Traveling Salesmen Problem Solving by Dynamic Programming & GVNS (TSP)

In this project we shall discuss on the Travelling Salesman Problem (TSP) a very famous NP-hard problem and will take a few attempts to solve it, using Dynamic programming, or by using approximation algorithms (GVNS) and work on the corresponding python implementations.

How to use the application ?

1. Clone the project on your machine with :
   

   
   git clone https://github.com/zakariamejdoul/tsp_solving_dp_gvns.git
   

2. Run all cells in Jupyter Notebook file named main-app.ipynb.
3. Put the instance path (you can use the instances provided in the folder named instances).
4. Put your choice : 1 for dynamic programming method and 2 for GVNS metaheuristic method.
5. Put your source city (the numbering order of cities starts with 1).
6. If you have selected the choice 2 (GVNS method) you must provide the maximum execution time
   of the program in minutes.
7. Enjoy the results !

Results include the optimal path, the optimal distance and the target plot.

Your matrix : 

   0,   12,   10,   19,    8
  12,    0,    3,    7,    2
  10,    3,    0,    6,   20
  19,    7,    6,    0,    4
   8,    2,   20,    4,    0

TSP solver

******* Menu *******
Please choose one of these methods :
(1) Solve with dynamic programming 
(2) Solve with GVNS


OPTIMAL POLICY : [3, 1, 5, 4, 2, 3]
OPTIMAL VALUE : 32

Generated coordinates of cities : 
[[32 33]
 [23 32]
 [38 21]
 [16 21]
 [10 32]]