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Multi-Objective Optimization for Construction Equipment Fleet Selection and Management In Highway Construction Projects Based on Time, Cost, and Quality Objectives

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Date Issued:
2019
Abstract/Description:
The sector of highway construction shares approximately 11% of the total construction industry in the US. Construction equipment can be considered as one of the primary reasons this industry has reached such a significant level, as it is considered an essential part of the highway construction process during highway project construction. This research addresses a multi-objective optimization mathematical model that quantifies and optimize the key parameters for excavator, truck, and motor-grader equipment to minimize time and cost objective functions. The model is also aimed to maintain the required level of quality for the targeted construction activity. The mathematical functions for the primary objectives were formulated and then a genetic algorithm-based multi-objective was performed to generate the time-cost Pareto trade-offs for all possible equipment combinations using MATLAB software to facilitate the implementation. The model's capabilities in generating optimal time and cost trade-offs based on optimized equipment number, capacity, and speed to adapt with the complex and dynamic nature of highway construction projects are demonstrated using a highway construction case study. The developed model is a decision support tool during the construction process to adapt with any necessary changes into time or cost requirements taking into consideration environmental, safety and quality aspects. The flexibility and comprehensiveness of the proposed model, along with its programmable nature, make it a powerful tool for managing construction equipment, which will help saving time and money within the optimal quality margins. Also, this environmentally friendly decision-support tool model provided optimal solutions that help to reduce the CO2 emissions reducing the ripple effects of targeted highway construction activities on the global warming phenomenon. The generated optimal solutions offered considerable time and cost savings.
Title: Multi-Objective Optimization for Construction Equipment Fleet Selection and Management In Highway Construction Projects Based on Time, Cost, and Quality Objectives.
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Name(s): Shehadeh, Ali, Author
Tatari, Omer, Committee Chair
Al-Deek, Haitham, Committee Member
Abou-Senna, Hatem, Committee Member
Flitsiyan, Elena, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2019
Publisher: University of Central Florida
Language(s): English
Abstract/Description: The sector of highway construction shares approximately 11% of the total construction industry in the US. Construction equipment can be considered as one of the primary reasons this industry has reached such a significant level, as it is considered an essential part of the highway construction process during highway project construction. This research addresses a multi-objective optimization mathematical model that quantifies and optimize the key parameters for excavator, truck, and motor-grader equipment to minimize time and cost objective functions. The model is also aimed to maintain the required level of quality for the targeted construction activity. The mathematical functions for the primary objectives were formulated and then a genetic algorithm-based multi-objective was performed to generate the time-cost Pareto trade-offs for all possible equipment combinations using MATLAB software to facilitate the implementation. The model's capabilities in generating optimal time and cost trade-offs based on optimized equipment number, capacity, and speed to adapt with the complex and dynamic nature of highway construction projects are demonstrated using a highway construction case study. The developed model is a decision support tool during the construction process to adapt with any necessary changes into time or cost requirements taking into consideration environmental, safety and quality aspects. The flexibility and comprehensiveness of the proposed model, along with its programmable nature, make it a powerful tool for managing construction equipment, which will help saving time and money within the optimal quality margins. Also, this environmentally friendly decision-support tool model provided optimal solutions that help to reduce the CO2 emissions reducing the ripple effects of targeted highway construction activities on the global warming phenomenon. The generated optimal solutions offered considerable time and cost savings.
Identifier: CFE0007863 (IID), ucf:52800 (fedora)
Note(s): 2019-12-01
Ph.D.
Engineering and Computer Science, Civil, Environmental and Construction Engineering
Doctoral
This record was generated from author submitted information.
Subject(s): highway construction -- construction equipment -- multi-objective optimization -- genetic algorithm-based -- Pareto trade-offs
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0007863
Restrictions on Access: campus 2024-12-15
Host Institution: UCF

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