50 0.35 0.35 0.19 0.29 0.19 0.35 0.65 0.85 0.85 0.65 0.50 0.50 0.35 0.44 0.35 0.50 0.65 0.85 0.85 0.65 0.50 0.50 0.35 0.44 0.35 0.50 0.81 1.00 1.00 0.81 0.65 0.65 0.50 0.60 0.50 0.65 0.71 0.90 0.90 0.71 0.56 0.56 0.40 0.50 0.40 0.56 0.81 1.00 1.00 0.81 0.65 0.65 0.50 0.60 0.50 0.65 0.65 0.85 0.85 0.65 0.50 0.50 0.44 0.44 0.35 0.50

Step 5: Aggregate the FPR matrix for all evaluators. The opinions of different evaluators were aggregated to obtain an aggregated weight for each factor of influence. [b.sup.k.sub.ij] was employed to denote the fuzzy preference relationship value of the kth evaluator to assess factors i and j. This study used an average value method to integrate the judgment values of m evaluators and obtain the averaged FPR matrix [bar.B]. The average function is shown below:

[[bar.b].sub.ij] = 1/m ([b.sup.1.sub.ij] + [b.sup.2.sub.ij] ... + [b.sup.m.sub.ij]). (17)

For example, [b.sup.k.sub.ij] for 3 evaluators were [b.sup.1.sub.12] = 0.69, [b.sup.2.sub.12] = 0.72, and [b.sup.3.sub.12] = 0.78. Equation (17) was then applied to generate [[bar.b].sub.12] = 0.73. The same approach was used to obtain an averaged FPR matrix [bar.B], as follows:

[bar.B] = 0.50 0.73 0.85 0.69 0.50 0.54 0.54 0.64 0.49 0.62 0.27 0.50 0.62 0.46 0.26 0.31 0.31 0.41 0.26 0.38 0.15 0.38 0.50 0.34 0.14 0.19 0.19 0.29 0.14 0.26 0.31 0.54 0.66 0.50 0.30 0.35 0.34 0.45 0.30 0.42 0.50 0.74 0.86 0.70 0.50 0.55 0.54 0.65 0.50 0.62 0.46 0.69 0.81 0.65 0.45 0.50 0.50 0.60 0.45 0.57 0.46 0.69 0.81 0.66 0.46 0.50 0.50 0.61 0.45 0.58 0.36 0.59 0.71 0.55 0.35 0.40 0.39 0.50 0.35 0.47 0.51 0.74 0.86 0.70 0.50 0.55 0.55 0.65 0.50 0.62 0.38 0.62 0.74 0.58 0.38 0.43 0.43 0.53 0.38 0.50

Step 6: Normalize the aggregated FPR matrix. Using R to indicate the normalized aggregate FPR matrix, the value of element [r.sub.ij] can be obtained using the function shown below:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (18)

For [[bar.b].sub.12] = 0.73, [10.summation over (i=1)] [[bar.b].sub.ij] = 6.21 when j = 2, applying equation (18), we can then get [r.sub.12]=0.118 and, in the same manner, obtain normalize averaged FPR matrix R as follows:

R = 0.128 0.118 0.115 0.119 0.129 0.125 0.123 0.121 0.129 0.122 0.069 0.081 0.084 0.079 0.069 0.072 0.070 0.077 0.068 0.076 0.038 0.061 0.067 0.059 0.037 0.044 0.042 0.055 0.037 0.052 0.079 0.087 0.089 0.086 0.078 0.081 0.078 0.084 0.078 0.083 0.129 0.118 0.115 0.120 0.130 0.127 0.124 0.122 0.130 0.123 0.117 0.111 0.109 0.112 0.118 0.116 0.113 0.113 0.118 0.113 0.119 0.112 0.110 0.112 0.119 0.117 0.114 0.114 0.119 0.114 0.091 0.095 0.096 0.094 0.091 0.092 0.090 0.094 0.091 0.093 0.131 0.119 0.116 0.120 0.131 0.128 0.125 0.122 0.131 0.124 0.099 0.099 0.099 0.099 0.099 0.099 0.121 0.099 0.099 0.099

Step 7: Obtain relative weights. Given that [WB.sub.i] denotes the priority weight of influencing factor i, the priority weight of each factor may be obtained using the following function:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (19)

In Case 1, [10.summation over (i=1)] [10.summation over (j=1)] [r.sub.ij] = 10 and [10.summation over (j=1)] [r.sub.ij] = 1.228 for i = 1,

applying equation (19) obtains a relative weight for influencing factor [BF.sub.1] of 0.123. Following the same process, the relative weights of influencing factors in Case 1 as assessed using three evaluators were obtained as [WB.sub.i] = {0.123, 0.074, 0.049, 0.082, 0.124, 0.114, 0.115, 0.093, 0.125, 0.101}.

3.3.2. Assessing the risk score for factors of influence in bid/no bid decision making

Risk score [RS.sub.i] represents the degree of risk in the factor of influence [BF.sub.i], which has been subjectively established by PDM using predetermined scores {0 - No risk, 25--Low risk, 50--Moderate risk, 75--High risk, 100--Prohibitive risk}. For Case 1, the value of risk associated with influencing factors using PDM is illustrated in Table 5.

3.3.3. Deciding to or not to submit a bid

A total bid/no bid score may then be calculated by summing degrees of significance:

[V.sub.B] = [10.summation over (i=1)] [WB.sub.i] x [RS.sub.i]. (20)

If [V.sub.B] [less than or equal to] 50, then a "bid" decision is recommended. Applying equation (20) to bid/no bid scores for cases 1 through 3 returned, respectively, total scores of 43.8, 52.9 and 45.3. As such, the contractor should bid on Case 1 and Case 3 and proceed to Phase III (the markup phase) for both.

3.4. Phase III--deciding appropriate markup

Once a decision to bid has been made, the next step is to determine appropriate markup sizes for bidding projects. Firstly, the PDM assign a set of markup scales. The evaluator then determines the relative weight of each factor of influence (see also Section 3.2.1) for each special markup scale. The resulting assess the probability of winning project for specify markup scale. The determining process is illustrated below.

3.4.1. Assign specific scale of markup

In construction projects, the scale of a markup is determined based on relevant contractor policies and project type. In general, markups tend to represent 3% ~ 7% of a project's total estimated cost, although in certain cases, markups may be in the 10% ~ 15% range or higher. This study adopted 5 frequently used markup scales {[M.sub.1]=3%, [M.sub.2]=4%, [M.sub.3]=5%, [M.sub.4]=7%, [M.sub.5]=10%} as examples.

3.4.2. Determine relative weight of influencing factors for special markup scale

The eight key factors previously identified as affecting markup scale decision making (MF.sub.i]) are listed in Table 2. These also represent factors of influence on outcome implementation. Assigning weights to each factor is done in the same manner as that described in Section 3.3.1. The only difference was the associated factors of influence used. The relative weight of each influencing factor [MF.sub.i], assessed using 3 evaluators, were obtained and represented as [WM.sub.i] = {0.078, 0.078, 0.064, 0.123, 0.191, 0.118, 0.150, 0.198}.

3.4.3. Forecast probability of winning project using markup scale

As bids typically involve multiple potential contractors, assessing the probability of bid success over competitors at a particular markup level is critical. Of course, markup scale may be expected to correlate inversely to probability of bid success. FPR was used here to forecast project bid success in the same manner as in Section 3.3.1.

For each case and defined markup scale, evaluators used linguistic terms to judge subjectively the relative importance of each factor in winning a bid and in losing a bid [MF.sub.i].

For example, in Case 1, when the markup was set to 3%, Evaluator 1 assessed the relative importance to win/lose case probability to be [b.sup.1.sub.uv] = {5,4,4,3,5,4,5,4}. Using questionnaire results, a 2x2 pair-wise comparison MPR matrix could then be constructed with two outcomes ("win" and "lose") for each factor of influence. The pair-wise comparison MPR matrix [B.sup.k] for each influencing factor was then constructed (see Table 6 below).

Applying the same process described from step 4 to 7 in Section 3.3.1., derived the average rating [PR.sub.i], which described the potential for winning in light of the identified factors of influence [MF.sub.i]. For example, in Case 1, at a markup of 3%, win probability ratings for relevant factors of influence [MF.sub.i] may be obtained using [PR.sub.i] ={0.81, 0.76, 0.79, 0.69, 0.78, 0.76, 0.83, 0.76}.

Finally, for a specify markup scale, the forecast probability of winning PM may be obtained using the following function:

PM = [8.summation over (i=1)] [WM.sub.i] x [PR.sub.i], (21)

where [WM.sub.i] and [PR.sub.i] denote the relative weights and the probability ratings of winning for identified markup scale factors of influence [MF.sub.i], i [member of] (1,2, ..., 8).

Using the example of Case 1 at a 3% markup and the value for [WM.sub.i] obtained in Section 3.4.2., we may apply equation (21) to obtain a win probability forecast PM = 78%. In the same manner, the win probability forecast at 4%, 5%, 7% and 10% markups were 71%, 63%, 43% and 25%, respectively, for Case 1. In Case 3, win probability forecasts were 77%, 68%, 60%, 46% and 28% for defined markups in the 3~10% range.

As defined in the model construct, probability of winning a project is kept and probability of losing a project is ignored, the latter yields a prospect value equal to 0.

3.4.4. Elicit the PDM utility function for the markup scale

This study adopted the TO method proposed by Wakker and Deneffe (1996) to elicit the PDM utility function for the markup scale. This paper will not describe the mechanisms by which such was accomplished, as the method has been described previously in the literature (Bleichrodt and Pinto 2000; Abdellaoui 2000; Abdellaoui et al. 2005). The elicited result for the PDM utility function is shown in Fig. 2.

[FIGURE 2 OMITTED]

3.4.5. Elicit the PDM probability weighting function

Bleichrodt and Pinto (2000) proposed a method to elicit PWF based on the TO method. This method first set p' [less than or equal to] 0.5 for low probabilities and p '>0.5 for high probabilities, then chose two prospects which were queried to subjects in order to assess an outcome. For probabilities p' [less than or equal to] 05, subjects were asked to assess an outcome [z.sub.y] such that the difference between [p', [x.sub.i]; 1 - p', [x.sub.j]] and [p', [x.sub.k]; 1 - p', [z.sub.r]] with [X.sub.k] [greater than or equal to] [x.sub.i] > [x.sub.j], [x.sub.k], [x.sub.i], and [x.sub.j] are elements of the standard sequence elicited in 3.4.4. The weighting of probabilities w (p') were determined using:

w(p') = u([x.sub.j]) - u([z.sub.r])/[u([x.sub.j]) - u([z.sub.r])] + [u([x.sub.k]) - u([x.sub.i]). (22)

For probabilities p '>0.5, subjects were asked to assess an outcome [z.sub.s] such that there is indifference between [p', [x.sub.m]; 1 - p', [x.sub.n]] and [[p', [z.sub.s]; 1-[p', [x.sub.q]] with [x.sub.m] [greater than or equal to] [x.sub.n] [greater than or equal to] [x.sub.q], [x.sub.m], [x.sub.n], and [x.sub.q] are elements of the standard sequence. Weighting of probabilities w(p') were determined by:

w(p') = u([x.sub.n]) - u([x.sub.q])/[u([z.sub.s]) - u)[x.sub.m])] + [u([x.sub.n]) - u([x.sub.q])]. (23)

This study used the same probabilities p' ={0.10, 0.25, 0.50, 0.75, 0.90} as those in Bleichrodt's study to elicit PDM's PWF. In the elicitation procedure, the PDM may be used to assess an outcome for the two prospects in probabilities that range from 0.10 to 0.90. If the first assumption assumes a low setting probability p', then the PDM will be asked to assess [z.sub.y] for the prospect of p', [x.sub.i]; 1 - p', [x.sub.j]] and [p', [x.sub.k]; 1 - p', [z.sub.r]], and apply equation (23) to calculate w(p'). If w(p') [greater than or equal to] 0 and p' [greater than or equal to] w (p'), then p' represents a low probability. Otherwise, p' should be high probability, and PDM will be asked in the same p' again to assess [z.sub.s] with prospects [p', [x.sub.m]; 1 - p', [x.sub.n]] and [p', [z.sub.s]; 1 - [p', [x.sub.q]], and apply equation (28) to calculate w(p') . Other probabilities of p' are assumed at a high probability to elicit w (p') . Fig. 3 shows the elicited PWF of the PDM in this study.

[FIGURE 3 OMITTED]

3.4.6. Determine the prospect value of the markup scale

Under CPT and FPR, the Prospect Value [V.sub.CPT] ([M.sub.i]) at a specified markup scale [M.sub.i] may be determined using the CPT equation:

[V.sub.CPT] ([M.sub.i]) = U ([M.sub.i]) x W (P[M.sub.i]), (24)

where U ([M.sub.i]) and W ([PM.sub.i]) may be found by interpolation:

U([M.sub.i]) = [M.sub.i] - [M.sub.j]/[M.sub.j+1] - [M.sub.j] [U([M.sub.j+1]) - U([M.sub.j])] + U([M.sub.j]); (25)

W([PM.sub.i]) = [PM.sub.i] - [PM.sub.j]/[PM.sub.j+1] - [PM.sub.j][W([PM].sub.j+1]])-W([PM.sub.j])]+W([PM.sub.j]). (26)

The calculated CPT value for each markup scale in Case 1 and Case 3 were listed in Table 7.

3.4.7. Comparison and decision making

Selecting the highest markup scale CPT value (Table 7) determined the markup scale in each case (i.e., 5% for Case 1 and 7% for Case 3). Estimated profit and bid price for Cases 1 and 3 were calculated and are shown in Table 8. Under circumstances in which contractors may only choose one case on which to bid, other consideration factors may be brought into play (e.g., duration, funding requirements, etc.).

4. Discussions

BD-MCPM was used successfully to help PDM determine which case(s) should be bid and the optimal markup. Knowing competitor markup scales prior to tender submission would be helpful in modifying the markup recommendations generated by BD-MCPM and allow for adjustments critical to winning the bid (markup adjustment downward) or increasing profit (markup adjustment upward). In practice, however, it is difficult to elicit a competitor's UF and PWF. Therefore, an effective methodology with which to infer such represents a valuable direction for future research.

5. Conclusions

This study developed a Multi-Criteria Prospect Model for Bidding Decision (BD-MCPM) to help contractors determine whether to submit a bid and, when the answer is in the affirmative, set an optimal markup scale. Research contributions include:

1. Identification of ten and eight key influencing factors used by contractors in Vietnam to make decisions, respectively, on bid/no bid and mark-up scales using literature review and questionnaire survey techniques.

2. Introduction of a new Multi-Criteria Prospect Model for Bidding Decision (BD-MCPM), which combines fuzzy preference relations (FPR), cumulative prospect theory (CPT), and Multi-Criteria Prospect Model (MCPM). The BD-MCPM is a systematic bidding model designed to help construction companies make strategic bid / no bid decisions and to determine the optimal markup scale for each project bid.

3. FPR using only a small number of expert input variables provides consistency in fuzzy preference relations that simplifies the process of evaluating relative factor weights to deciding bid/no bid phase and forecast probability project win for specific mark-up size. Moreover, applying FPR to evaluation and forecasting can connote the characteristic of evaluator's "experience" and "guesswork".

4. CPT evaluates the primary decision maker's risk prospects in terms of utility functions and probability weighting functions. CPT calculates preference values for assigned mark-up scales and probability of a project win based on prior forecasts. It further selects the mark-up scale delivering the optimal preference value so that the decision maker can make an optimal decision that takes into account the PDM's intuition.

5. The study validated the BD-MCPM using actual bidding projects obtained from construction companies in Vietnam and successfully helping the PDM to select cases on which to bid and to set optimal markup scale.

doi: 10.3846/13923730.2011.598337

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Min-Yuan Cheng (1), Chia-Chi Hsiang (2), Hsing-Chih Tsai (3), Hoang-Linh Do (4)

Department of Construction Engineering, National Taiwan University of Science and Technology, Keelung Rd., Taipei, Taiwan, R. O. C. 106

E-mails: (1) myc@mail.ntusledu.tw; (2) chiachi@cycu.edu.tw (corresponding author);

(3) tsaihsingchih@gmail.com; (4) hoanglinhmonitor@yahoo.com

Received 08 Oct. 2009; accepted 26 Nov. 2010

Min-Yuan CHENG. Professor in Department of Construction Engineering at National Taiwan University of Science and Technology (NTUST), Taiwan. He is a Board of Directors member of International Association for Automation and Robotics in Construction (IAARC). His research interests include geographic information system, construction automation and e-business, management information system, business process reengineering, artificial intelligence, knowledge management, and their applications.

Chia-Chi HSIANG. Lecturer in Department of Civil Engineering at Chung Yuan Christian University (CYCU), Taiwan. He is a Ph.D. candidate in Department of Construction Engineering at National Taiwan University of Science and Technology, Taiwan. His research interests include prospect theory, fuzzy theory, game theory, construction management, and bidding strategy.

Hsing-Chih TSAI. Assistant professor at National Taiwan University of Science and Technology, Taiwan. His research interests include computational mechanics, swarm intelligence, construction management, RFID Application, high-order neural networks, and soft programming engineering.

Hoang-Linh DO. He received his master's degree in Department of Construction Engineering, National Taiwan University of Science and Technology in 2008. His research interests include prospect theory, fuzzy theory, and bidding strategy.

Table 1. Key influencing factors of bid/no bid decision Category No. Inferential Factor Client 1 Reputation of client 2 Relationship with client 3 Financial capability of the client 4 Client requirements 5 Fostering good relationship with regular clients Other 6 Proportions to be subcontracted 7 Reputation of other consultants 8 Relationship with other consultants Project 9 Nature of project 10 Project size 11 Project period 12 Project complexity 13 Project location Resources 14 Experience for similar project 15 Professional demands of the contract 16 Physical resources necessary to carry out project 17 Availability of qualified/experienced staff 18 Financial resources necessary to carry out project Tender 19 Time available for tender preparation 20 Cost of bidding 21 Tender conditions 22 Tendering method 23 Adequacy of tender information 24 Current workload in bid preparation Contract 25 Type of contract 26 Contractual conditions Company 27 Compliance with business strategy 28 Current work load 29 Availability of other projects 30 Promoting reputation 31 Operational capacity Competitors 32 Number of competitors 33 Competence of the expected competitors 34 Degree of competition 35 Perceived chances of being successful Financial 36 Client budget 37 Financial situation 38 Expected profitability 39 Expected cash flow 40 Confidence in the cost estimate 41 Projected break-even point for the contract Culture 42 Local customs Market 43 Market conditions Risk 44 Expected risk Category No. Literatures 1 2 3 4 5 6 7 8 9 10 Client 1 * * * * * * 2 * * * * * * 3 * * * 4 * * 5 * Other 6 * * * 7 * * 8 * * * Project 9 * * * * * * * 10 * * * * * 11 * * * * 12 * * * * * 13 * * * * * * Resources 14 * * * * * * 15 * * 16 * 17 * * * * * 18 * Tender 19 * * * * * * 20 * * * 21 * * * 22 * * * 23 * * * * 24 * Contract 25 * * * 26 * * * * * * Company 27 * * 28 * * * * * 29 * * * * * * 30 * * 31 * * Competitors 32 * * * * * 33 * * 34 * 35 * * Financial 36 * * 37 * * * * 38 * * * * * 39 * * * 40 * * 41 * Culture 42 * * Market 43 * * * * * Risk 44 * * * * * Questionnaire survey Category No. Average Factor Score Client 1 2 6.55 [BF.sub.6] 3 4 5 Other 6 7 8 Project 9 10 7.10 [BF.sub.3] 11 12 6.25 [BF.sub.7] 13 Resources 14 7.15 [BF.sub.2] 15 16 17 5.85 [BF.sub.8] 18 Tender 19 20 21 22 23 24 Contract 25 26 6.75 [BF.sub.4] Company 27 28 6.70 [BF.sub.5] 29 30 31 Competitors 32 5.25 [BF.sub.9] 33 34 35 Financial 36 37 38 7.20 [BF.sub.1] 39 40 41 Culture 42 Market 43 Risk 44 5.10 [BF.sub.10] Note: Literature (1) Cook (1985); (2) Skitmore (1985); (3) Marsh (1989); (4) Cooke (1992); (5) Shash (1993); (6) Odusote and Fellows (1992); (7) Wanous et al. (2000); (8) Chua and Li (2000); (9) Han and Diekmann (2001); (10) Lewis (2003) Table 2. Key influencing factors for markup scale decision Category Inferential factor Factor Project Project size [MF.sub.5] Resources Experience in similar project [MF.sub.6] Company Need for work [MF.sub.i] Current workload [MF.sub.3] Competitors Number of competitors [MF.sub.4] Financial Expected profitability [MF.sub.8] Market Overall economy [MF.sub.7] Risk Expected risk [MF.sub.2] Table 3. Case study data Item Case 1 Case 2 Owner Housing and Urban Hanoi city people's Development Corporation committee (HUD) Project Housing project Housing project 2 units--14 floors 1 unit--21 floor and 21 floor Total Floor area Total Floor area 21960 [m.sup.2] 19950 [m.sup.2] Basement area Basement area 1588 [m.sup.2] 1800 [m.sup.2] Location Hanoi city, Vietnam Hanoi city, Vietnam Estimated cost Approx. US $17,954,000 Approx. US $4,228,000 Total duration 30 months 18 months Bidding system Open competitive bid Open competitive bid Fund Self, customer Self (government) mobilization fund, Agri Bank Contract type Lump sum Lump sum Payment methods Local currency (VND) Local currency (VND) Timing of 2.5 months 2 months payments Prior project Common markup 3-6% Common markup 3-6% markup scale The best case 20% gain The best case 20% gain The worst case 15% loss The worst case 15% loss Item Case 3 Owner Infrastructure Development and Construction Corporation (LICOGI) Project Housing project 2 units--14 floors and 17 floor Total Floor area 19558 [m.sup.2] Basement area 1500 [m.sup.2] Location HaiPhong city, Vietnam Estimated cost Approx. US$9,735,000 Total duration 24 months Bidding system Open competitive bid Fund Self, government, Viet Com Bank Contract type Lump sum Payment methods Local currency (VND) Timing of 2 months payments Prior project Common markup 3-6% markup scale The best case 20% gain The worst case 15% loss Table 4. Questionnaire sheet for importance of influencing factors of evaluator 1 Intensity of importance Factor Factor [BF.sub.i] AM VM SM WM EQ WL SL VL AL [BF.sub.j] [BF.sub.l] X [BF.sub.2] [BF.sub.2] X [BF.sub.3] [BF.sub.3] X [BF.sub.4] [BF.sub.4] X [BF.sub.5] [BF.sub.5] X [BF.sub.6] [BF.sub.6] X [BF.sub.7] [BF.sub.7] X [BF.sub.8] [BF.sub.8] X [BF.sub.9] [BF.sub.9] X Table 5. Risk assessment by PDM on influencing factors of Case 1 No Influencing factor [RS.sub.i] 0 25 50 75 100 1 Expected profitability X 2 Experience for similar project X 3 Project size X 4 Contractual conditions X 5 Current workload X 6 Relationship with client X 7 Project complexity X 8 Availability of qualified/ X experienced staff 9 Number of competitors X 10 Expected risk X Table 6. Evaluator 1's pair-wise comparison MPR matrix for case 1 and markup scale 3% Influencing MPR [B.sup.k] factor Win Lose [MF.sub.l] Win 1 5 Lose 1/5 1 [MF.sub.2] Win 1 4 Lose 1/4 1 [MF.sub.3] Win 1 4 Lose 1/4 1 [MF.sub.4] Win 1 3 Lose 1/3 1 [MF.sub.5] Win 1 5 Lose 1/5 1 [MF.sub.6] Win 1 4 Lose 1/4 1 [MF.sub.7] Win 1 5 Lose 1/5 1 [MF.sub.8] Win 1 4 Lose 1/4 1 Table 7. CPT value for each markup scale of Case 1 and Case 3 Markup scale Probability Markup Utility Value of Winning Case scale M(n) U(M(n)) P(M(n)) 1 3% 0.252 78% 4% 0.345 71% 5% 0.400 63% 7% 0.508 43% 10% 0.643 25% 3 3% 0.252 77% 4% 0.345 68% 5% 0.400 60% 7% 0.508 46% 10% 0.643 28% Probability Prospect Decision Markup Weight Value Markup Case scale M(n) PW(M(n)) VCPT scale 1 3% 0.682 0.172 4% 0.611 0.211 5% 0.554 0.221 5% 7% 0.424 0.215 10% 0.330 0.212 3 3% 0.668 0.168 4% 0.590 0.203 5% 0.532 0.213 7% 7% 0.439 0.223 10% 0.345 0.222 Table 8. Profit and bid price for Case 1 and Case 3 Case Estimated Cost Decision Profit Bid price (USD) Markup scale (USD) (USD) 1 17,954,000 5% 897,700 18,851,700 3 9,735,000 7% 681,450 10,416,450

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Author: | Cheng, Min-Yuan; Hsiang, Chia-Chi; Tsai, Hsing-Chih; Do, Hoang-Linh |
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Publication: | Journal of Civil Engineering and Management |

Article Type: | Report |

Geographic Code: | 2PANA |

Date: | Sep 1, 2011 |

Words: | 9747 |

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