Work

A deep reinforcement learning and a dynamic graph neural network-based scheduling agent to control a multi-task robot

Year: 2026

Type: article

Abstract: • A scheduling problem representative of today’s robotic-based production environments and challenges. • A mathematical formulation for a optimization model. • A framework including a simulator of the robot’s movements and a custom solving strategy based on Q-values guided beam search. • A dynamic and heterogeneous graph representation and a custom graph neural network. • A dedicated training algorithm based on reinforcement learning ( ɛ ɛ -greedy DQN).

Source: Robotics and Computer-Integrated Manufacturing

DOI: 10.1016/j.rcim.2026.103328

Authors: Hedi Boukamcha, Anas Neumann, Monia Rekik, Adnene Hajji, Gabriel Caron Guillemette, Mohamed Farah

Institutions: Université Laval, Polytechnique Montréal, Centre interuniversitaire de recherche sur les réseaux d'entreprise, la logistique et le transport, Manouba University

Language: English

Topic: Scheduling and Optimization Algorithms

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,540

Funder: Natural Sciences and Engineering Research Council of Canada