This program was a project assignement of the course Meta Heuristic at INSA Toulouse. The aim of this project is to famaliarize with meta heuristic methods to solve a combinatorial optimization problem.
The precise problem was a evacuation plan in case of wildfire. "A good evacuation plan must not only minimize the evacuation time of all the population but also maximize the spatial and temporal safety between the evacuees and the actual or potential wildfire front". Read this article for more information.
In this project we just focus on minimizing the total evacuation time, also respecting all the capacity constraints and due date constraints with the given instances.
We also would like to thank you our tutors, Mrs. Marie-Jo HUGET and M.Tom Portoleau, for your helps and your adivces when we are working on this project.
Our report is also available on this git. Read our report for more information about the programs.
We show some examples for generating solutions with given instances in Solution_Generator.ipynb. You can also apply it for other instances and generate a correspond solution by using our functionality included in DataProcess.py, LocalSearch.py.
To verify if this solution is valid or not, you can use our functionality in SolutionChecker.py. Some examples are demonstrated in wildfire_main.ipynb.
Some solutions are generated by us and they are also available in the folder Solutions_040619.
Remark : To run the program, your instance file must be found in folder Instances. Then when you run the function create_solution_file(..) in DataProcess.py, your solution will be found in the folder Solutions.
Our project just stops at generating a solution that verifies all capacity constraints and evacuation rate constraints, but we didn't respect the due date constraints yet. This problem may be upgraded in the future.