LOUIS University of Alabama in Huntsville
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Modeling on-demand meal delivery with multi-modal couriers: considering both certain and uncertain orders
Get PDFThe on-demand meal delivery sector has experienced significant transformation, reshaping consumer dining habits and operational practices in the food delivery industry. This dissertation explores the changing landscape of on-demand meal delivery services through two interrelated topics, namely Modeling On-demand Meal Delivery in a Link-Node Based stochastic User Equilibrium with certain demand and Modeling On-demand Meal Delivery with different vehicle modes and uncertain demand, aiming to address the evolving challenges and opportunities in this dynamic industry. The rest of this dissertation is composed of two parts. The first part addresses the integration of internal restaurant couriers and third-party platform couriers to improve delivery efficiency and meet customer demands. A comprehensive on-demand meal delivery model is presented, which combines these two delivery methods. The model focuses on optimizing delivery operations by identifying strategies for hiring and managing couriers, minimizing travel costs, and maximizing customer satisfaction. It also considers constraints such as road network topology, time-dependent demand patterns, and restaurant strategies, offering a robust and adaptable solution for meal delivery operations. By integrating traffic information and utilizing the Stochastic User Equilibrium model to predict route choices, the model provides actionable insights for restaurants to enhance their delivery operations. The second part addresses the intricate challenges of on-demand meal delivery operations, including dynamic routing, multiple delivery courier modes, demand uncertainty, and bundling in an integrated delivery system. An advanced simulation optimization model is developed to incorporate all of these challenges and find the most efficient solution for them. This part further develops the envisions on the stochastic nature of real-world demand scenarios. By dynamically assigning delivery tasks to the most suitable courier based on factors such as workload, load capacity, and fuel consumption, the model optimizes delivery routes and resource utilization, ensuring efficient and effective service. Furthermore, the model has the potential to consider multiple delivery modes, such as electric vehicles, drones, conventional vehicles, bikes, and other types of courier modes, to improve operational efficiency, reduce costs, and minimize environmental impact. By integrating these innovative technologies into the model, a more sustainable and customer-centric approach to food delivery is promoted. Additionally, the model investigates the advantages of bundling orders and implementing an integrated delivery system for multiple restaurants. This strategy simplifies delivery logistics, improves coordination, and guarantees a consistent and high-quality service. Through rigorous analysis and the application of relevant models, this dissertation provides valuable insights and practical solutions for businesses in the food delivery industry to adapt to evolving market demands and maintain a competitive edge
Documenting Fantasy Playhouse: A University and Community Collaboration
Get PDFhttps://louis.uah.edu/rceu-hcr/1473/thumbnail.jp
Ambient temperature modelling with ECOSTRESS and private weather stations
Get PDFThis thesis explores the development and application of a novel data architecture for predicting ambient temperatures across US cities, focusing on integrating multi-source data i.e. ECOSTRESS land surface temperatures, urban surface properties, and crowdsourced weather data. The methodology is designed for scalability and adaptability across different urban regions, employing rigorous data quality control to enhance prediction accuracy. The validation of this model across diverse urban settings, demonstrated through rigorous RMSE comparisons and spatial mapping, validates its superiority over traditional models. Through experiments in diverse climatic conditions in Madison, Wisconsin, and Las Vegas, Nevada, the study assesses the model’s generalizability and effectiveness in capturing spatio-temporal temperature variations. This study aims to contribute to urban heat island mitigation and sustainable urban planning, setting a benchmark for future research in urban climatology
Microstructure control through the use of gas tungsten arc welding based additive manufacturing
Get PDFAdditively manufactured (AM) parts are subjected to rapid and repeated localized melting, solidification, and reheating, which produces a heterogeneous microstructure. To homogenize the resulting heterogeneous microstructure that results from this non-equilibrium thermal cycling, ex-situ heat treatments can be used. However, this is intended to provide homogenous properties to the printed part, thereby not realizing one of the potential benefits of AM in providing site-specific properties such as fatigue resistance, wear resistance or high strength. This study explores the use of in-situ heat treatments to spatially control microstructure evolution, thereby controlling the location of specific mechanical properties. To guide the processing parameters, numerical models were used to predict the thermal histories and the corresponding desired microstructures and properties. To achieve this goal, a Gas Tungsten Arc Welding (GTAW) based AM process has been designed and built for spatial control of microstructures in a carbon steel. The GTAW process is selected due to the ability to separate the heat flux from the material deposition, providing the ability for in-situ heat treatments
