PhD Oral Exam - Ahmed Zaalouk, Building 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

Canada needs 3.5 million new homes by 2030 to address the ongoing housing affordability crisis. To meet this demand, offsite construction methods are viewed as a potential solution to increase housing supply. Among these methods, panelized construction is widely adopted because it offers benefits such as enhanced design flexibility and cost-effective assembly. However, it is more complex than other offsite construction methods, such as modular construction, as managing the production, transportation, and onsite assembly of individual panels is more demanding and time-intensive than delivering fully assembled modular units. Effective coordination among supply chain entities—including factory, trucks, trailers, and project sites—is therefore essential in panelized construction to mitigate the risk of operational conflicts that can lead to cost overruns and schedule delays. Yet, efforts to achieve such coordination are hindered by the absence of an effective and systematic master planning and scheduling approach that integrates the interlinked operations of factory production, transportation, and onsite assembly. Current practices are manual and time-consuming, lacking an integrated and optimized approach, which results in bottlenecks, underutilized resources, excess inventory, extended supply chain makespan , and increased costs.

This research presents a Just-in-Time-based integrated master planning and scheduling framework for supply chain management in panelized construction. The developed framework is composed of four key components: (1) a supply chain management structure framework that: (i) brings together the different operations involved in the supply chain; (ii) characterizes their relationships; and (iii) defines how these operations interfere with various planning and scheduling levels; (2) a supply chain master planning method that: (i) consolidates supply chain operations and resources by adopting Just-in-Time principles to align production with onsite needs; (ii) manages shared resources between sites and coordinates reverse transportation flow using a GIS map; and (iii) considers panel customization and practical planning rules; (3) an integrated master scheduling and resource allocation method that consists of: (i) a factory production scheduling model, which aligns factory operations with panel delivery dates across project sites; (ii) a logistics scheduling model, which manages the flow of logistics resources between the factory and sites; and (iii) an onsite assembly scheduling model, which ensures onsite operations are executed in accordance with the availability of assembly resources, and the assembly sequence; and (4) an automated and optimized scheduling system that integrates the developed master planning and scheduling methods. The system comprises three core components: (i) heuristic scheduling algorithms to automate master schedule generation; (ii) a self-adaptive, genetic algorithm-based multi-objective optimization algorithm designed to optimize key supply chain variables (e.g., project priorities); and (iii) a hybrid multiagent simulation that models supply chain operational constraints and applies the developed scheduling algorithms to evaluate and optimize different scheduling scenarios.

A prototype of the system is developed and validated through case studies from a panelized home prefabrication facility. Actual operational data are used to assess the effectiveness of the developed planning and scheduling methods and verify the system outputs. The results show that the methods coordinate supply chain operations to meet project delivery dates while adapting to dynamic operational needs and managing shared resources under varying conditions. The system identifies optimal scheduling parameters, including project priorities and the number of resources, to manage operations efficiently in terms of supply chain makespan and cost. For instance, a master schedule generated without optimization (i.e., using only the developed algorithms) reduces the supply chain makespan from 43 days in the actual schedule to 34 days, while optimization further reduces it to 29 days. Compared with the actual schedule, daily factory inventory decreases from the equivalent of 5 days of production capacity to approximately 1–2 days. This research advances the automation and optimization of supply chain master planning and scheduling in panelized construction by addressing coordination requirements for multi-line factories and incorporating practical considerations for transportation and onsite operations across multiple projects. The developed methods and system provide a scalable and adaptable solution for managing complex supply chain operations and is expected to improve supply chain management practices in offsite construction.