Electronic Theses and Dissertations

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    Modeling and analysis of Regional Haul Steer (RHS) truck tire model
    (2024-04-01) Khosravi, Mehran; El-Gindy, Moustafa; El-Sayegh, Zeinab
    In this thesis, the RHS truck tire size 315/80R22.5 was developed using Finite Element Analysis and several material properties. The tire model was then validated using static and dynamic testing, against physical measurements provided by the manufacturer. A simulation model of flooded and snow terrain was then developed using the Smoothed-Particle Hydrodynamics technique and hydrodynamic elastic-plastic material model. The tire-terrain interaction characteristics were then evaluated over flooded and snow surfaces. The interaction characteristics included the rolling resistance, cornering force, self-aligning moment, and overturning moment. The analysis was performed at different operating conditions including terrain depth, longitudinal speed, and vertical loads. In general, the results from both surfaces exhibited similar trends, even though the values were not the same. Future work involves the utilization of genetic algorithms to generate semi-empirical relationships, as well as the implementation of temperature and wear models for the RHS tire.
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    Autonomous UAV-UGV robot collaboration for exploration and mapping of unknown environments
    (2024-04-01) Khabbaz, Noor; Nokleby, Scott
    This thesis addresses the limitations of existing approaches to autonomous exploration and mapping of unknown environments that use multiple Unmanned Ground Vehicles (UGVs). An Unmanned Aerial Vehicle (UAV) is introduced into the multirobot system to overcome the challenges of relative localization and obstacle detection. A novel method is proposed for autonomously determining the UGVs’ starting poses using ArUco markers visible to the UAV, resulting in the initialization of a global merged map. A second method is developed to overcome UGV obstacle detection limitations. UAV depth camera data is processed to detect and incorporate previously unseen obstacles into the UGVs’ navigation schemes, enabling avoidance. Experimental validation demonstrates the effectiveness of both methods in enhancing system autonomy. The integration of a UAV into multi-robot systems presents a promising solution to address UGVs’ localization challenges and limited field of view to improve their functionality in hazardous environments.
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    Characterization of apoptotic cell death in bovine red blood cells
    (2024-04-01) Kennedy, Bailey; Qadri, Syed
    Red blood cells (RBCs) in mammalian species display phenotypic variations in cellular functions and metabolism ascribed to their compositional and morphological differences. After accruing stress-induced damages, organelle-free human RBCs display a specialized apoptotic cell death process characterized by breakdown of normal phospholipid cell membrane architecture. This process facilitates swift phagocytic recognition and catabolism of apoptotic RBCs, which could significantly curtail their lifespan in circulation. Due to multiple phospholipid anomalies in bovine RBCs, their cell death machinery is not completely understood. Herein, we observed that bovine RBCs display differential cell death patterns in response to various pathophysiologic cell stressors in vitro as compared to human RBCs, which were only partly explained by increased Ca2+ influx and oxidative stress. In conclusion, premature cell death of circulating bovine RBCs could potentially contribute to the pathogenesis of anemia in cattle of varying etiology. The present observations may have relevance to livestock health and productivity.
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    Analysis of microgrid with renewable generation and energy storage system
    (2024-03-01) Jadeja, Prachal; Sood, Vijay
    The widespread integration of converter-fed renewable energy sources (RESs) and supported by energy storage systems (ESSs), along with their associated challenges, is causing a pressing need to reconsider the operation of distribution networks. Microgrids (MGs) appear as a practical solution to accommodate these RESs and ESSs. This thesis conducts an in-depth analysis of the design and modeling of a generic MG that integrates RESs and ESSs, incorporating a fuzzy logic controller (FLC)-based energy management system (EMS). The MG operation is evaluated in both grid-connected and islanded modes. A frequency analysis reveals that the point of common coupling (PCC) frequency is controlled within the operational range of ± 0.3 Hz, according to IEEE Standard I547. This control is achieved using a combination of battery and supercapacitor ESS in both operational modes. A comparative evaluation of Proportional Integrator (PI) v/s Proportional Resonant (PR) controllers for current controllers for the MG to reduce the amount of total harmonic distortion (THD) at PCC is conducted. Dynamic testing of MG is also conducted under various loading conditions, encompassing small-, medium-, and large-signal changes. The results prove that the PCC voltages are effectively kept within the typical range of ± 0.05 pu through the FLC-based EMS.
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    An investigation of the environmental factors that affect water quality and the occurrence of harmful cyanobacteria in stormwater management ponds
    (2024-04-01) Horton, Kaitlyn; Kirkwood, Andrea
    Stormwater management ponds (SWMP) have an important role in flood mitigation and basic water quality treatment via sedimentation. As aquatic ecosystems, less is known about their role as habitats for aquatic organisms and their potential to transform pollutants. This study focused on twelve SWMP in Oshawa, Ontario, Canada to assess the impacts of flow conditions and pond characteristics on SWMP water quality treatment and algal growth, including toxic cyanobacteria. Net release of total phosphorus (TP) and net retention of total nitrogen (TN) by SWMP were observed. Flow conditions had little affect on the overall functioning of the study SWMP. Large pond designs and recent dredging were observed to positively influence the reduction of total suspended solids (TSS), TN, and TP net release from SWMP. Algae and cyanobacteria were observed to be generally N-limited. The presence of the cyanobacterial toxin gene mcyE was positively associated with chloride and heterocyst-forming cyanobacteria.
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    Supporting registered early childhood educators through online collaborative professional practice discussions (CPPD project)
    (2024-04-01) Hope, Ashley; van Oostveen, Roland
    This study examines the digital competence and technology use of a small sample of Registered Early Childhood Educators (RECEs) in an attempt to gauge their readiness for fully online professional learning. A multi-phase mixed-methods approach was used to gather data on RECE digital practices. Findings suggest that fully online collaborative discussions meet RECEs' professional learning needs despite identified gaps in their digital awareness. Participant experiences highlight the importance of considering emotional intelligence, social interaction, technological awareness, and personal adaptability when designing fully online professional learning experiences for RECEs. The study positions fully online learning communities (FOLCs) as a solution to the logistical challenges of professional learning due to their ability to offer RECEs flexibility and continuous support. However, it is recommended that future investigations explore how FOLCs can support RECEs in completing their CPL portfolios to close the training and compensation gap in the sector.
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    Comprehensive integration of safety, optimization, and regulation in ¹⁷⁷Lu-based theranostic radiopharmaceuticals: from production to dose assessment
    (2024-03-01) Haghi, Pardis; Waller, Edward
    In recent decades, medical radioisotopes have transformed cancer diagnosis and treatment. The advent of theranostic radionuclides, enabling both diagnostic and targeted radiotherapy with a single radionuclide, merges two nuclear medicine fields, enhancing personalized targeted radiotherapy through internal dosimetry. Among these agents, the short-lived β-emitter ¹⁷⁷Lu is a promising theranostic agent for various oncological diseases. This research covers advanced production techniques, radiation safety, transportation, Canadian regulations, and internal dose assessment in radiopharmaceutical clinical applications. The study reviews regulations for obtaining a license from the Canadian Nuclear Safety Commission for a radiopharmaceutical therapy facility. It also examines internal dose assessment in nuclear medicine, explaining each step and providing an introduction to common software codes, along with a comparison of commercial software packages. Two case studies compare software codes and internal dose assessment results, emphasizing the ongoing challenge of determining accurate organ-absorbed doses in the RPT process. The ultimate goal is to comprehensively integrate key aspects of ¹⁷⁷Lu targeted therapy into a unified overview, bridging a crucial gap in understanding.
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    Advancing and expanding siRNA and saRNA therapeutics applications through chemical modifications
    (2024-04-01) Giorgees, Ifrodet; Desaulniers, Jean-Paul
    Oligonucleotides are short strands of DNA or RNA that are used to treat complex diseases like cancer and rare genetic diseases. They rely on biological pathways in our body to work. Two pathways that are important to this study are gene silencing and activation. Short interfering RNAs (siRNAs) silence genes, while short activating RNAs (saRNAs) activate them. Both types of strands can be used to create new cancer treatments. However, RNA-based therapies face challenges like instability, off-target effects, and low cell membrane permeability. To overcome these challenges, this study focuses on incorporating new chemical modifications into the RNA and assessing their impact on RNA activity. Our aim is to enhance RNA therapeutic efficacy for potential cancer therapy applications. The first goal focused on creating a combination therapy for cancer treatment by directly conjugating free base corrole molecules to siRNA. This novel construct created a combination therapy effect of gene silencing and simultaneous photodynamic therapy (PDT). This combination therapy is expected to be more targeted and non-invasive compared to traditional cancer treatments like surgery or chemotherapy. The second goal of this research involved exploring the potential of metal corrole molecules within siRNA for personalized cancer treatment. In this study, Ga-corrole was directly conjugated to siRNA, resulting in an advanced treatment consisting of live imaging and gene silencing. This novel construct created a new tool for siRNA real-time imaging applications that could potentially allow for real-time drug monitoring during cancer treatment. The third goal focused on discovering nuclease-resistant and active saRNAs targeting STING, which is a potential target for the treatment of solid tumors. In this study, a library of chemically modified saRNA was screened for their nuclease resistance ability and investigated for any potential correlations between chemical modifications, nuclease resistance and high gene upregulation activity. The results of nuclease stability assays revealed that the position of the chemical modifications within the RNA can significantly influence nuclease resistance. Furthermore, novel chemical modification designs were established for the synthesis of stable and highly active STING saRNA duplexes. In conclusion, this dissertation highlights novel approaches to enhance RNA therapeutics and employ RNA molecules for cancer drug monitoring or treatment applications.
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    A Graph Neural Network for pairwise surrogate modeling in population-based algorithms with tournament selection
    (2024-04-01) Gharavian, Vida; Makrehchi, Masoud; Rahnamayan, Shahryar
    Optimization problems widely arise in various science and engineering fields. Optimisation involves evaluating a candidate solution, which can be computationally intensive. Machine learning-based surrogate models can contribute to learning the specific pattern among the decision variables and objective values to reduce the computation time of fitness evaluation. In this study, we have proposed a novel pairwise surrogate model to identify the superiority between candidate solutions in a pairwise comparison. We demonstrated a Graph Neural Network (GNN) to be trained on number of pairs, then utilized to compare a pair of candidate solutions. To examine the efficacy of our model, we utilized the surrogate model on CEC2017 benchmarks in different dimensions. Moreover, the result of surrogate-assisted and none-assisted form of two well-known optimization algorithms were compared. Results show that the proposed method can significantly reduce the computing cost. In the presence of higher dimensions, our model is more effective than most surrogate models for comparison-based optimizers.
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    Requirements engineering-driven collaborative software maintenance framework for embedded systems using continual learning
    (2024-04-01) Fariha, Asma; Alwidian, Sanaa; Azim, Akramul
    Embedded software post-deployment evolutions pose significant threats to the safety and reliability of embedded software if it is not adapted to software maintenance through requirements engineering. To solve this problem, we propose a collaborative framework that enables efficient requirements elicitation and continuously integrates it into maintenance. We designed a requirements forum to enhance elicitation through centralized stakeholder collaboration. This study investigated fault and failure detection in the maintenance phase with continual learning as a mechanism of incremental inclusion. The novel CNNBiLSTM deep-learning model on a public drone dataset outperformed state-of-the-art models, achieving a 100% true positive rate in three scenarios. On the other hand, we experienced a 14% increase in the recall metric for the replay-based method combined with pre-training compared to pre-training when fault detection requirements were integrated incrementally. Our findings support the idea that embedded software safety and security can be greatly enhanced through this collaborative framework.
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    Robust nonlinear controller for single ended primary inductor converter supplying shared load from multiple sources with asymmetrical dynamics
    (2024-04-01) El Haj, Youssef; Sood, Vijay; Milman, Ruth
    This work proposes a systematic approach to design a novel Integral Sliding Mode Controller (ISMC) for a Single-Ended Primary-Inductor converter (SEPIC) with dynamic load sharing. The designed sliding surface is used to connect and control two different input-energy sources via two SEPICs to drive a parallel-connected load with a fixed or dynamic autotuned sharing ratio. This structure enables to maintain battery health and extend its life. This proposal provides a solution that is scalable to the power system industry where there is a need to integrate other energy sources to the main power network; the proposed controller can function with sources having different dynamics and varying voltage levels with respect to the main network. Furthermore, the work also contributes to the field of control theory by deriving and designing a SMC for a SEPIC converter with only one parameter to tune where the upper and lower bounds are derived. The designed surface results in a minimal chattering behaviour at the output voltage as well as at the duty cycle level and allows for operating the SEPIC at a fixed switching frequency. The proposed controller can withstand up to a 75% variation in the input voltage, 100% variation on the load side in addition to providing a superior cold start performance. The proposed controller is nonlinear and has a variable structure; these features suit the SEPIC converter which is based on switching behaviour. The controller’s ability to reject input voltage disturbances which vary over a wide range is a key to integrating alternative energy sources (such as an ultracapacitor) to the main power network. Finally, the work demonstrates how the proposed sliding surface can be modified to drive two parallel converters to dynamically share load current where the current shared ratio is autotuned during the transient period while at steady-state it follows a pre-set shared ratio.
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    A few-shot learning method for single-object visual anomaly detection
    (2024-04-01) Ejaz, Neha; Qureshi, Faisal
    We propose a few-shot learning method for visually inspecting single objects in an industrial setting. The proposed method is able to identify whether or not an object is defective by comparing its visual appearance with a small set of images of the “working” object, i.e., the object that passes the visual inspection. The method does not require images of defective objects. Furthermore, the method does not need to be “trained” when used to inspect new, previously unseen, objects. This suggests that the method can be easily deployed in industrial settings. We have evaluated the method on three visual anomaly detection benchmarks—1) MVTec, 2) MPDD, and 3) VisA. On the first two datasets the proposed method achieves performance that is comparable to state-ofthe- art methods that require access to object-specific training data. Model performance on VisA is poor; however, it is to be noted that the model was never trained on VisA dataset. We also show that the proposed model boasts fast inference times, which is a plus for industry applications. This project is funded in part by Axiom Plastics Inc., and we have evaluated the proposed method on a proprietary dataset provided by Axiom. The results confirm that the proposed method is well-suited for single-object visual anomaly detection in industry settings.
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    Heart rate variability (HRV) as a predictor of anemia in premature infants using Artemis
    (2024-02-01) Desai, Rachit; McGregor, Carolyn
    Premature infants often receive multiple blood transfusions within the first few weeks of life because of their physiological needs. Anemia is a major contributor to the need for transfusions in premature infants, and current detection practices rely on laboratory testing of blood samples. This thesis introduces a Clinical Decision Support System (CDSS) framework that utilizes high frequency streaming physiological data and laboratory information for clinical insights through visual analytics. The framework leverages the Artemis platform, a Big Data and Artificial Intelligence based CDSS, by exploring relationships between blood transfusions and heart rate variability (HRV). Using Artemis, this thesis aimed to identify patterns in HRV to enable non-invasive detection of physiologically significant anemia through data visualization. This work contributes to health informatics by presenting an integrated CDSS framework and to laboratory sciences by demonstrating the potential of laboratory data integration for non-invasive anemia detection.
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    Neuroimaging metrics of externalizing disorders: assessing neurobiological features of substance use, withdrawal, substance use disorders, and psychopathy
    (2024-05-01) Denomme, William James; Shane, Matthew
    Neuroimaging research has provided several insights into the neurobiological correlates of externalizing features, notably substance use, substance use disorders, and antisociality. For instance, researchers have paired neuroimaging metrics with machine learning (ML) algorithms to classify externalizing patients from controls and externalizing patients with varying severity and prognoses. In addition, studies have used neural reactivity to drug and food rewards to separate cocaine-dependent participants from non-dependent controls, as well as cocaine-dependent with and without a history of withdrawal symptoms and varying degrees of historical cocaine use and psychopathic traits. However, variability in the classification accuracy of ML models precludes inferences of how well neuroimaging metrics can distinguish externalizing patients and controls. Moreover, variability in the classification accuracy of ML models and the lack of work using modalities outside of cue-reactivity preclude sound inferences on how well neuroimaging can distinguish subgroups of externalizing patients. This dissertation consists of three studies to address these factors. In Study 1, a meta-analysis of 49 ML models with neuroimaging predictors demonstrated that neuroimaging metrics could distinguish externalizing patients and controls with an accuracy of ~80%. Study 1 also demonstrated a classification accuracy of ~79% when distinguishing externalizing patients with severity and prognosis differences. However, it is important to note that most studies included in this meta-analysis validated their results using cross-validation, which may have inflated their classification accuracy. Next, Studies 2 and 3 demonstrated that cocaine-dependent participants were distinct from non-dependent controls in terms of gray matter concentration (GMC) or functional connectivity (FNC) in response to drug and food rewards within the orbitofrontal cortex, middle temporal gyrus, dorsolateral prefrontal cortex, middle frontal gyrus (MFG), and anterior cingulate cortex. These studies also found that GMC and FNC within several corticolimbic regions, notably the MFG, separated cocaine-dependent participants with and without a history of withdrawal and varying degrees of lifetime history of cocaine use and psychopathic traits. These results provide preliminary evidence that neuroimaging metrics could distinguish externalizing patients from control, and separate externalizing patients that are subgrouped by symptomology, severity, and prognosis. The presented work has substantial implications for developing novel assessment protocols and optimal treatment strategies.
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    The relationship between ratings of perceived exertion, training load, and types of deliberate practice in ice hockey training
    (2024-04-01) Csiernik, Ben; Wattie, Nick
    Ratings of perceived exertion (RPE) are used in team sport settings to measure the perceived exercise intensity of training. Coaches may use measures of RPE as a method to monitor training load to ensure athletes are being sufficiently challenged without overtraining. However, limited published evidence exists evaluating the use of differential RPE (dRPE) in ice hockey. Further, there is a need to understand the impact of practice design on training, and if coaches and athletes perceive the demands of training equally. This study aimed to evaluate multiple domains of training in collegiate women’s ice hockey. Specifically, this study examined the relationship between RPE and dRPE, the impact of practice microstructure on RPE, coach-athlete RPE congruency, and a descriptive approach to training load. Results suggest that dRPE significantly explains the variance seen in RPE, that coaches and players show strong congruence, and that more research is required on practice microstructure.
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    Characterizing midair handwriting in virtual reality
    (2024-04-01) Chan, Matthew; Collins, Christopher; Qureshi, Faisal
    Midair handwriting poses challenges due to the lack of a physical plane to press against while writing, making it difficult to determine when ink should be placed. In this thesis, we gathered midair handwriting data from 24 participants in an environment that allowed them to write freely. We compared writing with a pen-like object and writing using a finger across two writing methods (writing freely versus on a virtual whiteboard). Using our data, we trained a neural network to detect when ink should be placed during midair handwriting, achieving an overall 85% accuracy. We developed a data-viewing application to recreate sentences for visual analysis. Participant feedback favoured the pen-like object as a writing utensil, with equal preference for both writing methods. Our contributions include a midair handwriting Virtual Reality (VR) application for data collection, a dataset containing 480 sentences of frame-by-frame midair handwriting data, and 20 unique prompts used in participant trials.
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    Enhancing early mathematical professional learning with fully online learning communities (FOLC) and collaborative action research
    (2024-02-01) Branch, Jessie; van Oostveen, Roland
    Early math skills are fundamental to children’s development and significantly impact later academic success. The purpose of the study was to examine how collaborative action research (CAR) in fully online learning community (FOLC) environments affects RECEs’ early mathematical knowledge and instruction. The study used a mixed-methods approach consisting of the phase one surveys and phase two workshop and interview sessions to seek evidence related to the research questions. The surveyed RECEs possessed average digital competence concerning processes congruent with everyday practices. Findings suggest CAR as a potential mechanism for RECE professional learning as it provides opportunities to connect with others on issues related to one’s practice. Additionally, FOLC environments, emphasizing equitable, learner-centred experiences, provided participants with an accessible space for discussing authentic problems and solutions to achieve transformative learning.
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    Design and investigation of renewable natural gas and methane production systems
    (2024-04-01) Bolt, Andre; Dincer, Ibrahim; Agelin-Chaab, Martin
    This thesis presents the design and investigation of renewable natural gas and methane production systems. The thesis comprises a theoretical and an experimental portions. The experimental portion of the thesis includes the design, construction, and experimental testing of a new helical fixed-bed reactor, as well as a production system to support and monitor the reactor. Additionally, the experimental system integrates gas bending and the recycling of the coolant working fluid to pre-heat the reactant molecules prior to entering the reactor. The experimental tests include studying the effects of pressure variability at the inlet of the reactor, variations in the reactor starting temperature, and variations in the mole ratio between reactants. Most notably, the system is able to achieve a maximum CH4 production rate of 10.61 L/h. This equates to overall energy and exergy efficiencies of 13.36% and 12.46%, respectively. However, during the simulation aspect of the thesis, computational fluid dynamics (CFD) analyses are conducted. These analyses consider the design of four unique fixed-bed natural gas reactor concepts. Additionally, each of the reactor concepts is presented as having three unique configurations. The analyses show that Concept 4’s helical reactor design presents the greatest potential to mitigate elevations in the reactors’ temperature due to more of its surface area being exposed compared to the other reactor concepts. Additionally, Configuration 1 of Concept 4 is able to achieve a yield of 86.7%. The theoretical portion also investigates four novel multigeneration systems capable of synthesizing natural gas while simultaneously producing several useful outputs. System 1 considers a target location of Alberta, Canada, and uses biomass and solar energy as its source. The system achieves energy and exergy efficiencies of 61.0% and 28.6%, respectively, during heating mode. The other three multigeneration systems reduce CO2 emissions from industries that produce substantial amounts of greenhouse gas emissions (cement, steel, and glass industries), through the integration of chemical absorption techniques, the CO2 that is extracted can be used to synthesize CH4 renewably. These systems also harness solar, wind, hydro, and tidal as the energy sources.
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    Exploring flipped classroom instructional design in health sciences
    (2024-04-01) Banks, Laura; Kay, Robin
    The effectiveness of the flipped classroom in undergraduate studies has been studied, however, limited research has been conducted on instructional design. This study explored student satisfaction related to the instructional design of a flipped classroom in human anatomy and physiology courses at Ontario Tech University. One hundred forty-six students enrolled in first- and second-year courses completed three surveys on their flipped classroom experiences. Most survey respondents positively perceived the asynchronous lecture videos, asynchronous lecture video interactions, and flipped synchronous classroom activities. A large effect size was observed, with Year 2 students reporting significantly higher satisfaction than Year 1 students in viewing pre-recorded lecture episodes, lecture episode quantity, and duration. Differences between Year 1 and Year 2 students may be rooted in cognitivism, social constructivism, cognitive load, and metacognition principles. Future research is warranted to explore the influence of other demographic variables on student satisfaction in a flipped classroom design.
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    A novel approach for route generation and real-time scheduling for public services
    (2024-04-01) Baghyari, Farhad; Seo, Jaho
    Snowplowing and sweeping are essential services to municipalities, which affect travel safety, environment protection, and health to residents. To provide acceptable quality services, route optimization is one of the key strategies that allow for enhancing efficiency, saving costs, and balancing workloads among operational teams. In order to address this issue and reflect on recent research trends in routing problems that require variable conditions and real-time events, this study proposes two heuristic methods: Smart Selective Navigator and a two-stage algorithm for real-time scheduling and route generation. Through two major case studies — winter operations in the City of Oshawa and autonomous street sweeping in Uchi Park —the proposed methods demonstrate superior performance in generating optimal routes that satisfy complex constraints such as turn restrictions and supply limits and handle real-time events like vehicle breakdowns.