DynamicPlacementGenerator

The DynamicPlacementGenerator is a Python program used to generate the best spare engine placement for a specific aircraft fleet.

Background

Delta Air Lines is an industry-leading, globally operating United States airline servicing over 300 destinations with a fleet of approximately 900 aircraft. Delta’s Engine Demand Planning team (EDP), which falls under Delta TechOps, is responsible for planning engine removals, assigning spare engines to seven designated hubs, and setting up the logistics of the removals and repairs of these engines.

The objective of this project is to assist Delta’s EDP team with improving the allocation of spare engines across the contiguous United States. To assist Delta in decreasing both transportation and AOS costs incurred throughout the year, the solution determines the optimal configuration of all spare engines on a monthly basis through a Markov Decision Process. The solution outputs a configuration recommendation for the upcoming month associated with the minimal cost of all possible options. Delta’s EDP team can use the model to make data-informed, cost-driven decisions with the added benefit of reducing required labor hours.

This program currently generates optimal spare placement for the following engine types:

• BR700-715C1-30
• CF6-80C2B8F
• CFM56-5A
• CFM56-5B3-3
• CFM56-7B26
• CFM56-7B27E-B1F
• PW2000-2037
• PW2000-2040
• TRENT8-892-17
• V2500-D5

Spare engine placement for additional engine types can be determined only if the necessary information is provided.

Installations and Setup

Instructions to install the required installations are outlined below with provided terminal commands. These instructions assume basic understanding of using Terminal on Mac. If your machine is not a Mac, these instructions may need to be altered slightly.

Before Cloning this Repository

Install python3. Check that your version matches the one below or is more recent.

python3 --version
Python 3.7.6

Install pip3. Check that you have it by running the command below to check the version. You should see a response similar to the one below.

pip3 -V
pip 19.3.1 from /usr/local/lib/python3.7/site-packages/pip (python 3.7)

Install numpy using pip3.

pip3 install numpy

Install pandas using pip3.

pip3 install pandas

Install scipy using pip3.

pip3 install scipy

Clone this Repository

To clone this repository, navigate to the folder in your terminal that you would like it to be in. Then run the following command:

git clone https://github.com/meredithmurfin/DynamicPlacementGenerator.git

You should then be able to use this command to be in the local DynamicPlacementGenerator directory on your machine:

cd DynamicPlacementGenerator

After Cloning this Repository

This application uses the pymdptoolbox module. Clone the repository for it with the following command. Make sure you are cloning the repository while in the DynamicPlacementGenerator directory on your machine.

git clone https://github.com/sawcordwell/pymdptoolbox.git

Navigate to the pymdptoolbox folder, which now resides in the DynamicPlacementGenerator directory.

cd pymdptoolbox

Setup and install pymdptoolbox using the following terminal command (while in the pymdptoolbox directory):

python3 setup.py install

Usage

First Run

Some files will need to be created to use for all subsequent runs. These tasks will only need to be completed once per machine this program is used on.

Navigate to the DynamicPlacementGenerator directory. Run the following command to set the FIRST_RUN environment variable to indicate this is the first time running this program on your machine:

export FIRST_RUN=true

Doing this will create the following for future use:

• All possible states exported to a file
• All possible actions exported to a file
• All possible removal situations for each engine type exported to a file

Subsequent Runs

The FIRST_RUN environment variable can be set to FALSE for all future runs.

export FIRST_RUN=false

Prior to running this program each month, a few files need to be updated.

Update Future Flight Information

For each engine type, update:

• total_RONSRADS_ground_time_by_hub_monthly.csv
• total_departures_ground_time_by_state_region_monthly.csv
• num_departures_by_hub_monthly.csv

These files can be found in DynamicPlacementGenerator/data_to_read/engine_subtype/.

It is very important that the format of these files stays the same as the examples that are currently provided. You must provide data for each engine subtype for the next 3 months. Below are descriptions of the structure for each, but we have provided file examples for each engine subtype already in the folders that reflect accurate data needed to run this program for January 2019.

The files look similar to this:

YEAR-MONTH	ATL	CVG	DTW	LAX	MSP	SEA	SLC	OTHER
2019-01	26159	2606	42975	11590	28155	38375	34586	975834
2019-02	24378	2067	36572	10296	32870	29834	36271	999810
2019-03	20976	3890	9786	9875	35019	40192	30289	967182

The us_airport_data.csv file located in DynamicPlacementGenerator/data_to_read/ provides information on all airports used for this project. Any reference to all airports is only referring to airports listed in this file. The information for each airport includes:

• 3-letter IATA code
• Name
• Address
• State
• State Region (regions created by our group defined mostly by state lines)
• Closest hub by distance, with distance in miles
• Closest hub by travel time, with travel time in minutes

total_RONSRADS_ground_time_by_hub_monthly.csv

This file contains the summed monthly total ground time (in minutes) of all departures defined as a RON or RAD for each hub. The summed total for all airports excluding hubs is included in the OTHER column.

total_departures_ground_time_by_state_region_monthly.csv

This file contains the summed monthly total ground time (in minutes) of all departures for each hub and state region.

num_departures_by_hub_monthly.csv

This file contains the monthly count of departure occurrences for each hub. The count of departure occurrences for all airports excluding hubs is included in the OTHER column.

Update Information to Reflect Current State/System

data_to_read/removal_info.csv

This file contains information on expected number of removals for each engine subtype. This will most likely not need to be updated often. For each engine subtype, the following is specified:

• Expected maximum number of removals in a month for all airports
• Expected maximum number of removals in a month for each specific hub
• Expected maximum number of removals in a month for all airports excluding hubs
• Expected AOS cost
• Whether or not these files were updated from the previous month (if any of the data for a subtype has been updated, make sure to set the UPDATED column value for that row to be TRUE)

Our team based these values on past removal data for each type. We set the maximum number of removals that could happen based on data from 2015-2019 by taking the maximum that had ever occurred for each and adding 1 to it. For example, if no more than 3 removals ever occurred in ATL, we assumed the maximum number of removals that could ever happen at ATL would be 4.

Limitations:

• The maximum number of removals for all airports cannot be less than 1 or greater than 10
• The maximum number of removals for each specific hub cannot be greater than 10
• The maximum number of removals for all airports excluding hubs cannot be greater than 2

The purpose of this file is to minimize the iterations the program runs so that runtime is reduced and extremely unlikely situations are not considered.

data_to_read/engine_info.csv

This file contains information on engine numbers for each engine subtype. This will probably need to be updated each month. For each engine subtype, the following is specified:

• Total number of current spare engines
• Number of current working spare engines
• Number of current broken spare engines being repaired at ATL
• Number of current broken spare engines being repaired at MSP
• Number of working spare engines currently being stored at each hub

Limitations:

• The total number of current spare engines cannot be less than 1 and cannot be greater than 5

The purpose of this file is to understand the current state being considered so that the action to take associated with the minimum cost is returned.

data_to_read/engine_subtypes.csv

This file contains a list of all engine subtypes this program will generate optimal spare placement for. Engines may be removed from this list prior to running the program if spare placement is to only be generated for specific engines. Engines may also be added to this list, but only if all of the necessary information is provided for that engine in the same format.

Run the Program

Navigate to the DynamicPlacementGenerator directory if you aren't there already.

In your terminal, run the following command:

python3 app.py

The program may take several hours to run.

Files Provided

For each engine subtype (located in DynamicPlacementGenerator/data_to_read/engine_subtype/):

File	Description
total_RONSRADS_ground_time_by_hub_monthly	RON/RAD ground time data for 01-2019 spare placement
total_departures_ground_time_by_state_region_monthly	Departure ground time data for 01-2019 spare placement
num_departures_by_hub_monthly	Departure counts for 01-2019 spare placement
regression	Regression values based on 2015-2019 data
number_of_broken_engines_and_number_repaired	Probabilities of engines repaired given on engines broken
expected_transport_cost	Expected transportation costs from hubs to state regions

Turnover documents will be provided that will outline how to re-calculate regression values based on new past data.

The format of these documents (the naming of the file, the header structure and naming, etc.) must remain the same in order for the program to work.

Authors

Industrial and Systems Engineering, Georgia Institute of Technology, Spring 2020

Team 10

• Samantha Davanzo
• Brian Davis
• Mary Elizabeth Davis
• Bella Jackson
• Meredith Murfin
• Miles Trumbauer