Learning to Solve Resource-Constrained Project Scheduling Problems with Duration Uncertainty using Graph Neural Networks

TL;DR

This paper introduces Wheatley, a novel framework using Graph Neural Networks and Deep Reinforcement Learning to generate resilient schedules for resource-constrained project scheduling problems with uncertain task durations, outperforming existing methods.

Contribution

The paper presents a new GNN-based DRL approach for RCPSP with duration uncertainty, providing a robust scheduling policy that generalizes well across scenarios.

Findings

Outperforms existing scheduling methods on benchmark datasets

Demonstrates strong generalization to different uncertainty scenarios

Provides an open-source framework for further research

Abstract

The Resource-Constrained Project Scheduling Problem (RCPSP) is a classical scheduling problem that has received significant attention due to of its numerous applications in industry. However, in practice, task durations are subject to uncertainty that must be considered in order to propose resilient scheduling. In this paper, we address the RCPSP variant with uncertain tasks duration (modeled using known probabilities) and aim to minimize the overall expected project duration. Our objective is to produce a baseline schedule that can be reused multiple times in an industrial setting regardless of the actual duration scenario. We leverage Graph Neural Networks in conjunction with Deep Reinforcement Learning (DRL) to develop an effective policy for task scheduling. This policy operates similarly to a priority dispatch rule and is paired with a Serial Schedule Generation Scheme to produce a schedule. Our empirical evaluation on standard benchmarks demonstrates the approach's superiority in terms of performance and its ability to generalize. The developed framework, Wheatley, is made publicly available online to facilitate further research and reproducibility.