Museum-Path-Optimization

• Museum-Path-Optimization
  • Group Members
  • Description
    • Data Input
    • Branch and Bound
    • Simulated Annealing
  • Project Tree

Group Members

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

Description

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.

Data Input

To generate and store the cost matrices of all the test cases, do the following from root:

$ python code/data_input/base_input.py

Branch and Bound

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

Simulated Annealing

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

Project Tree

.
├───code
│   ├───ant_colony
│   ├───branch_and_bound
│   ├───data_input
│   ├───genetic
│   └───simulated_annealing
├───data
│   ├───cost_matrices
│   └───Symmetric_TSPLIB
├───output
└───reports