Construction prequalification using data envelopment analysis

McCabe, Brenda; Tran, Viet; Ramani, Joseph

doi:10.1139/l04-073

Cited by 50 publications

(18 citation statements)

References 13 publications

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“…This approach was first used in 2005 to develop a contractor prequalification system aiming to assist auctioneers in tenders to select the best contractors, as well as to inform contractors concerning their performance providing guidance for future improvement [34]. Five years later, another study stated that the best bids/candidates in the selection process are usually located on the DEA frontier, an outcome that has immediate applications regarding bid/no-bid decisions [35].…”

Section: Literature Reviewmentioning

confidence: 99%

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Modeling bidding competitiveness and position performance in multi-attribute construction auctions

Ballesteros‐Pérez

Campo-Hitschfeld

Mora-Meliá

et al. 2015

Operations Research Perspectives

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a b s t r a c tCurrently, multi-attribute auctions are becoming widespread awarding mechanisms for contracts in construction, and in these auctions, criteria other than price are taken into account for ranking bidder proposals. Therefore, being the lowest-price bidder is no longer a guarantee of being awarded, thus increasing the importance of measuring any bidder's performance when not only the first position (lowest price) matters.Modeling position performance allows a tender manager to calculate the probability curves related to the more likely positions to be occupied by any bidder who enters a competitive auction irrespective of the actual number of future participating bidders.This paper details a practical methodology based on simple statistical calculations for modeling the performance of a single bidder or a group of bidders, constituting a useful resource for analyzing one's own success while benchmarking potential bidding competitors.

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Section: Literature Reviewmentioning

confidence: 99%

“…Ballesteros-Pérez et al / Operations ResearchPerspectives 2 (2015) [24][25][26][27][28][29][30][31][32][33][34][35] …”

mentioning

confidence: 99%

Modeling bidding competitiveness and position performance in multi-attribute construction auctions

Ballesteros‐Pérez

Campo-Hitschfeld

Mora-Meliá

et al. 2015

Operations Research Perspectives

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“…Additionally, DEA was utilized in construction to benchmark and measure performance by McCabe, Tran, and Ramani (2005), El-Mashaleh, O'Brien, and Minchin, (2006), El-Mashaleh, Minchin, and O'Brien (2007, El-Mashaleh, Rababeh, and Hyari (2010), Cheng and colleagues (2007), Xue, Shen, Wang, and Lu, (2008), Horta, Camanho, and Da Costa (2010), and Horta, Camanho, and Lima (2013).…”

Section: Data Envelopment Analysis (Dea) (Stages 4 and 5)mentioning

confidence: 99%

A Multi-Attribute Decision-Making Model for Construction Enterprise Resource Planning System Selection

El-Mashaleh

Hyari

Bdour

et al. 2015

International Journal of Construction Education and Research

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Enterprise resource planning (ERP) systems are increasingly becoming essential business technologies. Selection of the optimal ERP system among numerous alternatives is regarded as a major challenge for the decision-maker. This choice is complicated by multiple attributes that the decision-maker needs to consider when evaluating different ERP options. The purpose of this article is to propose a multi-attribute decision-making model for ERP system selection based on data envelopment analysis (DEA). The strengths of the proposed DEA model include the input-output framework, which allows decision-makers to incorporate multiple attributes when evaluating different ERP options. These attributes are not required to be congruent. They can be measured by such criteria as monetary sums, time, or subjective score. Additionally, DEA can accommodate a large number of options (i.e., ERP systems) and variables (i.e., attributes). The proposed approach was utilized by a construction contractor to select the optimal ERP option out of eleven alternatives. During the evaluation process, the decision-maker incorporated six attributes to make the decision. These considered attributes are total cost, implementation schedule, functionality, user friendliness, customization capability, and service and support quality. Sensitivity analysis proved that the proposed model is stable and robust.

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“…A series of decision models based on various approaches were developed for contractor selection, such as the integrated multi-objective decision making process (Keeney and Raiffa 1976;Kashiwagi and Byfield 2002;Demirtas and Üstün 2008), the neural networks (Khosrowshahi 2001;Lam et al 2000), AHP (Al-Harbi 2001;Al-Reshaid and Kartam 2005;Mahdi et al 2002;Topcu 2004), DEA (Tran 2002;McCabe et al 2005) the multi-attribute analysis model (Lai et al 2004), analytic network process (Cheng and Li 2004;Ravi et al 2005;Bayazit 2006), integer programming (Missbauer and Hauber 2005), unit-price based (Wang et al 2006), multi-attribute utility theory (Lambropoulos 2007), fuzzy-excellent order method (Wang and Triantaphyllou 2008), fuzzy-AHP-Smart (Padhi and Mohapatra 2009), and the binary goal programming model (Padhi et al 2010). Although these methods were designed to single decision maker, they have drawn great attention to the area of contractor selection in construction projects and provided basis and new ideas to group decision-making.…”

Section: Group Decision-making Approaches For Contractor Selectionmentioning

confidence: 99%

A Group Decision-Making Aggregation Model for Contractor Selection in Large Scale Construction Projects Based on Two-Stage Partial Least Squares (PLS) Path Modeling

et al. 2014

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Modern projects are normally characterized by huge investments, long construction periods, and complex technology. Therefore, the selection of an appropriate contractor for smooth project delivery is challenging. Previous studies attempted to develop suitable frameworks for contractor selection. However, correlation among indicators, the subjectivity of indicator weights, and heterogeneity among experts' professional capabilities for selecting contractors were not successfully removed from the decision-making process. Typical partial least square (PLS) path modeling can solve these problems. However, it can only solve problems with the same direction correlation among the indicators (e.g., the correlation coefficients of the indicators are all 123 B. Liu et al. positive). For indicators with different direction correlations, path modeling is helpless. Aiming to overcome this limitation, this research introduces a group decision model based on two-stage PLSs path modeling. This decision model can eliminate correlation among indicators and the impact of subjectivity and heterogeneity among experts on the reliability of weighting schemes; more importantly, it can effectively solve the problem of having correlation indicators with different directions. Through a literature review, we first established an indicator system of contractor selection on large scale construction projects. Second, a two-stage PLS path modeling combined with the maximization of deviations principle was proposed as an aggregation approach for performance evaluation. Finally, a comparison was made between the two-stage and typical PLS path modeling methods through a case study, which was conducted to validate the reliability of the new approach.

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Construction prequalification using data envelopment analysis

Cited by 50 publications

References 13 publications

Modeling bidding competitiveness and position performance in multi-attribute construction auctions

Modeling bidding competitiveness and position performance in multi-attribute construction auctions

A Multi-Attribute Decision-Making Model for Construction Enterprise Resource Planning System Selection

A Group Decision-Making Aggregation Model for Contractor Selection in Large Scale Construction Projects Based on Two-Stage Partial Least Squares (PLS) Path Modeling

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