Graph-Enhanced Deep Reinforcement Learning for Multi-Objective Unrelated Parallel Machine Scheduling

TL;DR

This paper introduces a novel deep reinforcement learning approach combining Graph Neural Networks and PPO to effectively solve complex multi-objective unrelated parallel machine scheduling problems, outperforming traditional methods.

Contribution

It presents a new RL framework with GNNs for representing complex scheduling states, enabling direct policy learning for multi-objective optimization in UPMSP.

Findings

PPO-GNN significantly outperforms standard dispatching rules.

The method achieves better trade-offs between TWT and TST.

Experimental results validate scalability and robustness.

Abstract

The Unrelated Parallel Machine Scheduling Problem (UPMSP) with release dates, setups, and eligibility constraints presents a significant multi-objective challenge. Traditional methods struggle to balance minimizing Total Weighted Tardiness (TWT) and Total Setup Time (TST). This paper proposes a Deep Reinforcement Learning framework using Proximal Policy Optimization (PPO) and a Graph Neural Network (GNN). The GNN effectively represents the complex state of jobs, machines, and setups, allowing the PPO agent to learn a direct scheduling policy. Guided by a multi-objective reward function, the agent simultaneously minimizes TWT and TST. Experimental results on benchmark instances demonstrate that our PPO-GNN agent significantly outperforms a standard dispatching rule and a metaheuristic, achieving a superior trade-off between both objectives. This provides a robust and scalable solution for complex manufacturing scheduling.