Dynamic Scheduling Problem Python Solver with RL

Still work in progress

This project focuses on exploring the research field of dynamic scheduling problems with RL, dealing with stochastic arrival of the new orders and tardiness cost function

Main points:

• Developed the ’Environment’ class using object-oriented programming in Python to address a dynamic scheduling problem, enhancing system flexibility and scalability.
• Engineered a Reinforcement Learning (RL) agent complete with a customized learning algorithm to efficiently manage dynamic scheduling of orders, accommodating random arrival times and diverse cost functions such as earliness and tardiness.
• Conducted comparative analysis of the RL agent against heuristic solutions, achieving a 10% cost reduction in scenarios utilizing the tardiness cost function, thereby demonstrating significant performance improvements.

The final paper and code will be released soon