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Full-Text Articles in Engineering

Exact Models, Heuristics, And Supervised Learning Approaches For Vehicle Routing Problems, Zefeng Lyu Dec 2023

Exact Models, Heuristics, And Supervised Learning Approaches For Vehicle Routing Problems, Zefeng Lyu

Doctoral Dissertations

This dissertation presents contributions to the field of vehicle routing problems by utilizing exact methods, heuristic approaches, and the integration of machine learning with traditional algorithms. The research is organized into three main chapters, each dedicated to a specific routing problem and a unique methodology. The first chapter addresses the Pickup and Delivery Problem with Transshipments and Time Windows, a variant that permits product transfers between vehicles to enhance logistics flexibility and reduce costs. To solve this problem, we propose an efficient mixed-integer linear programming model that has been shown to outperform existing ones. The second chapter discusses a practical …


Improving Mobility And Safety In Traditional And Intelligent Transportation Systems Using Computational And Mathematical Modeling, Shahrbanoo Rezaei Aug 2023

Improving Mobility And Safety In Traditional And Intelligent Transportation Systems Using Computational And Mathematical Modeling, Shahrbanoo Rezaei

Doctoral Dissertations

In traditional transportation systems, park-and-ride (P&R) facilities have been introduced to mitigate the congestion problems and improve mobility. This study in the second chapter, develops a framework that integrates a demand model and an optimization model to study the optimal placement of P&R facilities. The results suggest that the optimal placement of P&R facilities has the potential to improve network performance, and reduce emission and vehicle kilometer traveled. In intelligent transportation systems, autonomous vehicles are expected to bring smart mobility to transportation systems, reduce traffic congestion, and improve safety of drivers and passengers by eliminating human errors. The safe operation …


Advanced Air Quality Management With Machine Learning, Cheng-Pin Kuo May 2023

Advanced Air Quality Management With Machine Learning, Cheng-Pin Kuo

Doctoral Dissertations

Air pollution has been a significant health risk factor at a regional and global scale. Although the present method can provide assessment indices like exposure risks or air pollutant concentrations for air quality management, the modeling estimations still remain non-negligible bias which could deviate from reality and limit the effectiveness of emission control strategies to reduce air pollution and derive health benefits. The current development in air quality management is still impeded by two major obstacles: (1) biased air quality concentrations from air quality models and (2) inaccurate exposure risk estimations

Inspired by more available and overwhelming data, machine learning …


Improved Spatial Resolution For Double-Sided Strip Detectors Using Lithium Indium Diselenide Semiconductors, Jake Alexander Gallagher May 2023

Improved Spatial Resolution For Double-Sided Strip Detectors Using Lithium Indium Diselenide Semiconductors, Jake Alexander Gallagher

Doctoral Dissertations

This research focuses on the evaluation of lithium indium diselenide (LISe) semiconductors in double-sided strip detector (DSSDs) designs as an example for the potential to achieve unparalleled neutron detection efficiency, spatial resolution, and timing resolution detection. LISe semiconductors offer high neutron detection efficiency due to the ~25% atomic ratio of Lithium-6, maximizing its efficiency of ~75% with 1 mm thickness at 2.8 angstroms. Furthermore, the 4.78 MeV 𝑄-value enables high intrinsic gamma discrimination in a pixelated design (electron range). These characteristics make LISe an alternative option for neutron radiography, energy-resolved imaging, and other neutron interrogation techniques. This dissertation summarizes my …


Understanding And Simulating Wildfire Changes Using Advanced Statical And Process-Oriented Models, Rongyun Tang May 2023

Understanding And Simulating Wildfire Changes Using Advanced Statical And Process-Oriented Models, Rongyun Tang

Doctoral Dissertations

This study aims to investigate the spatiotemporal dynamic of global wildfires, their underlying climate-driving mechanisms, and their predictability by utilizing multiple data sources (both process-based model simulations and satellite-based observations) and multiple analytical methods including machine learning techniques (MLTs).

We first explored the global wildfire interannual variability (IAV) and its climate sensitivity across nine biomes from 1997 to 2018, leveraging the state-of-art U.S. Department of Energy’s Energy Exascale Earth System Model (E3SM) land component (ELM-v1) simulations with six sets of climate forcings. Results indicate that 1) ELM simulations could reproduce the IAV of wildfire in terms of magnitudes, distribution, bio-regional …