This is Team18's submission for the Fedex Midprep Problem Statement.

Introduction

This encloses the code for a complete Packing Solution. After running this code on a dataset of packages, the optimal location of each package in their respective ULD's. The output is present in the required specifications. The base algorithm aims to minimise the cost of delay while minimising the number of ULD's in which priority packages are packed.

Usage

Prerequisites

It is highly recommended to use a python virtual environment to run the code. If not, make sure that your pip installation is able to install python packages on your machine. It is also highly recommended to run in Linux or MacOS. Windows users may face some issues with the Stress Analysis feature.

• A working installation of Python (3.12 or higher), with Python added to PATH.
• A working installation of pip.
• Microsoft Visual C++ Build Tools. (only for Windows users, and if using Stress Analysis)

Installation

Run the following command in the project directory in your terminal:

python run.py

This will install the required packages for the code to run, and open a dashboard for the user to interact with the code.

If Streamlit asks for an email, you can safely ignore and click the "Enter" Key. Subsequent runs will skip this step.

In some case, in Ubuntu, 3 links may appear. Try all three of these.

Running the code

There are two primary ways to run the code:

1. Using the Dashboard (Recommended):
   After running the run.py file, a dashboard will open in your default browser. You can upload a dataset, and run the code using the dashboard. Any new test cases must use the same input format as the original test case
   

   python run.py

2. Using the Command Line: You can also run the code using the command line. The following command will run the code on the dataset Challenge_FedEx.txt:

   python src/main.py test/Challenge_FedEx.txt

   NOTE: The path to the dataset should be relative to the project directory.

   Flags:

   • --plot: To plot the 3D view of the ULDs.
   • --stress-analysis: To run the stress analysis feature. (Might not work on Windows: see Prerequisites)
   • --animate: To animate the packing process.

   Example:

   python src/main.py test/Challenge_FedEx.txt --plot

Navigating the Dashboard

What should I do?

1. Drag and Drop/Select the test file data into the Browse Files Button.
   You should see a tabular form of your Packages and details about their number and number of ULD's
2. Adapt Parameters for the current requirements
3. Click the 'Run Packing Algorithm' button

Input Options

1. Rotational Constraint --> An additional column of boolean values must be present in the input data.
   If 'True', then the package can be rotatated along all 3 axes. If 'False', the the package can only be roatated along z-axis.

2. Family of Packages --> An additional column of Family IDs must be present in the in the original dataset. These Family IDs must be integers. If Family Cost is enabled, then the algorithm tries to put family packages closer to each other.

Search Methods and Related Parameters

Normal Search

This is a quick search optimising for time to find Solution

Hyper Search

This is a deep search algorithm that optimises for "Cost" as defined by the Problem Statement

Layering (Available only in Normal Search)

Creates space and cost efficient layers according to the 2DBP logic sytems

Multi-Processing

Takes advantage of multiple cores of modern CPU's for a exponential decrease in runtime

Beam Width (Available only in Hyper Search)

Higher Beam Width = Better Solution It is a parameter used to control how wide our search is in the solution space

Number of iterations

Higher Number of iterations = Better Solution It is a parameter used to control how many internal cycles the programs utilises

Number of cores

Higher Number of Cores = Faster Runtime It is a parameter used to control how many of your multicore CPU is used during running the solution

More Useful Features

The following additonal conditions have been implemented:

• Express Priority Arrival
• Families of packages
• Stability
• This-Side-Up / Packages with Orientation Constraints
• IATA ULD Regulations
• Helper Tool
• Stress Analysis

Note: For 'Families of packages' and 'This-Side-Up' features an extra column must be added in the original dataset.

Express Priority Package

Assuming that the packing algorithm is completed and the packages have started being packed (physically). This is a useful additon which helps us accomodate a new priority package of specified dimensions with minimal disruption!

Method 1 --> Insert Package Here we find an empty space which can fit the required package (Useful for small express packages)
Method 2 --> Replace Package Here we remove an economy package to make space for the new express package. This is done with minimal disturbance to the current package.

Stability

This feature makes sure that none of the packages are off balance.

IATA ULD Regulations

Here we make sure the solutions adhere to this international standard for packing ULD's.

Helper Tool

Using a combination of graph theory and topological sorting we create the package order. We show the most efficient packing order such that real-world constraints are included for a ground staff to pack the ULD's

Stress Analysis

Using a state-of-the-art physics engine we will calculate stress on each package. In future modifications of this code we can increase customer satisfaction with regard to fragile packages etc.

Output format

We are exporting output in the form of a text file, in the format specified in the Problem Statement.

Cost,Number of Packages placed,Number of prioritised ULDs P-1,U1, P-2,U1,

Code Structure

Directories

.
├── README.md
├── requirements.txt
├── run.py
├── AlgorithmicExplanation.mp4
├── src
│   ├── algorithm_interface.py
│   ├── dashboard.py
│   ├── entity.py
│   ├── environment.py
│   ├── family_cost.py
│   ├── geometry_helpers.py
│   ├── insert_package.py
│   ├── layering.py
│   ├── main.py
│   ├── parser.py
│   ├── solvers
│   │   ├── hybrid.py
│   │   ├── layerpack.py
│   │   └── NAC.py
│   └── util.py
└── test
    ├── Challenge_FedEx_Raw.txt
    ├── Challenge_FedEx.txt
    └── layer.txt

Troubleshooting

1. If pip throws an error, check your python environment, and virtual environment.
2. If Dashboard doesn't load, check if all packages are installed and up to date.
3. System compatability issues:
4. Python Version Compatitibilty issues: install python 3.12 or later
5. Pybullet may not work on Windows systems,therefore to solve errors for this use Linux or MacOS systems.