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Improving Decision-Making Processes in Construction and Infrastructure Bidding : = Qualitative and Quantitative Approaches Including Graph Theory, Game Theory, and Machine Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Improving Decision-Making Processes in Construction and Infrastructure Bidding :/
Reminder of title:	Qualitative and Quantitative Approaches Including Graph Theory, Game Theory, and Machine Learning.
Author:	Ahmed, Muaz Osman Elzubeir.
Description:	1 online resource (311 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
Contained By:	Dissertations Abstracts International86-01B.
Subject:	Architectural engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798383194430

Improving Decision-Making Processes in Construction and Infrastructure Bidding : = Qualitative and Quantitative Approaches Including Graph Theory, Game Theory, and Machine Learning.
Ahmed, Muaz Osman Elzubeir.

Improving Decision-Making Processes in Construction and Infrastructure Bidding :Qualitative and Quantitative Approaches Including Graph Theory, Game Theory, and Machine Learning. - 1 online resource (311 pages)

Source: Dissertations Abstracts International, Volume: 86-01, Section: B.

Thesis (Ph.D.)--Missouri University of Science and Technology, 2023.

Includes bibliographical references

Construction and infrastructure bidding is a highly competitive process that entails various uncertainties faced by contractors. Contractors weigh various factors to determine the expected benefits of a construction project and decide their bid value. However, the situation is more complex in multi-stage bidding (MSG), where general contractors must account for the bids of their subcontractors and face a greater threat of the winner's curse (i.e., situation where the winning contractor underestimates the actual cost of the project). As such, bidding-related complexities, risks, and uncertainties, if uncontrolled, can lead to the rise of claims and disputes between stakeholders. Existing research falls short on the aforementioned bidding-related issues. As such, the goal of this research is to improve decision-making processes in construction and infrastructure bidding with a focus on MSG. The associated four modules that tackle these issues using various approaches, including graph theory, game theory, and machine learning, are as follows: (1) Module 1: investigates the factors that impact the bidding decision-making processes and identifies areas of future research needs; (2) Module 2: derives a novel game-theoretic bid function to be utilized by general contractors to deal with the issue of the winner's curse in MSG; (3) Module 3: creates a holistic framework that aids both general contractors and subcontractors in MSG to deal with the winner's curse issue; and (4) Module 4: investigates the main causes related to the bidding stage, which lead to disputes in construction and infrastructure projects, and identifies the key associations among them. Collectively, this dissertation paves the way towards improved and practical bidding models and contributes to the knowledge and effectiveness of bidding practices in the construction and infrastructure industry.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383194430Subjects--Topical Terms:

1180400
Architectural engineering.
Subjects--Index Terms:

Construction biddingIndex Terms--Genre/Form:

554714
Electronic books.

Improving Decision-Making Processes in Construction and Infrastructure Bidding : = Qualitative and Quantitative Approaches Including Graph Theory, Game Theory, and Machine Learning.
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