Repository logo
 

The bids-evaluation decision model development and application for PPP transport projects: a project risks modeling framework

Simple item page
dc.contributor.authorJang, Guan-wei, author
dc.contributor.authorDuff, William S., advisor
dc.contributor.authorAlciatore, David G., committee member
dc.contributor.authorLabadie, John W., committee member
dc.contributor.authorPuttlitz, Christian Matthew, committee member
dc.date.accessioned2007-01-03T04:56:42Z
dc.date.available2007-01-03T04:56:42Z
dc.date.issued2010
dc.description.abstractPublic-private partnership (PPP) infrastructure projects play a key role in economic growth. Value for money (VFM), a core objective when conducting PPP projects, is defined as the optimal combination of whole life costs and benefits of the project to meet user requirements. The PPP infrastructure projects are generally very complex and have highly dynamic, interdependent risks and uncertainties that occur over the life cycle of a project. By using PPP arrangements, experts transfer and allocate risks to the party who is most capable of managing them in a cost effective manner. This requires the optimization of risk allocation between the public and private sectors in order to achieve the best VFM. Risk assessment is a critical element when selecting a project partner and examining projected VFM performance. Unfortunately, the current contractor selection methods used in the industry do not address interdependently dynamic and non-linear risk interactions. Such methods are unable to address unstructured or even semi-structured real world problems. By using these methods, experts often lack the global perspectives of project life cycles and ignore the uncertainty of project performance outcomes. This researcher developed a theoretical approach which applied hybrid techniques to a bidding proposal selection model from the public perspective. Using System Dynamics modeling and relevant statistical techniques, the dynamic risk interactions and interdependencies over project construction and operation phases were addressed and quantified. By employing Monte Carlo simulation, this researcher estimated the probability distribution of the overall project net present value (NPV) with compounding both downside and beneficial effects over project construction and operation phases. By applying appropriate decision making methods to compare the probability distribution of NPV among the bidding proposals, a capable contractor can be selected.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierJang_colostate_0053A_10096.pdf
dc.identifierETDF2010100014MCEN
dc.identifier.urihttp://hdl.handle.net/10217/46217
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectsystem dynamics
dc.subject.lcshPublic-private sector cooperation -- Risk assessment
dc.subject.lcshConstruction projects -- Management -- Decision making
dc.subject.lcshMultiple criteria decision making -- Construction
dc.subject.lcshRisk management
dc.titleThe bids-evaluation decision model development and application for PPP transport projects: a project risks modeling framework
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Jang_colostate_0053A_10096.pdf
Size:
2.92 MB
Format:
Adobe Portable Document Format
Description:
Download

Collections

2000-2019
Theses and Dissertations