PhD Oral Exam - Angat Pal Singh Bhatia, Civil Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Modular construction is a promising alternative to conventional construction; offering improved productivity, high-quality end products, and reduced labour requirements. To realize these benefits, sequencing module components during prefabrication process in a manner that ensures efficient allocation and utilization of labor resources at workstations is essential. However, one of the significant challenges in modular construction manufacturing (MCM) is that it follows a make-to-order process, resulting in customized module components. This customization leads to variations in the design specifications of module components, causing different processing times at each workstation. This imbalances in production lines result in increasing the waiting time for module components between the workstations, ultimately extending the makespan. This poses a challenge for production line managers, requiring frequent adjustments to plans and schedules related to the sequencing of module components at workstations using conventional methods.

To address these challenges, this thesis introduces a framework composed of three modules: (i) a simulation-based statistical method for planning in modular construction; (ii) a deep neural network (DNN)-based method for predicting production process times; and (iii) a hybrid optimization technique for scheduling in modular construction. In the first module, a simulation based statistical method is developed to plan the sequencing of module fabrication and the allocation of workers at workstations. The method encompasses data collection process to obtain historical/near real-time data and identification of significant impact factors affecting process times at workstations along the production line. In the second module, a newly developed method for predicting processing time at each workstation is introduced utilizing Deep Neural Network (DNN), Artificial Neural Network (ANN), and Multiple Linear Regression (MLR) for predicting production process time spent at each workstation in a manufacturing plant. The third module focuses on planning and scheduling method that ensures optimal sequencing of module components at workstations using Genetic Algorithm (GA), Simulated Annealing (SA), and Hybrid Genetic Algorithm Simulated Annealing (HGASA).

Two case studies were analyzed to demonstrate the use of the developed methods and test their performance. The first case is of a light gauge steel (LGS) wall panel production line operated by a modular fabricator in Edmonton, Canada, and the second is of a wood-based semi-automated wall panel production line also in Edmonton, Canada. These cases involve the production of 200 wall panels in the first case and 39703 wall panels in the second at various workstations along the production line. The simulation-based statistical method developed in the first module yielded 89.39% accuracy in prediction of process time and indicate a 44.42 hr duration to produce 309 wall panels with regards to first case. The results of the second case showing process time predictive method developed in the second module for most workstations had a mean absolute error (MAE) of under 2.50 minutes, with symmetric mean absolute percentage error (SMAPE) ranging between 22 % - 28%, respectively. The developed scheduling method of the third module provided an optimal sequence of wall panels for prefabrication, minimizing makespan. As a result, the hybrid optimization reduces makespan to 105.63 hr from those generated by GA (138.08 hr) and SA (108.06hr).