Jungstershark/P-Median-Problem

The P-Median Problem project uses metaheuristic optimization to solve the p-median location problem, with Jupyter notebooks implementing random sampling and local search algorithms to minimize service distances.

[SHARP] Metaheuristic Optimization - P Median Problem

This repository contains the Jupyter notebooks, text files, and supporting documentation for the project activity on the p-median location problem. The objective of this project is to determine the optimal selection of facilities from a set of potential locations, minimizing the total distance for serving all demand points while ensuring that each demand point is assigned to its nearest selected facility.

Contents

• Ipyb Files: Contains Jupyter notebooks.

  • Random Sampling - Instance 1.ipynb: Implementing the random sampling algorithm for instance 1.
  • Random Sampling - Instance 2.ipynb: Implementing the random sampling algorithm for instance 2.
  • Local Search - Instance 1.ipynb: Implementing the local search algorithm for instance 1.
  • Local Search - Instance 2.ipynb: Implementing the local search algorithm for instance 2.

• Text Files: Supplementary text files corresponding to the Jupyter notebooks.

  • Random Sampling - Instance 1.txt: Text file for implementing of random sampling algorithm
  • Random Sampling - Instance 2.txt: Text file for implementing of random sampling algorithm
  • Local Search - Instance 1.ipynb: Text file for implementing of local search algorithm
  • Local Search - Instance 2.ipynb: Text file for implementing of local search algorithm

• requirements.txt: List of Python packages required to run the notebooks.

• SHARP_MHO_1.pdf: A comprehensive report detailing solution encoding representation, search procedure explanation, and results obtained for each exercise.

Problem Overview

Given a set of n locations representing demand points and a set of m potential facility locations, the p-median problem aims to determine the optimal selection of p facilities from the set of potential locations.

Instances

• Instance 1: 100 locations, requiring 15 facilities to be open.
• Instance 2: 1000 locations, requiring 30 facilities to be open.

Setup & Execution

Setup & Execution

1. Ensure you have Jupyter Notebook and required Python packages installed.
2. Clone the repository:

git clone https://github.com/<your_username>/P-Median-Problem.git

3. Navigate to the cloned directory.
4. Install required packages:

pip install -r requirements.txt

5. Run Jupyter Notebook:

jupyter notebook

6. Open each notebook and execute the cells sequentially.