https://github.com/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.
https://github.com/jungstershark/p-median-problem

alogorithms jupyter-notebook local-search metaheuristic metaheuristic-algorithms metaheuristic-optimisation metaheuristics optimization optimization-algorithms optimization-methods p-median p-median-problem python python3 random-sampling

Last synced: 4 days ago
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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.

• Host: GitHub
• URL: https://github.com/jungstershark/p-median-problem
• Owner: Jungstershark
• License: apache-2.0
• Created: 2023-10-08T08:22:02.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2024-08-29T07:08:04.000Z (6 months ago)
• Last Synced: 2024-12-25T17:41:04.364Z (about 2 months ago)
• Topics: alogorithms, jupyter-notebook, local-search, metaheuristic, metaheuristic-algorithms, metaheuristic-optimisation, metaheuristics, optimization, optimization-algorithms, optimization-methods, p-median, p-median-problem, python, python3, random-sampling
• Language: Jupyter Notebook
• Homepage: https://github.com/Jungstershark/P-Median-Problem
• Size: 3.05 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md
  • License: LICENSE

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README

        
# [SHARP] Metaheuristic Optimization - P Median Problem

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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:

```bash

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

```

3. Navigate to the cloned directory.

4. Install required packages:

```bash

pip install -r requirements.txt

```

5. Run Jupyter Notebook:

```bash

jupyter notebook

```

6. Open each notebook and execute the cells sequentially.