elsp_drl

Implementation of the IEEE TASE paper Stochastic Economic Lot Scheduling via Self-Attention based Deep Reinforcement Learning. IEEE Transactions on Automation Science and Engineering, 2023.

@ARTICLE{
author={Song, Wen and Mi, Nan and Li, Qiqiang and Zhuang, Jing and Cao, Zhiguang},
journal={IEEE Transactions on Automation Science and Engineering},
title={Stochastic Economic Lot Scheduling via Self-Attention based Deep Reinforcement Learning},
year={2023},
volume={},
number={},
pages={1-12},
doi={10.1109/TASE.2023.3248229}
}

Get Started

Installation on Linux

git clone https://github.com/Meeea-914/elsp_drl.git & cd elsp_drl
python3 -m venv venv
source venv/bin/activate

Install gpu or cpu version pytorch==1.7

pip install -r requirments.txt

Introduction

The usage of files or directories:

 elsp_env_manager - Python module for ELSP simulation
 experiment - Main functional module
 config.yaml - Experiment config file

The help context:

Usage: run.py train [OPTIONS]

Options:
  -t, --net_type [ssa|mlp]  Select the type of network to use
  -e, --env_no [3|4|5|6]    Set the number [i] of simulation environment,
                            where i means there are i products
  -h, --help                Show this message and exit.

Usage: run.py evaluate [OPTIONS]

Options:
  -t, --net_type [ssa|mlp]     Select the type of network to use
  -e, --env_no [3|4|5|6]       Set the number [i] of simulation environment,
                               where i means there are i products
  -s, --demand_scale FLOAT     Set the demand scale of all products
  -m, --model_path TEXT        Give the path of a trainded model file to
                               evaluate
  -r, --result_xlsx_path TEXT  Give the path of the result xlsx file
  -h, --help                   Show this message and exit.

Train

python run.py train -t ssa -e 3
python run.py train -t ssa -e 4
python run.py train -t ssa -e 5
python run.py train -t ssa -e 6
python run.py train -t mlp -e 3

Evaluate

python run.py evaluate -t ssa -e 3 -s 1 -m /path/to/a/optimized/model
python run.py evaluate -t mlp -e 3 -s 1 -m /path/to/a/optimized/model