This is a field service dispatching planner, focusing on Reinforcement Learning and Optimization based automatic Dispatching.

We are working on demo.

EasyDispatch relies on Postgres DB, Redis and Kafka. Those three components can be started by docker and compose or provisioned seperately. You also should have npm and node for frontend development.

1. To run easydispatch locally, first install it by:

git clone https://github.com/alibaba/easydispatch.git && cd easydispatch
pip install -e .

2. Then copy and modify env file from $easydisaptch/etc/dev_env_sample to $easydisaptch/dev.env . Start the database, redis and kafka by docker composer.

docker-compose -f kafka-redis-postgres-compose.yml -p easy up

3. Open another terminal, populate some sample data and run the frontend:

python -m dispatch.cli database init
python -m dispatch.cli server start --port 8000 dispatch.main:app 

4. Visit the page at : http://localhost:8000/login

Those scripts can be used for testing purpose:

• clear off all existing workers and jobs
• generate sample data for a period
• use batch optimizer to dispatch all jobs in the planning window

cd $EASHDISPATCH_HOME
python  tests/kandbox_clear_data.py 
python -m dispatch.cli util generate --dataset veo --team_code london_t1 --start_day 20210503 --end_day 20210505 --username demo --password demo
python -m dispatch.cli job dispatch  --team_code london_t1  --start_day 20210503 --dispatch_days 2

We tested it on Ubuntu 20.04 and MacOS, Python 3.7 / 3.8

The frontend and server technology stack (vue + python) were adapted from Netflix Dispatch. Data structures are not compatible.