dravalico/optimizing_automotive_inbound_logistics

Implementation of an optimization model described in the paper

Optimizing automotive inbound logistics: A mixed-integer linear programming approach

Final project for the exam of Mathematical Optimisation course @ University of Trieste, carried out with Lorenzo Elia. This repository contains the implementation of the model described here.

Setup

To clone this repository, you need to have git installed. After that you can open a terminal window and run

git clone https://github.com/damianoravalico/optimizing_automotive_inbound_logistics

Move to the project folder

cd optimizing_automotive_inbound_logistics

Now you need to install the dependencies. Make sure you have pip installed and then run

pip install -r requirements.txt

To run the program, do the following

cd src

and finally

python main.py

Directories

• The results directory contains all the data needed for the analysis. Thanks to git, the only file in it is the file csv, named collected_data.csv, used for analysis done and written within the presentation. When you run the main.py, all new data is saved inside this directory, with the name data_ + datetime + .csv
• src is the basis package containing all Python code. Inside there are:
  • model.py files, which represent the model used for optimization (variables and constraints)
  • main.py, the entry point
  • Package anal, which contains all the scripts to do the analysis. Note that these scripts are based at collected_data.csv
  • dataset package, which contains the scripts and a class, used within the model and for generating the dataset