Work

A Scalable Two-Level Deep Reinforcement Learning Framework for Joint WIP Control and Job Sequencing in Flow Shops

Year: 2025

Type: article

Abstract: Effective production control requires aligning strategic planning with real-time execution under dynamic and stochastic conditions. This study proposes a scalable dual-agent Deep Reinforcement Learning (DRL) framework for the joint optimisation of Work-In-Process (WIP) control and job sequencing in flow-shop environments. A strategic DQN agent regulates global WIP to meet throughput targets, while a tactical DQN agent adaptively selects dispatching rules at the machine level on an event-driven basis. Parameter sharing in the tactical agent ensures inherent scalability, overcoming the combinatorial complexity of multi-machine scheduling. The agents coordinate indirectly via a shared simulation environment, learning to balance global stability with local responsiveness. The framework is validated through a discrete-event simulation integrating agent-based modelling, demonstrating consistent performance across multiple production scales (5–15 machines) and process time variabilities. Results show that the approach matches or surpasses analytical benchmarks and outperforms static rule-based strategies, highlighting its robustness, adaptability, and potential as a foundation for future Hierarchical Reinforcement Learning applications in manufacturing.

Source: Applied Sciences

DOI: 10.3390/app151910705

Authors: Maria Grazia Marchesano, Guido Guizzi, Valentina Popolo, Anastasiia Rozhok

Institutions: University of Naples Federico II, Pegaso University, University of Genoa

Language: English

Topic: Scheduling and Optimization Algorithms

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896