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Operations Research, Systems Engineering and Industrial Engineering Commons™
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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Exact Models, Heuristics, And Supervised Learning Approaches For Vehicle Routing Problems, Zefeng Lyu
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 …
Assessing And Predicting The Students’ Systems Thinking Preference: Multi-Criteria Decision Making And Machine Learning, Siham Tazzit
Assessing And Predicting The Students’ Systems Thinking Preference: Multi-Criteria Decision Making And Machine Learning, Siham Tazzit
Theses and Dissertations
The 21st century is marked by a technological revolution that features digital implementation and high interconnectivity between systems across different domains, such as transportation, agriculture, education, and health. Although these technological changes resulted in modern systems capable of easing individuals’ lives, these systems are increasingly complex, and that increased complexity is only expected to continue. The increased system complexity is due to the rapid exchange of information between subsystems, which creates high interconnectivity and interdependence between the subsystems and their elements. Workforce skill sets, as a result, must be modified appropriately to ensure the systems’ success. Systems Thinking is an …
Improving Mobility And Safety In Traditional And Intelligent Transportation Systems Using Computational And Mathematical Modeling, Shahrbanoo Rezaei
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 …
Understanding And Simulating Wildfire Changes Using Advanced Statical And Process-Oriented Models, Rongyun Tang
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 …
Machine Learning For Ultraviolet Spectral Prediction, Linh Ho Manh
Machine Learning For Ultraviolet Spectral Prediction, Linh Ho Manh
Industrial, Manufacturing, and Systems Engineering Dissertations
Machine Learning has found wide applications in material science, including dielectric polymers, superconducting materials, and drug property prediction. The use of data analytics and machine learning methods to predict Vacuum Ultraviolet (VUV) spectra by encoding molecular structure is gaining interest because high-quality VUV spectral prediction capability would enable the study of new molecules without costly wet-lab measurements. This dissertation aims to study feature representations for molecular structures that enhance the prediction of VUV spectra via machine learning models. Both interpretable machine learning and deep learning are studied. Chapter 1 provides an overview of VUV/UV spectra retrieval, and Chapter 2 reviews …
Reducing Restaurant Inventory Costs Through Sales Forecasting, Tyler Mason, Chris Schoen, Trevor Gilbert, Jonathan Enriquez
Reducing Restaurant Inventory Costs Through Sales Forecasting, Tyler Mason, Chris Schoen, Trevor Gilbert, Jonathan Enriquez
Senior Design Project For Engineers
Family Restaurant is a local restaurant in the greater Atlanta area that serves a variety of dishes that include an assortment of 19 different proteins. Currently, Family Restaurant places protein orders based on business intuition, and tends to over-stock and sometimes under-stock. To minimize inventory costs by reducing over-stocking and preventing under-stocking of proteins, we applied Facebook Prophet (FB Prophet), ARIMA, and XG Boost machine learning models to predict protein demand and then fed these results into a Fixed Time Period inventory model to make an overall order suggestion based on the specified time period. We trained our models on …