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Operations Research, Systems Engineering and Industrial Engineering Commons™
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Articles 1 - 9 of 9
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Identifying An Optimization Technique For Maker Usage To Address Covid-19 Supply Shortfalls, Michael J. Wilson
Identifying An Optimization Technique For Maker Usage To Address Covid-19 Supply Shortfalls, Michael J. Wilson
Doctoral Dissertations
Fused Deposition Modeling (FDM) can be purchased for under five hundred dollars. The availability of these inexpensive systems has created a large hobbyist (or maker) community. For makers, FDM printing is used numerous uses.
With the onset of the COVID-19 pandemic, the needs for Personal Protective Equipment (PPE) skyrocketed. COVID-19 mitigation strategies such as social distancing, businesses closures, and shipping delays created significant supply shortfalls. The maker community stepped in to fill gaps in PPE supplies.
In the case of 3DP, optimization remains the domain of commercial entities. Optimization is, at best, ad-hoc for makers. With the need to PPE …
Forecasting Nigeria's Electricity Demand And Energy Efficiency Potential Under Climate Uncertainty, Olawale Olabisi
Forecasting Nigeria's Electricity Demand And Energy Efficiency Potential Under Climate Uncertainty, Olawale Olabisi
Doctoral Dissertations
The increasing population and socio-economic growth of Nigeria, coupled with the current, unmet electricity demand, requires the need for power supply facilities expansion. Of all Nigeria’s electricity consumption by sector, the residential sector is the largest and growing at a very fast rate. To meet this growing demand, an accurate estimation of the demand into the future that will guide policy makers to adequately plan for the expansion of electricity supply and distribution, and energy efficiency standards and labeling must be made. To achieve this, a residential electricity demand forecast model that can correctly predict future demand and guide the …
Improving Reinforcement Learning Techniques For Medical Decision Making, Matthew Baucum
Improving Reinforcement Learning Techniques For Medical Decision Making, Matthew Baucum
Doctoral Dissertations
Reinforcement learning (RL) is a powerful tool for developing personalized treatment regimens from healthcare data. In RL, an agent samples experiences from an environment (such as a model of patient health) to learn a policy that maximizes long-term reward. This dissertation proposes methodological and practical developments in the application of RL to treatment planning problems.
First, we develop a novel time series model for simulating patient health states from observed clinical data. We use a generative neural network architecture that learns a direct mapping between distributions over clinical measurements at adjacent time points. We show that this model produces realistic …
Quality And Productivity Improvements In Additive Manufacturing, Huseyin Kose
Quality And Productivity Improvements In Additive Manufacturing, Huseyin Kose
Doctoral Dissertations
Additive manufacturing (AM) is a relatively new manufacturing technology compared to the traditional manufacturing methods. Even though AM processes have many advantages, they also have a series of challenges that need to be addressed to adapt this technology for a wide range of applications and mass production.
AM faces a number of challenges, including the absence of methods/models for determining whether AM is the best manufacturing process for a given part. The first study of this thesis proposes a framework for choosing specific AM processes by considering the complexity level of a part. It has been proven that the method …
Automated Warehouse Systems: A Guideline For Future Research, Wenquan Dong
Automated Warehouse Systems: A Guideline For Future Research, Wenquan Dong
Doctoral Dissertations
This study aims to provide a comprehensive tool for the selection, design, and operation of automated warehouse systems considering multiple automated storage and retrieval system (AS/RS) options as well as different constraints and requirements from various business scenarios.
We first model the retrieval task scheduling problem in crane-based 3D AS/RS with shuttle-based depth movement mechanisms. We prove the problem is NP-hard and find an optimality condition to facilitate the development of an efficient heuristic. The heuristic demonstrates an advantage in terms of solving time and solution quality over the genetic algorithms and the other two algorithms taken from literature. Numerical …
Optimization Of Islanded Utility-Microgrids After Natural Disasters, Rodney Kizito
Optimization Of Islanded Utility-Microgrids After Natural Disasters, Rodney Kizito
Doctoral Dissertations
Natural disasters can cause widespread disturbances/power outages within distribution networks and hinder a utility’s ability to provide uninterrupted power supply to the critical public buildings (e.g., hospitals, grocery stores, fire, police and gas stations) within the utility’s serviced region. Backup generators, which are typically relied on during power interruptions, have limited capacities and have been reported to experience failures during usage. Microgrids, defined as localized power grids that incorporate distributed generators (DGs) and energy storage systems (ESSs) to allow them to operate independent of the main grid (i.e., island mode), can help utilities provide disaster relief power supply to critical …
Optimal Communication Structures For Concurrent Computing, Andrii Berdnikov
Optimal Communication Structures For Concurrent Computing, Andrii Berdnikov
Doctoral Dissertations
This research focuses on communicative solvers that run concurrently and exchange information to improve performance. This “team of solvers” enables individual algorithms to communicate information regarding their progress and intermediate solutions, and allows them to synchronize memory structures with more “successful” counterparts. The result is that fewer nodes spend computational resources on “struggling” processes. The research is focused on optimization of communication structures that maximize algorithmic efficiency using the theoretical framework of Markov chains. Existing research addressing communication between the cooperative solvers on parallel systems lacks generality: Most studies consider a limited number of communication topologies and strategies, while the …
Models, Theoretical Properties, And Solution Approaches For Stochastic Programming With Endogenous Uncertainty, Tanveer Hossain Bhuiyan
Models, Theoretical Properties, And Solution Approaches For Stochastic Programming With Endogenous Uncertainty, Tanveer Hossain Bhuiyan
Doctoral Dissertations
In a typical optimization problem, uncertainty does not depend on the decisions being made in the optimization routine. But, in many application areas, decisions affect underlying uncertainty (endogenous uncertainty), either altering the probability distributions or the timing at which the uncertainty is resolved. Stochastic programming is a widely used method in optimization under uncertainty. Though plenty of research exists on stochastic programming where decisions affect the timing at which uncertainty is resolved, much less work has been done on stochastic programming where decisions alter probability distributions of uncertain parameters. Therefore, we propose methodologies for the latter category of optimization under …
Human Fatigue Predictions In Complex Aviation Crew Operational Impact Conditions, Suresh Rangan
Human Fatigue Predictions In Complex Aviation Crew Operational Impact Conditions, Suresh Rangan
Doctoral Dissertations
In this last decade, several regulatory frameworks across the world in all modes of transportation had brought fatigue and its risk management in operations to the forefront. Of all transportation modes air travel has been the safest means of transportation. Still as part of continuous improvement efforts, regulators are insisting the operators to adopt strong fatigue science and its foundational principles to reinforce safety risk assessment and management. Fatigue risk management is a data driven system that finds a realistic balance between safety and productivity in an organization. This work discusses the effects of mathematical modeling of fatigue and its …