In alphabetical order:
Apurva Kulkarni, Arsh Khan, Harshal Kataria, K T Prajwal Prathiksh, Miloni Atal, Mridul Agarwal, Patel Joy Pravin Kumar, Nakul Randad, Souvik Kumar Dolui, Umang Goel
Contains code meant to optimize the route for a tourist visiting the Louvre Museum, such that the satisfaction level is maximised by visiting all/select exhibits in a single working day.
This repository represents the work done as part of the course project for AE - 755: Optimization for Engineering Design (Spring 2020), Prof. Abhijit Gogulapati, Indian Institute of Technology Bombay.
Instructions on running specific algorithms are mentioned below:
Note: All of the commands mentioned below support CLI. Use the argument -h for help in each case.
To generate and store the cost matrices of all the test cases, do the following from root:
$ python code/data_input/base_input.py
To run the branch and bound algorithm, do the following from root:
$ python code/branch_and_bound/time_opti.py
Run the following to get all the command-line arguments:
$ python code/branch_and_bound/time_opti.py -h
Simple Algorithm
To run the simple simulated annealing algorithm, do the following from root:
$ python code/simulated_annealing/simple_simulated_annealing.py
Complex Algorithm
To run the complex simulated annealing algorithm, do the following from root:
$ python code/simulated_annealing/complex_simulated_annealing.py
Automator
To run the automator file, do the following from root:
$ python code\simulated_annealing\automate.py
.
├───code
│ ├───ant_colony
│ ├───branch_and_bound
│ ├───data_input
│ ├───genetic
│ └───simulated_annealing
├───data
│ ├───cost_matrices
│ └───Symmetric_TSPLIB
├───output
└───reports