https://github.com/arnobt78/vrptw-solver-comparison-pyapp

My Master's Thesis Project - Solving Vehicle Routing Problem with Time Windows using multiple Classic Heuristic, Metaheuristic algorithms (Hybrid Genetic Search (HGS), Guided Local Search (GLS), Ant Colony Optimisation (ACO), Simulated Annealing (SA)), compare result's with each one's results and present it in the graph.
https://github.com/arnobt78/vrptw-solver-comparison-pyapp

ant-colony-optimization grasp guided-local-search high-integrity-systems hybrid-genetic-search informatik jupiter-notebook local-search master-thesis python simulated-annealing vehicle-routing-problems-with-time-window vrp vrptw

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My Master's Thesis Project - Solving Vehicle Routing Problem with Time Windows using multiple Classic Heuristic, Metaheuristic algorithms (Hybrid Genetic Search (HGS), Guided Local Search (GLS), Ant Colony Optimisation (ACO), Simulated Annealing (SA)), compare result's with each one's results and present it in the graph.

• Host: GitHub
• URL: https://github.com/arnobt78/vrptw-solver-comparison-pyapp
• Owner: arnobt78
• Created: 2024-02-11T21:16:35.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-08-25T17:05:43.000Z (about 1 year ago)
• Last Synced: 2024-12-01T13:37:58.270Z (10 months ago)
• Topics: ant-colony-optimization, grasp, guided-local-search, high-integrity-systems, hybrid-genetic-search, informatik, jupiter-notebook, local-search, master-thesis, python, simulated-annealing, vehicle-routing-problems-with-time-window, vrp, vrptw
• Language: Jupyter Notebook
• Homepage:
• Size: 6.91 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          

## The Comparison results of different NP metahuristic algorithms for VRPTW

Running Algorithms on dataset: rc108.txt

- - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - -

Best-Known Solution (BKS) Route Cost: 1114.2

BKS solution:

Route #1: 2 6 7 8 46 4 45 5 3 1 100

Route #2: 12 14 47 17 16 15 13 9 11 10

Route #3: 33 32 30 28 26 27 29 31 34 93

Route #4: 41 42 44 43 40 38 37 35 36 39

Route #5: 61 81 94 71 72 54 96

Route #6: 64 51 76 89 18 48 19 20 66

Route #7: 65 83 57 24 22 49 21 23 25 77

Route #8: 69 98 88 53 78 73 79 60 55 70 68

Route #9: 82 99 52 86 87 59 97 75 58 74

Route #10: 90

Route #11: 92 95 67 62 50 63 85 84 56 91 80

- - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - -

Hybrid Genetic Search (HGS) Route Cost: 1114.2

HGS solution:

Route #1: 12 14 47 17 16 15 13 9 11 10 

Route #2: 82 99 52 86 87 59 97 75 58 74 

Route #3: 65 83 57 24 22 49 21 23 25 77 

Route #4: 64 51 76 89 18 48 19 20 66 

Route #5: 92 95 67 62 50 63 85 84 56 91 80 

Route #6: 33 32 30 28 26 27 29 31 34 93 

Route #7: 61 81 94 71 72 54 96 

Route #8: 41 42 44 43 40 38 37 35 36 39 

Route #9: 2 6 7 8 46 4 45 5 3 1 100 

Route #10: 90 

Route #11: 69 98 88 53 78 73 79 60 55 70 68 

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Guided Local Search (GLS) Route Cost: 1266.9

GLS solution:

Route #1: 71 72 44 43 40 38 37 35 36 39

Route #2: 98 82 90 53 78 73 79 2 60

Route #3: 92 67 32 30 28 26 27 29 31 34 93

Route #4: 65 99 24 22 20 49 21 23 25 77

Route #5: 95 51 76 89 33 50 62 91 80

Route #6: 12 14 47 17 16 15 13 9 11 10

Route #7: 88 6 7 8 46 4 45 5 3 1 100 55

Route #8: 69 70 61 81 94 96 54 41 42 68

Route #9: 83 52 57 86 87 59 97 75 58 74

Route #10: 64 19 48 18 63 85 84 56 66

- - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - -

Ant Colony Optimization (ACO) Route Cost: 1321.8459204561746

ACO solution:

Route #1: 69 98 88 82 99 52 86 74 57 83 66 91

Route #2: 65 64 51 76 89 85 63 62 56 80

Route #3: 90 53 73 79 78 60 55 68

Route #4: 33 28 30 32 34 31 29 27 26

Route #5: 72 71 93 94 81 61 54 96 100 70

Route #6: 2 45 5 8 7 6 46 4 3 1

Route #7: 41 42 44 38 39 40 36 35 37 43

Route #8: 19 21 23 18 48 49 22 20 24 25

Route #9: 12 14 47 17 16 15 11 10 9 13

Route #10: 59 58 87 97 75 77

Route #11: 92 95 84 50 67

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Simulated Annealing (SA) Route Cost: 1237.620141359753

SA solution:

Route #1: 7 8 46 4 45 5 3 1 100 55

Route #2: 64 51 76 89 63 85 84 56 91

Route #3: 69 98 53 12 15 16 17 47 14

Route #4: 90 82 9 13 11 10

Route #5: 61 42 44 40 39 38 37 35 36 43

Route #6: 65 52 86 77 25 23 57

Route #7: 88 60 78 73 79 6 2 70 68

Route #8: 92 67 62 34 50 94 96

Route #9: 99 87 59 97 75 58 74

Route #10: 83 24 22 19 18 48 21 49 20 66

Route #11: 81 93 71 72 41 54

Route #12: 95 33 32 30 28 26 27 29 31 80

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Algorithms - - No. of Routes - - Costs - - Gap(%) - - Runtime(seconds)

  BKS - - - - - - 11 - - - - - - - - - - 1114.2 - - -	-

  HGS - - - - - - 11 - - - - - - - - - - 1114.2 - - 0.0 - - - - 300.137736082077

  GLS - - - - - - 10 - - - - - - - - - - 1266.9 - - 13.7 - -- - 300.0492959022522

  ACO - - - - - - 11 - - - - - - - - - - 1321.8 - - 18.63 - - - 877.1980187892914

  SA - - -- - - - 12 - - - - - - - - - - 1237.6 - - 11.08 - - - 416.80780506134033

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![3 HGS rc108](https://github.com/user-attachments/assets/afac9be9-37a1-4b4d-a7f8-5d346d653e2f) ![3 GLS rc108](https://github.com/user-attachments/assets/b8140b7f-30c2-4f65-9d55-24207754dd0f) ![3 ACO rc108](https://github.com/user-attachments/assets/3643d0e9-c3d4-418c-8ed7-efea8d94ede9) ![3 SA rc108](https://github.com/user-attachments/assets/3632158b-6131-4157-97bd-9cd4116bcecf)

## VRPTW-Solver-Comparison-pyApp

Solving Vehicle Routing Problems with Time Windows using multiple Classic Heuristic and metaheuristic algorithms (Hybrid Genetic Search (HGS), Guided Local Search (GLS), Ant Colony Optimization (ACO), Simulated Annealing (SA)), then comparing results with each one's results and present it in the graph. This was my master's thesis project.

## keywords

Vehicle Routing Problems with Time Window (VRPTW), Hybrid Genetic Search (HGS), Guided Local Search (GLS), Ant Colony Optimization (ACO), Simulated Annealing (SA), MACS-VRPTW, Genetic Algorithm (GA), Exact, Heuristics, Metaheuristics, Machine Learning Algorithms, GRASP, Local Search (LS), Neighborhood Search, OR-Tools, VRP, VRPTW, Vehicle Routing Problems (VRP), pyVRP.

## Important Note

Using the Python Jupyter Notebook is highly advised. While compiling, each model executes independently. However, if you run the code straight from main.py , the application may crash and display errors due to the ACO procedure’s use of multiple threads! However, it functions perfectly now that I’m using the Python Jupyter Notebook code.

## More Details

To have a thorough understanding (about the parameter tuning and machanise) of my Vehicle Routing Problem with Time Windows project, I recommend reading my master's thesis. Thank you.

# Development

## Create venv

```sh

python -m venv .venv

```

## Activate venv

```sh

. .venv/bin/activate

```

## Install requirements

```sh

pip install -r requirements.txt

```

## Run the Project

```sh

python main.py

```

## Clone git repository URL 

```sh

git clone {paste repository URL}

```