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Reinforcement Learning for Scheduling and Routing: Electric Vehicles Charging and Patrol Scheduling
Get PDFThis thesis offers a comprehensive exploration of Reinforcement Learning (RL), beginning with fundamental theoretical constructs, Markov Decision Processes, Dynamic Programming, Monte Carlo, and Temporal Difference methods, and extending into state-of-the-art deep RL approaches such as Deep Q-Networks (DQN) and policy-gradient algorithms. Through analytical experiments in controlled environments, the thesis demonstrates how distinct algorithmic choices (e.g., exploration techniques, eligibility traces, or network architectures) influence convergence and stability. These foundational insights pave the way for two in-depth case studies, which apply RL techniques to critical, real-world scheduling and routing challenges.
The first case study tackles the Electric Vehicle (EV) routing and charging problem, in which vehicles must navigate a road network populated with limited charging stations under battery and time constraints. To address this, first, a heuristic-based Q-learning (TQL) strategy that extends classical tabular Q-learning with temporal features of traffic and charging demands is proposed. A variant of deep Q-learning (DQL) is then introduced to accommodate larger-scale networks and continuous state parameters. Extensive simulations reveal that RL-based methods, particularly TQL, outperform baseline heuristics by reducing total travel distance and charging overhead. Notably, DQL demonstrates scalability, effectively handling more nodes and dynamic charging station placements, an essential factor for complex, real-time EV routing scenarios.
The second case study dives deeper into fleet management through a vehicle patrol scheduling problem where multiple patrol vehicles must repeatedly traverse a set of critical locations subject to diverse constraints, including patrol frequency, travel times, and emergency calls. This study thoroughly compares classical optimization (e.g., mixed integer linear programming), heuristic (e.g., genetic algorithms, adaptive hill-climbing), and RL-based approaches. Of particular interest is the proposed RL framework, which integrates a policy-based architecture with real-time decision-making to handle unpredictable emergency events and evolving traffic conditions. By combining RL and heuristic strategies, the system maintains efficient patrol coverage while dynamically reallocating vehicles to emergent hotspots. Simulation-based evaluations demonstrate significant improvements in response times, minimized travel distance, and balanced distribution of vehicle workloads, even under high uncertainty. In addition, sensitivity analyses examine how variations in patrol duration, rest times, and emergency rates impact the performance of each algorithm, providing actionable guidelines for deploying RL-driven patrol strategies in real-world settings.
By systematically integrating classical RL algorithms with modern deep learning architectures, this thesis reveals how an agent’s learning process can be significantly improved when critical constraints, such as energy or resource limitations, timing requirements, or rapidly changing system states, are carefully embedded into RL reward structures, state representations, and update mechanisms. Extensive experiments demonstrate that tabular approaches excel in managing smaller, more structured problems, whereas deep methods scale more effectively in complex domains with high-dimensional or continuous state spaces. Crucially, hybrid RL solutions bridge these strengths by incorporating domain-oriented heuristics, adaptive exploration, and targeted function approximation, resulting in faster convergence and more robust behavior in the presence of uncertainty. Collectively, these findings show that thoughtfully designed RL systems, whether tabular, deep, or hybrid, can quickly adapt to new conditions, consistently surpassing conventional heuristics or purely optimization-based methods while maintaining computational feasibility. Ultimately, this work underscores the transformative potential of RL for dynamic scheduling, routing, and resource management, illustrating a practical pathway to achieve higher-quality decision-making in real-world deployments
To Encounter the Other? How Journalists Make Catholic Faith-Based Organizations in Mexico and Russia (In)Visible
Get PDFCatholic Faith-Based Organizations (CFBOs) in Mexico and Russia are unique entities of study and comparison to understand the power dynamics between majority/minority institutional religions and their media representation. Although both countries recognize their religious diversity, Catholicism is still the major religion in Mexico, not only because of its large number of adherents but also its significant cultural and political capital. In contrast, in Russia, Catholicism is considered a minority religion and is often viewed as foreign to Russian values and traditions. These conditions influence how CFBOs have been featured in the news media in both countries. However, no single factors determine the news media visibility of CFBOs. Their (in)visibility is also shaped by the state’s project of the nation, conceptualizations of religious charity, perceptions on how the state and the church must interact in society, and beliefs of what amounts to good journalism in each country, to name a few.
By combining two different studies, a discourse content analysis of CFBOs in Mexican and Russian secular mainstream news media during the first 13 months of the COVID-19 pandemic, and 20 semi-structured online interviews with journalists in both countries, this dissertation explores how journalists working for non-religious mainstream news media in Mexico and Russia make CFBOs (in)visible. The point of departure is that what we see and how we see it depends on clusters of knowledge, or discourses, about how we understand the world around us at a particular period of time. Therefore, this dissertation understands visibility as a social construction where the objects of visualization are the CFBOs working in social assistance and operating in Mexico City or Moscow.
My research identifies that in both countries, journalists struggle with how to reconcile looking at a religious organization that, despite the good it does for assisting vulnerable groups, is identified with the Roman Catholic Church and its connotations in each country, while upholding their professional values of objectivity and pluralism. Moreover, the ambivalence of journalists to conceive CFBOs as equal to their counterparts, the secular NGOs, demonstrates that for journalists to make visible CFBOs, the state needs to mobilize discourses surrounding the collective care of people in need, and the role that civil society, including faith-based organizations, plays in social assistance
Individual Differences in the Evolution of Strategies in Spatial Problem Solving
Get PDFNavigating the world requires a strategy – you must choose which information to attend to and use from a wealth of internal and external cues in order to find your way. People have biases to prioritize some kinds of information over other. This is, in part, determined by individual differences in cognitive abilities. For instance, some individuals have a bias for spatial information that leads them to not only solve spatial tasks faster but are also more likely to use spatial information to solve tasks even when other information is available. However, peoples\u27 navigation strategies also evolve over time. Although variation in spatial strategies is well documented, it remains unknown if performance on an initial spatial task can predict strategy use over many days of repeated experience. We address this gap here. Participants were tested on mnemonic similarity and object-in-place tasks, common acute tasks of cognitive abilities relevant to navigation. Performance on these tasks was then compared with a virtual navigation task in which participants had to find a random set of objects placed throughout a virtual maze from varying starting points. Participants were shown a set route to each object, although shorter paths (i.e., shortcuts) were possible. This testing was repeated for 13 testing sessions to assess strategy switching over time. We found that performance on the object-in-place task but not the mnemonic similarity task predicted both accuracy and latency in the virtual navigation task. Importantly, object-in-place scores also predicted the rate at which participants deviated from the demonstrated path and adopted shortcuts during repeated virtual navigation. These data suggest long-term navigation strategy use is largely determined by general spatial memory abilities that can be identified in acute tests
“Your lads […] looked much like the reappearance of Jesus Christ to us”: Canada, the ICCS and the release of US Prisoners of War in Vietnam
Get PDFAbstract : Between January and July 1973, 240 Canadian military personnel took part in the International Commission for Control and Supervision (ICCS), responsible for monitoring the Paris Peace Agreements ending the American war in Vietnam. Canadian military observers notably supervised the release of 595 Americans prisoners of war (POWs) as well as over 32,000 Vietnamese captives. This article suggests that the ICCS, often viewed in the historiography as ineffective because of the numerous violations of the agreements, was much more successful in terms of monitoring US POW releases. In addition, it argues that helping the ally was a priority for the Canadian peace observers
Review of Horses, Howitzers, and Hymns: The Story of Lieut. Skey, MC, and his Father in the Great War by Marianne S. Goodfellow
Get PDFReview of Horses, Howitzers, and Hymns: The Story of Lieut. Skey, MC, and his Father in the Great War by Marianne S. Goodfello
Review of Sir John A. Macdonald & the Apocalyptic Year 1885 by Patrice Dutil
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Review of Silent Partners: The Origins and Influence of Canada’s Military Industrial Complex edited by Alex Souchen and Matthew S. Wiseman
Get PDFReview of Silent Partners: The Origins and Influence of Canada’s Military Industrial Complex edited by Alex Souchen and Matthew S. Wisema
Review of Blue Helmet Bureaucrats: United Nations Peacekeeping and the Reinvention of Colonialism, 1945-1971 by Margot Tudor
Get PDFReview of Blue Helmet Bureaucrats: United Nations Peacekeeping and the Reinvention of Colonialism, 1945-1971 by Margot Tudo
Review of “Pathway to The Stars: 100 Years of the Royal Canadian Air Force” by Michael Hood and Tom Jenkins
Get PDFReview of Pathway to The Stars: 100 Years of the Royal Canadian Air Force by Michael Hood and Tom Jenkin
“We’re Here, We’re Queer, and We’re Not Going Away” Student Policies and LGBTQ+ Students’ Experiences on Canadian Christian Campuses
Get PDFResearch highlights that Christian campuses are often unwelcoming and hostile spaces for lesbian, gay, bisexual, transgender, queer, and other gender and sexually diverse (LGBTQ+) students. Yet, research, including emerging Canadian research, often overlooks how institutional policies shape LGBTQ+ students’ experiences. Further, little attention is given to the affirming experiences LGBTQ+ students encounter on Christian campuses. Through a collective case study of two Canadian Christian universities involving document analysis and interviews, I explored the discourses that school policies constructed concerning gender and sexuality, LGBTQ+ students’ experiences of exclusion and affirmation, and how policy, alongside other factors, shapes their experiences and well-being. As well, I examined ways in which students’ identities were affirmed and their resilience. Findings show that while anti-LGBTQ+ policies contribute to discrimination and the gradual erosion of students’ wellbeing, students find meaningful instances of affirmation internally and from faculty, staff, and peers. The findings bring visibility to LGBTQ+ students’ experiences and highlight the importance of school community members affirming students’ LGBTQ+ identities in the context of exclusion