Matching Items (26)

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A Reliability Driven Model for Airline Crew Vacation Grid Optimization

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

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.

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  • 2015-05

Data Analytics to Identify the Genetic Basis for Resilience to Temperature Stress in Soybeans

Description

This paper explores the ability to predict yields of soybeans based on genetics and environmental factors. Based on the biology of soybeans, it has been shown that yields are best

This paper explores the ability to predict yields of soybeans based on genetics and environmental factors. Based on the biology of soybeans, it has been shown that yields are best when soybeans grow within a certain temperature range. The event a soybean is exposed to temperature outside their accepted range is labeled as an instance of stress. Currently, there are few models that use genetic information to predict how crops may respond to stress. Using data provided by an agricultural business, a model was developed that can categorically label soybean varieties by their yield response to stress using genetic data. The model clusters varieties based on their yield production in response to stress. The clustering criteria is based on variance distribution and correlation. A logistic regression is then fitted to identify significant gene markers in varieties with minimal yield variance. Such characteristics provide a probabilistic outlook of how certain varieties will perform when planted in different regions. Given changing global climate conditions, this model demonstrates the potential of using data to efficiently develop and grow crops adjusted to climate changes.

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  • 2018-05

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Simulating The Performance of Various Revenue Managment Engines

Description

Revenue management (RM) attempts to understand and shape consumer behavior to maximize revenue from a perishable resource. Various algorithms can be used to control bid-prices, and subsequently, perform differently with

Revenue management (RM) attempts to understand and shape consumer behavior to maximize revenue from a perishable resource. Various algorithms can be used to control bid-prices, and subsequently, perform differently with respect to the total network revenue that they generate. There is currently a need for some method to compare RM engines; a simulation can fulfill this need.

The first module of this thesis will create a statistically accurate representation of customers arriving at ticket purchasing channels. Each customer's attributes are: arrival time, origin and destination, number of destined tickets, and willingness to pay. Each attribute can be generated using a specific distribution.

The created customers will then be used to simulate the purchase of tickets and overall revenue for a flight network. With a valid simulation, airlines will be able to compare the performance of different RM engines under various circumstances.

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  • 2012-05

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A Stochastic Airline Staff Scheduling Model with Risk Considerations that Minimizes Costs

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

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.

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  • 2016-05

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Operations Research Contributions to Emergency Department Patient Flow Optimization: A Review

Description

In recent years, Operations Research (OR) has had a signicant impact on improving the performance of hospital Emergency Departments (EDs). This includes improving a wide range of processes involving patient

In recent years, Operations Research (OR) has had a signicant impact on improving the performance of hospital Emergency Departments (EDs). This includes improving a wide range of processes involving patient ow from the initial call to the ED through disposition, discharge home, or admission to the hospital. We mainly seek to illustrate the benet of OR in EDs, and provide an overview of research performed in this vein to assist both researchers and practitioners. We also elaborate on possibilities for future researchers by shedding light on some less studied aspects that can have valuable impacts on practice.

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  • 2013-12

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Optimal Scheduling of the Refurbishment of Rotable Parts in an Aircraft Maintenance System

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

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.

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  • 2013-12

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NBA Player Clustering: Exploring Player Archetypes in a Changing NBA

Description

The findings of this project show that through the use of principal component analysis and K-Means clustering, NBA players can be algorithmically classified in distinct clusters, representing a player archetype.

The findings of this project show that through the use of principal component analysis and K-Means clustering, NBA players can be algorithmically classified in distinct clusters, representing a player archetype. Individual player data for the 2018-2019 regular season was collected for 150 players, and this included regular per game statistics, such as rebounds, assists, field goals, etc., and advanced statistics, such as usage percentage, win shares, and value over replacement players. The analysis was achieved using the statistical programming language R on the integrated development environment RStudio. The principal component analysis was computed first in order to produce a set of five principal components, which explain roughly 82.20% of the total variance within the player data. These five principal components were then used as the parameters the players were clustered against in the K-Means clustering algorithm implemented in R. It was determined that eight clusters would best represent the groupings of the players, and eight clusters were created with a unique set of players belonging to each one. Each cluster was analyzed based on the players making up the cluster and a player archetype was established to define each of the clusters. The reasoning behind the player archetypes given to each cluster was explained, providing details as to why the players were clustered together and the main data features that influenced the clustering results. Besides two of the clusters, the archetypes were proven to be independent of the player's position. The clustering results can be expanded on in the future to include a larger sample size of players, and it can be used to make inferences regarding NBA roster construction. The clustering can highlight key weaknesses in rosters and show which combinations of player archetypes lead to team success.

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  • 2019-05

Applying Knowledge Management Systems to ASU Capstone Courses: Implementing Knowledge Sharing Practices to Better Capture Data and Lessons Learned from Year-Long Capstone Projects

Description

In the past, Industrial Engineering/Engineering Management Capstone groups have not provided adequate documentation of their project data, results, and conclusions to both the course instructor and their project sponsors. The

In the past, Industrial Engineering/Engineering Management Capstone groups have not provided adequate documentation of their project data, results, and conclusions to both the course instructor and their project sponsors. The goal of this project is to mitigate these issues by instituting a knowledge management system with one of ASU’s cloud storage tools, OSF, and by updating course rubrics to reflect knowledge sharing best practices. This project used existing research to employ tactics that promote the long-term use of this system. In addition, data specialists from ASU Library’s Research and Data Management department were involved.

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  • 2019-12

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The Application of Generative Design in Product Development

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The premise of this thesis developed from my personal interests and undergraduate educational experiences in both industrial engineering and design studies, particularly those related to product design. My education has

The premise of this thesis developed from my personal interests and undergraduate educational experiences in both industrial engineering and design studies, particularly those related to product design. My education has stressed the differences in the ways that engineers and designers approach problem solving and creating solutions, but I am most interested in marrying the two mindsets of designers and engineers to better solve problems creatively and efficiently.
This thesis focuses on the recent appearance of generative design technology into the world of industrial design and engineering as it relates to product development. An introduction to generative design discusses the uses and benefits of this tool for both designers and engineers and also addresses the challenges of this technology. The relevance of generative design to the world of product development is discussed as well as the implications of how this technology will change the roles of designers and engineers, and especially their traditional design processes. The remainder of this paper is divided into two elements. The first serves as documentation of my own exploration of using generative design software to solve a product design challenge and my reflections on the benefits and challenges of using this tool. The second element addresses the need for employing quantitiative methodologies within the generative design process to aid designers in selecting the most advantageous design option when presented with generative outcomes. Both sections aim to provide more context to this new design process and seek to answer questions about some of the ambiguous processes of generative design.

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Date Created
  • 2019-05

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Capacity Planning, Production and Distribution Scheduling for a Multi-Facility and Multi-Product Supply Chain Network

Description

In today’s rapidly changing world and competitive business environment, firms are challenged to build their production and distribution systems to provide the desired customer service at the lowest possible

In today’s rapidly changing world and competitive business environment, firms are challenged to build their production and distribution systems to provide the desired customer service at the lowest possible cost. Designing an optimal supply chain by optimizing supply chain operations and decisions is key to achieving these goals.

In this research, a capacity planning and production scheduling mathematical model for a multi-facility and multiple product supply chain network with significant capital and labor costs is first proposed. This model considers the key levers of capacity configuration at production plants namely, shifts, run rate, down periods, finished goods inventory management and overtime. It suggests a minimum cost plan for meeting medium range demand forecasts that indicates production and inventory levels at plants by time period, the associated manpower plan and outbound shipments over the planning horizon. This dissertation then investigates two model extensions: production flexibility and pricing. In the first extension, the cost and benefits of investing in production flexibility is studied. In the second extension, product pricing decisions are added to the model for demand shaping taking into account price elasticity of demand.

The research develops methodologies to optimize supply chain operations by determining the optimal capacity plan and optimal flows of products among facilities based on a nonlinear mixed integer programming formulation. For large size real life cases the problem is intractable. An alternate formulation and an iterative heuristic algorithm are proposed and tested. The performance and bounds for the heuristic are evaluated. A real life case study in the automotive industry is considered for the implementation of the proposed models. The implementation results illustrate that the proposed method provides valuable insights for assisting the decision making process in the supply chain and provides significant improvement over current practice.

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Date Created
  • 2020