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Automated Planning and Scheduling for Industrial Construction Processes Open Access


Other title
Dynamic Programming
Domain Independent Artificial Intelligence Planning
Time Stepped Simulation-based Scheduling
Automated Planning and Scheduling
Pipe Spool Fabrication Sequence
Industrial Construction
Module Installation Sequence
Type of item
Degree grantor
University of Alberta
Author or creator
Hu, Di
Supervisor and department
Dr. Yasser Mohamed (Civil and Environmental Engineering)
Examining committee member and department
Dr. Tarek Hegazy (Civil and Environmental Engineering, University of Waterloo)
Dr. Amit Kumar (Mechanical Engineering)
Dr. Simaan AbouRizk (Civil and Environmental Engineering)
Dr. Aminah Robinson Fayek (Civil and Environmental Engineering)
Dr. Amy Kim (Civil and Environmental Engineering)
Department of Civil and Environmental Engineering
Construction Engineering and Management
Date accepted
Graduation date
Doctor of Philosophy
Degree level
Cost overrun and schedule slippage are common problems for mega industrial construction projects. Lack of effective planning and scheduling tools is identified as a major contributing factor to poor project performance. Planning and scheduling tools should be custom designed to address the characteristics of mage industrial projects: unique components, modularized execution strategy and its extremely accelerated project delivery. The main objective of this research is to investigate and develop automated solutions for planning and scheduling two essential stages of mega industrial projects, shop fabrication and on-site construction. This research explores use of discrete event simulation (DES) to automate scheduling of shop fabrication and on-site construction processes. For industrial fabrication shops, a new simulation structuring methodology is developed to address the complex routing issue. Following this methodology, a simulation model is developed for pipe spool fabrication shops, which performs scheduling for shop operations, and mainly evaluates the impact of fabrication sequence on the spool cycle time. For site construction, a time-stepped simulation framework is developed to address congestion and dynamic resource allocation issue. For a real-life industrial construction case, this framework returns a schedule that has 12% shorter duration than those generated from Microsoft Project and Primavera P6. The research investigates use of domain-independent Artificial Intelligence (AI) planning to automate the sequence planing for pipe spool fabrication and on-site module installation. Experiment results show that AI planning is not suitable for sequencing spool fabrication due to the limited parsing capability of existing AI planners. However, AI planning is efficient to identify feasible sequence plans for module installation based on the current module availability and installation status. The research finds Dynamic Programming (DP) is suitable to sequence pipe spool fabrication. A DP algorithm is developed to automatically identify the optimal sequence in terms of the minimum position welds. Simulation experiments were conducted with 29 real-life spools to quantify the performance improvement obtained from the DP algorithm. Results showes that by using the DP algorithm, there is a 45% reduction in the number of position welds, which is translated to a reduction in the total cycle time, ranging from 4.8% to 12%.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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