Matching Items (11)
Description
The crew planning problem in the airline industry presents a very computationally complex problem of high importance to the business. Airlines must schedule crew members to ensure that all flights are staffed while remaining in compliance with the business needs and regulatory requirements set by entities such as unions and FAA. With the magnitude of operation of the prominent players in the airline industry today, the crew staffing problem proves very large and has become heavily reliant on operations research solution methodologies. An area of opportunity that has not yet been extensively researched lies in the planning of crew vacation. This paper develops a model driven by the idea of system risk that constructs an optimal vacation grid for the time period of one year. The model generates a daily allocation that maximizes vacation offering while ensuring a given level of system reliability. The model is then implemented using data from US Airways and model improvements are provided for practical application in the airline industry based on the output.
ContributorsFisher, Tignes Noel (Author) / Gel, Esma (Thesis director) / Jacobs, Tim (Committee member) / Clough, Michael (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor) / Department of Supply Chain Management (Contributor)
Created2015-05
Description
Simulation games are widely used in engineering education, especially for industrial engineering and operations management. A well-made simulation game aids in achieving learning objectives for students and minimal additional teaching by an instructor. Many simulation games exist for engineering education, but newer technologies now exist that improve the overall experience of developing and using these games. Although current solutions teach concepts adequately, poorly-maintained platforms distract from the key learning objectives, detracting from the value of the activities. A backend framework was created to facilitate an educational, competitive, participatory simulation of a manufacturing system that is intended to be easy to maintain, deploy, and expand.
ContributorsChandler, Robert Keith (Author) / Clough, Michael (Thesis director) / Pavlic, Theodore (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
At Arizona State University, the Disability Resource Center provides disabled students transportation around campus. This transportation service known as DART is composed of approximately 20 student workers and 9 carts that pick riders up based on pre-determined pick-up times and locations. With the current system, the scheduling of drivers to riders is inefficient, and in response, a tool was developed to schedule the rides in a faster manner. A demonstration of the new tool resulted in a time reduction of 98%.
ContributorsFranke, Alexandra Nicole (Author) / Clough, Michael (Thesis director) / Jennings, Cheryl (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
The efficient refurbishment of rotable parts on an aircraft proves to be a main concern for airline carriers today. Airlines must be able to seamlessly rotate parts into and out of the system for maintenance in accordance with FAA requirements while leaving daily operations uninterrupted. In this paper, we develop an airline maintenance scheduling model that constructs an optimal schedule for part maintenance over a given time horizon using deterministic forecasting techniques. The model generates a schedule that minimizes the total cost of a maintenance schedule solution while maximizing the utility of all parts in the system. The model is then tested against actual network data of three part types crucial to airline operations and used to investigate the current data collection processes of US Airways maintenance lead time metrics. Manual sensitivity analysis is performed to generate the marginal value of each parameter and potential model extensions are highlighted as a result of these conclusions.
ContributorsDunham, Nicole Elizabeth (Author) / Gel, Esma (Thesis director) / Jacobs, Timothy (Committee member) / Clough, Michael (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2013-12
Description
The listing price of residential rental real estate is dependent upon property specific attributes. These attributes involve data that can be tabulated as categorical and continuous predictors. The forecasting model presented in this paper is developed using publicly available, property specific information sourced from the Zillow and Trulia online real estate databases. The following fifteen predictors were tracked for forty-eight rental listings in the 85281 area code: housing type, square footage, number of baths, number of bedrooms, distance to Arizona State University’s Tempe Campus, crime level of the neighborhood, median age range of the neighborhood population, percentage of the neighborhood population that is married, median year of construction of the neighborhood, percentage of the population commuting longer than thirty minutes, percentage of neighborhood homes occupied by renters, percentage of the population commuting by transit, and the number of restaurants, grocery stores, and nightlife within a one mile radius of the property. Through regression analysis, the significant predictors of the listing price of a rental property in the 85281 area code were discerned. These predictors were used to form a forecasting model. This forecasting model explains 75.5% of the variation in listing prices of residential rental real estate in the 85281 area code.
ContributorsSchuchter, Grant (Author) / Clough, Michael (Thesis director) / Escobedo, Adolfo (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Most staff planning for airline industries are done using point estimates; these do not account for the probabilistic nature of employees not showing up to work, and the airline company risks being under or overstaffed at different times, which increases costs and deteriorates customer service. This model proposes utilizing a stochastic method for American Airlines to schedule their ground crew staff. We developed a stochastic model for scheduling that incorporates the risks of absent employees and as well as reliability so that stakeholders can determine the level of reliability they want to maintain in their system based on the costs. We also incorporated a preferences component to the model in order to increase staff satisfaction in the schedules they get assigned based on their predetermined preferences. Since this is a general staffing model, this can be utilized for an airline crew or virtually any other workforce so long as certain parameters about the population can be determined.
ContributorsOtis, Matthew (Co-author) / Reyes, Katherine (Co-author) / Gel, Esma (Thesis director) / Jacobs, Tim (Committee member) / Clough, Michael (Committee member) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This thesis was conducted to study and analyze the fund allocation process adopted by different states in the United States to reduce the impact of the Covid-19 virus. Seven different states and their funding methodologies were compared against the case count within the state. The study also focused on development of a physical distancing index based on three significant attributes. This index was then compared to the expenditure and case counts to support decision making.
A regression model was developed to analyze and compare how different states case counts played out against the regression model and the risk index.
ContributorsJaisinghani, Shaurya (Author) / Mirchandani, Pitu (Thesis director) / Clough, Michael (Committee member) / McCarville, Daniel R. (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Department of Information Systems (Contributor) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This review explores popular gambling strategies often believed to guarantee wins, such as card counting and taking advantage of arbitrage. We present a mathematical overview of these systems to evaluate their theoretical effectiveness in ideal conditions by presenting prior research and mathematical proofs. This paper then generates results from these models using Monte Carlo simulations and compares them to data from real-world scenarios. Additionally, we examine reasons that might explain the discrepancies between theoretical and real-world results, such as the potential for dealers to detect and counteract card counting. Ultimately, although these strategies may fare well in theoretical scenarios, they struggle to create long-term winning solutions in casino or online gambling settings.
ContributorsBoyilla, Harsha (Author) / Clough, Michael (Thesis director) / Eikenberry, Steffen (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05
Description
When the Covid-19 virus began to spread worldwide in the spring of 2020, life across the globe changed rapidly. Government restrictions required people to move into prolonged social isolation and the resulting impact of this isolation on mental health has been profound (Brooks et al., 2020). Initial research has begun to be conducted to understand the scope of how social isolation and the pandemic have influenced people’s psychological well-being. Thus far there has been a marked increase in depression and anxiety disorders (Eyice Karabacak et al., 2021). Agoraphobia is an anxiety disorder that is characterized by fear and avoidance of certain settings. Undoubtedly, fear and avoidance of certain settings during the pandemic cannot be considered abnormal behavior as it is in line with a normative response to such a threatening phenomenon.
This study investigates how normative avoidance of certain settings during the pandemic may have now evolved into fear of the settings themselves. In particular, it tested if the start of the pandemic is associated with the prevalence rate of Agoraphobia. This was done via a retrospective study that uses self-report data to examine whether participants presented symptoms of Agoraphobia prior to the start of the pandemic and if they currently exhibit symptoms of Agoraphobia. In addition to the study, the website designed to host this study also provided mental health resources to participants, in light of the documented increase in mental illness since the start of the pandemic (Brooks et al., 2020).
ContributorsPeterson, Bria (Author) / Franke, Jillian (Co-author) / Clough, Michael (Thesis director) / Lanphier, Erin (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2022-05
Description
When the Covid-19 virus began to spread worldwide in the spring of 2020, life across the globe changed rapidly. Government restrictions required people to move into prolonged social isolation and the resulting impact of this isolation on mental health has been profound (Brooks et al., 2020). Initial research has begun to be conducted to understand the scope of how social isolation and the pandemic have influenced people’s psychological well-being. Thus far there has been a marked increase in depression and anxiety disorders (Eyice Karabacak et al., 2021). Agoraphobia is an anxiety disorder that is characterized by fear and avoidance of certain settings. Undoubtedly, fear and avoidance of certain settings during the pandemic cannot be considered abnormal behavior as it is in line with a normative response to such a threatening phenomenon. This study investigates how normative avoidance of certain settings during the pandemic may have now evolved into fear of the settings themselves. In particular, it tested if the start of the pandemic is associated with the prevalence rate of Agoraphobia. This was done via a retrospective study that uses self-report data to examine whether participants presented symptoms of Agoraphobia prior to the start of the pandemic and if they currently exhibit symptoms of Agoraphobia. In addition to the study, the website designed to host this study also provided mental health resources to participants, in light of the documented increase in mental illness since the start of the pandemic (Brooks et al., 2020).
ContributorsFranke, Jillian (Author) / Peterson, Bria (Co-author) / Clough, Michael (Thesis director) / Lanphier, Erin (Committee member) / Barrett, The Honors College (Contributor) / Civil, Environmental and Sustainable Eng Program (Contributor)
Created2022-05