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MODULES MULTI-LIFT PLANNING ON INDUSTRIAL SITE: DEVELOPMENTS OF DECISION SUPPORT TOOLS Open Access

Descriptions

Other title
Subject/Keyword
Module
Mobile Crane
Multi-lift
Heuristic
Monte Carlo Tree Search
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Farajmandi, Mahmoud
Supervisor and department
AbouRizk, Simaan (Civil and Environmental Engineering)
Examining committee member and department
Lu, Ming (Civil and Environmental Engineering)
Liu, Wei Victor (Mining and Petroleum Engineering)
Department
Department of Civil and Environmental Engineering
Specialization
Construction Engineering and Management
Date accepted
2016-09-28T11:03:39Z
Graduation date
2016-06:Fall 2016
Degree
Master of Science
Degree level
Master's
Abstract
Properly planning module installation on industrial sites is a critical factor in ensuring that projects are delivered safely, on time, and within budget. In industrial modular construction, pre-fabricated modules are installed on site, in specific patterns, based on design documents. Depending on module size and weight, as well as crane availability, location, and configuration, different sizes of heavy-duty mobile cranes are used to safely pick, swing, and place modules for installation. High crane operating costs, number of options available to install the modules on site, and multiple crane-module technological constraints necessitate schedulers to spend weeks in a trial-and-error basis to prepare and improve module installation plans. A formalized framework for providing feasible, optimum installation plans will considerably minimize crane operation costs. This thesis focuses on developing a novel framework (i.e., method, algorithm) for automating and improving module installation planning processes for modular construction on industrial construction sites with respect to number of crane relocations, number of crane reconfigurations, and total number of crane locations. Given the proposed framework, a decision tool is developed to facilitate the planning by ensuring that project constraints are satisfied while minimizing the crane operation cost. Two novel methodologies are presented in this thesis. First, a heuristic-based methodology is proposed to build a module installation schedule iteratively by formalizing subject matter expert knowledge using heuristic rules. This methodology is implemented in a software prototype. A sample case study is provided to illustrate the calculation procedures and a practical case study is used to demonstrate the effectiveness of the developed tool. Then, an artificial intelligence based Monte-Carlo Tree Search (MCTS) methodology is proposed where the optimum plan is searched for using biased sampling of the solution space. Based on this methodology, a decision support tool is developed for generating optimum plan. The same sample case study is used to demonstrate the procedure and calculation steps and features of the developed tool. It is found that the methodology efficiently discovers the optimum solution for the smaller scale problem. However, the MCTS-based method requires further development to be applied to large practical projects. As a result of this research, the frameworks, along with their corresponding decision support tools, have been developed to automate on site module installation planning processes. The case studies investigated demonstrates that the heuristic-based rules efficiently minimize crane operation costs for large, complex projects. Although the current MCTS-based methodology is limited in its ability to formulate of module installation plans for large industrial construction projects, these limitations have the potential to be overcome.
Language
English
DOI
doi:10.7939/R3K649Z4X
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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