A multiple kernel learning-based decision support model for contractor pre-qualification

Lam, K. C.; Yu, Cuibo

doi:10.1016/j.autcon.2010.11.019

Cited by 27 publications

(18 citation statements)

References 15 publications

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“…However, it has also been observed that if not properly materialised through environmental performance measurement tools and indicators, the organisation can have difficulties measuring their environmental performance accurately (Lundberg et al, 2009). Lam and Yu (2011) point out that although an EMS has profound positive environmental influence on construction, optimal performance might not be guaranteed by implementing the EMS alone. For this reason, other instruments such as environmental impact assessments (EIA), life-cycle assessment (LCA) and green specifications could also be used as tools for ensuring environmental sustainability from the perspective of GHG in construction.…”

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confidence: 99%

Evaluation framework for green procurement in road construction

Sánchez¹,

Lehtiranta

Hampson

et al. 2014

Smart and Sustainable Built Environment

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Purpose – Most barriers and enablers of sustainable projects are related to procurement. The purpose of this paper is to propose a framework for evaluating green procurement practices throughout the lifecycle of road construction projects and demonstrates its application through an Australian case study. Design/methodology/approach – The study is based on linking the phases of road construction with incentive mechanisms for proactively motivating behavioural change. A holistic view on utilised and potential incentives is attempted with a literature review and a state-of-practice review. The latter is based on interviews and 90 policy and procurement documents across five Australian states. Findings – An evaluation framework with seven procurement stages is suggested to describe current state green procurement incentives throughout the delivery lifecycle of road construction projects. The Australian case study was found to provide useful data to identify gaps and strong points of the different states regarding their level of integration of sustainability and greenhouse gas emissions (GHG) reduction elements in their procurement practices. This understanding was used to draw recommendations on future advancement of green procurement. Originality/value – Government entities across the globe can impact considerably the achievement of sustainability and GHG targets, by using their procurement practices and requirements to create incentives for contractors and suppliers to engage in more GHG conscious practices. The present study provides a systematic account of how green procurement practices can be underpinned using the Australian road construction industry as a case study, and distinguish between strong and weak links in the green procurement chain to draw recommendations for future initiatives.

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confidence: 99%

Evaluation framework for green procurement in road construction

Sánchez¹,

Lehtiranta

Hampson

et al. 2014

Smart and Sustainable Built Environment

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“…Numerous authors have provided various definitions for contractor prequalification (Lam et al , 2005; Lam and Yu, 2011; Amirhosein, 2013; Afshar et al , 2017). These definitions do not adequately exhaust the true nature of the contractor prequalification process.…”

Section: Introductionmentioning

confidence: 99%

“…Afshar et al (2017) described the steps involved in a generic contractor pre-qualification procedure namely; the selection of the criteria, determining the importance of the criteria, deciding on the kind of data need for assessing the selected criteria, drawing conclusions based on the evidence and selection of suitable contractors. Lam and Yu (2011), divided the prequalification process into four main phases: selection of criteria; weighting of the criteria; scoring of the contractors against individual criterion; and the selection of the ranking system. …”

Section: Introductionmentioning

confidence: 99%

Review of empirical arguments on contractor pre-qualification criteria

Acheamfour

Kissi

Adjei-Kumi

et al. 2019

JEDT

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Purpose The studies on contractor prequalification focus more on the review of models and algorithms rather than review of the criteria for contractor prequalification. However, the basis of every prequalification model primarily relates to the measurement and judgement of prospective contractors based on a set of decision criteria. This paper aims to address the gap by reviewing academic papers on contractor prequalification criteria. Design/methodology/approach A desktop search was conducted under the “T/A/K (title/abstract/keyword)” field of the Scopus search engine. A total of 49 papers were initially identified; however, only peer reviewed journals were selected for the study; therefore, a sample of 36 was subsequently used. Further filtering was done in which 26 papers were found valid for further analysis as it was realized that, not all the identified papers presented empirical arguments about the issue of contractor pre-qualification criteria. The selected 26 papers were subjected to content analysis to identify the key contractor pre-qualification criteria. Findings A total of 41 criteria were identified which were subsequently classified into six main categories, namely, technical considerations, management considerations, financial considerations, reputation considerations, general experience considerations and health, safety and environmental considerations. There was an indication that, the involvement of health, safety and environmental considerations in contractor prequalification proceedings is limited. Research limitations/implications The major limitation of this research was the limited number of papers selected for further analysis based on the Scopus search engine. The identified criteria serve as a basis for further empirical studies on contractor prequalification criteria. Practical implications The outcome of this study broadens the understanding of practitioners and researchers on the various criteria for contractor prequalification. Originality/value By critically reviewing available literature on contractor prequalification, the study sets the tone for further empirical studies on contractor prequalification.

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“…Tserng et al [1] established models to predict the bankruptcy status of construction firms based on the LR and the SVM; the research found that the SVM methods outperform the LR. Lam and Yu [13] proposed a prediction model based on the multiple kernel learning for contractor prequalification.…”

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confidence: 99%

Evaluating Contractor Financial Status Using a Hybrid Fuzzy Instance Based Classifier: Case Study in the Construction Industry

Cheng

Hoang

2015

IEEE Trans. Eng. Manage.

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Construction firms are vulnerable to bankruptcy due to the complex nature of the industry, high competitions, the high risk involved, and considerable economic fluctuations. Thus, evaluating financial status and predicting business failures of construction companies are crucial for owners, general contractors, investors, banks, insurance firms, and creditors. The prediction results can be used to select qualified contractors capable of accomplishing the projects. In this study, a hybrid fuzzy instancebased classifier for contractor default prediction (FICDP) is proposed. The new approach is constructed by incorporating the fuzzy K-nearest neighbor classifier (FKNC), the synthetic minority over-sampling technique (SMOTE), and the firefly algorithm (FA). In this hybrid paradigm, the FKNC is utilized to classify the contractors into two groups ("default" and "nondefault") based on their past financial performances. Since the "nondefault" samples dominate the historical database, the SMOTE algorithm is employed to create synthetic samples of the minority class and therefore alleviates the between-class imbalance problem. Moreover, the FA is employed to determine an appropriate set of model parameters. Experimental results have shown that the proposed FICDP can outperform other benchmark methods.Index Terms-Default prediction, fuzzy instance based classifier, swarm intelligence, synthetic minority over-sampling technique.

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A multiple kernel learning-based decision support model for contractor pre-qualification

Cited by 27 publications

References 15 publications

Evaluation framework for green procurement in road construction

Evaluation framework for green procurement in road construction

Review of empirical arguments on contractor pre-qualification criteria

Evaluating Contractor Financial Status Using a Hybrid Fuzzy Instance Based Classifier: Case Study in the Construction Industry

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