MNE/ISE MS Project Presentation by Mr. Arun Kumar Mutyala
When: Thursday,
November 30, 2017
10:00 AM
-
11:00 AM
Where: Textiles Building 101E
Description: Mechanical Engineering (MNE)/
Industrial & Systems Engineering (ISE)
Master of Science Project Presentation
by Mr. Arun Kumar Mutyala
DATE:
November 30, 2017
TIME:
10:00 a.m. 11:00 a.m.
LOCATION:
Textile Building, Room 101E
TOPIC:
Examining Different Learning Methods for Airline Decisions of Route Planning
ABSTRACT:
The airline industry has important decision to make in airline route planning i.e. add new route if it has demand and deletes existing route if the route has not much demand. Such decisions are important for increasing profitability, understanding customers flight preference behavior, satisfying customers need, make changes in route planning and adapting to competitive changes. The aim of this paper is to develop a predictive model to identify parameters affecting airline route planning and making an accurate decision while adding or deletion a route for effective network topology in airline transportation. These models are useful for stakeholders and airline managers to make smart choices. Discrete choice models are widely used for modeling algorithms in the airline industry. These models are created using utility function, a linear function of independent variables and their interaction effects. Utility function uses binary choice model for making decision of route selection. They are also used to project future demand estimates to support policy exploration. This latter use for prediction is indirectly aligned with and conditional to the models estimation which aims to fit the observed data. On the other hand, machine learning models are designed in such a way that they maximize prediction accuracy. In this project, different models are made for airline decisions on route planning using discrete choice model and machine learning models and a comparison study is performed using prediction accuracy on route addition or deletion. The performance was better using machine learning models compared to the discrete choice model in both route addition and deletion.
ADVISOR:
Dr. Soheil Sibdari, Decision and Information Sciences, Charlton College of Business, UMD
COMMITTEE MEMBERS:
Dr. Wenzhen Huang, Mechanical Engineering, College of Engineering, UMD
Dr. Bharatendra Rai, Decision and Information Sciences, Charlton College of Business, UMD
Open to the public. All MNE students are encouraged to attend.
For more information, please contact Dr. Soheil Sibdari (ssibdari@umassd.edu, 508-999-8019).
Thank you,
Sue Cunha, Administrative Assistant
scunha@umassd.edu
508-999-8492
Industrial & Systems Engineering (ISE)
Master of Science Project Presentation
by Mr. Arun Kumar Mutyala
DATE:
November 30, 2017
TIME:
10:00 a.m. 11:00 a.m.
LOCATION:
Textile Building, Room 101E
TOPIC:
Examining Different Learning Methods for Airline Decisions of Route Planning
ABSTRACT:
The airline industry has important decision to make in airline route planning i.e. add new route if it has demand and deletes existing route if the route has not much demand. Such decisions are important for increasing profitability, understanding customers flight preference behavior, satisfying customers need, make changes in route planning and adapting to competitive changes. The aim of this paper is to develop a predictive model to identify parameters affecting airline route planning and making an accurate decision while adding or deletion a route for effective network topology in airline transportation. These models are useful for stakeholders and airline managers to make smart choices. Discrete choice models are widely used for modeling algorithms in the airline industry. These models are created using utility function, a linear function of independent variables and their interaction effects. Utility function uses binary choice model for making decision of route selection. They are also used to project future demand estimates to support policy exploration. This latter use for prediction is indirectly aligned with and conditional to the models estimation which aims to fit the observed data. On the other hand, machine learning models are designed in such a way that they maximize prediction accuracy. In this project, different models are made for airline decisions on route planning using discrete choice model and machine learning models and a comparison study is performed using prediction accuracy on route addition or deletion. The performance was better using machine learning models compared to the discrete choice model in both route addition and deletion.
ADVISOR:
Dr. Soheil Sibdari, Decision and Information Sciences, Charlton College of Business, UMD
COMMITTEE MEMBERS:
Dr. Wenzhen Huang, Mechanical Engineering, College of Engineering, UMD
Dr. Bharatendra Rai, Decision and Information Sciences, Charlton College of Business, UMD
Open to the public. All MNE students are encouraged to attend.
For more information, please contact Dr. Soheil Sibdari (ssibdari@umassd.edu, 508-999-8019).
Thank you,
Sue Cunha, Administrative Assistant
scunha@umassd.edu
508-999-8492
Topical Areas: Faculty, General Public, Students, University Community, Decision and Information Science, College of Engineering, Mechanical Engineering, Lectures and Seminars