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https://github.com/zhuagenborn/genetic-delivery-man
🚚 A genetic model written in Python for minimizing the delay time of delivery routes.
https://github.com/zhuagenborn/genetic-delivery-man
delivery-optimization genetic-algorithm traveling-salesman-problem
Last synced: 8 days ago
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🚚 A genetic model written in Python for minimizing the delay time of delivery routes.
- Host: GitHub
- URL: https://github.com/zhuagenborn/genetic-delivery-man
- Owner: Zhuagenborn
- License: mit
- Created: 2021-05-23T12:18:43.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-02-13T19:51:45.000Z (11 months ago)
- Last Synced: 2024-11-08T23:39:55.155Z (2 months ago)
- Topics: delivery-optimization, genetic-algorithm, traveling-salesman-problem
- Language: Python
- Homepage:
- Size: 435 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
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README
# Genetic Delivery Man
[](https://www.python.org)
## Introduction
This project uses a genetic model to solve a derivative version of the ***traveling salesman problem*** with time consideration.
> A delivery man needs to deliver some goods to customers in different cities. Every customer has been waiting for a while and has a time limit on the delivery. If the time needed is greater than a customer's limit, the exceeded is treated as a delay.
The genetic model needs to find a route that minimizes the *total delay time* for all customers.
## Getting Started
### Prerequisites
- Install [*Python 3.10*](https://www.python.org).
- Install all dependencies.
```bash
pip install -r requirements.txt
```
### Running
```bash
python main.py
```
### Configurations
The configuration is in the `src/config.json` file.
## Data
All data are stored in the `src/data` folder.
### Cities
The city information is in the `cities.csv` file. The *first* city will automatically be set as the location of the delivery company, in other words, the starting point. And the location of each city have the following constraints:
- 0 ≤ `X` ≤ `mapSize.width`
- 0 ≤ `Y` ≤ `mapSize.height`
The values of `mapSize.width` and `mapSize.height` can be set in the configuration file.
```json
"mapSize": {
"height": 600,
"width": 800
}
```
### Orders
The order information is in the `orders.csv` file. Each order has the following attributes:
| Attribute | Description |
| :---------: | :-------------------------------------: |
| `ID` | Unique order ID. |
| `City` | The destination. |
| `WaitTime` | The time the customer has been waiting. |
| `TimeLimit` | The time limit on delivery. |
For example, if a order's `WaitTime` is 5 and `TimeLimit` is 20, it means the customer has been waiting for 5 minutes and the delivery must be sent within the next 15 minutes. Otherwise there will be a delay.
If all orders' `WaitTime` and `TimeLimit` are equal to 0, the problem will be the same as the *traveling salesman*. The model is actually trying to find the shortest route.
## Genetic Model
### Fitness
### Crossover
The model uses *ordered crossover*. It randomly selects a subsequence of the first route, then fills the remainder with orders from the second route in the order in which they appear, without duplicate.
### Mutation
The mutation randomly swaps two orders in a route.
### Elitism
Keep the best route from the previous generation unchanged and replace the worst in the current generation with it.
### Iteration
The maximum iteration number can be set in the configuration file. There are two variables:
```json
"maxIter": {
"total": 2000,
"unchanged": 0
}
```
The model will stop evolution if it's iteration reaches the value of `maxIter.total`. It is always effective.
`maxIter.unchanged` can be set to 0 and the model will not consider it. When it is larger than 0, the model will stop if the best individual remains unchanged over `maxIter.unchanged` iterations.
The console will show the detail of iterations.
```console
(344) Update the shortest delay: 1180.45 -> 1149.62
0 -> 1 -> 4 -> 5 -> 13 -> 7 -> 8 -> 11 -> 3 -> 12 -> 2 -> 10 -> 6 -> 9
(416) Update the shortest delay: 1149.62 -> 1145.07
0 -> 1 -> 4 -> 5 -> 13 -> 7 -> 8 -> 11 -> 12 -> 2 -> 10 -> 3 -> 6 -> 9
The shortest delay: 1145.07
0 -> 1 -> 4 -> 5 -> 13 -> 7 -> 8 -> 11 -> 12 -> 2 -> 10 -> 3 -> 6 -> 9
```
## Test Statistics
The default configuration is:
```json
{
"speed": 100,
"mapSize": {
"height": 600,
"width": 800
},
"elitism": true,
"populationSize": 100,
"rate": {
"cross": 0.4,
"mutate": 0.01
},
"maxIter": {
"total": 2000,
"unchanged": 0
}
}
```
Before the test, 50 orders were randomly generated. Each order has a unique city as its destination. Their `WaitTime` and `TimeLimit` are equal to 0.
```
ID,City,WaitTime,TimeLimit
0,1,0.0,0.0
1,2,0.0,0.0
2,3,0.0,0.0
3,4,0.0,0.0
```
When testing a variable, the others are fixed.
### Population Size
### Crossover Rate
### Mutation Rate
### Elitism
## Class Diagram
```mermaid
classDiagram
class City {
float x
float y
distance(City) float
}
class Map {
distance(from, to) float
}
Map o-- City
class Order {
City city
float wait_time
float time_limit
}
Order --> City
class OrderList {
random_route() Route
}
OrderList o-- Order
OrderList ..> Route
class TimeOnWay {
Map map
float speed
}
TimeOnWay --> Map
class Route {
Map map
TimeOnWay time_on_way
City origin
float delay
}
Route --> TimeOnWay
class Item {
Route route
float fitness
list~Order~ dna
}
Item --> Route
Item --> Order
class Population {
list~Item~ items
list~float~ fitness
generate(size)
}
Population o-- Item
class Genetic {
Population population
float cross_rate
float mutate_rate
evolve(max_iter, max_unchanged_iter)
}
Genetic --> Population
```
## Dependencies
- [*pygame*](https://www.pygame.org)
- [*pandas*](https://pandas.pydata.org)
- [*NumPy*](https://numpy.org)
## License
Distributed under the *MIT License*. See `LICENSE` for more information.