MESLedger: AI-Driven Scheduling and Blockchain-Based Control for Digitalized Manufacturing

ABSTRACT Digitalized manufacturing processes necessitate a shift from traditional production control systems to more intelligent frameworks. Existing systems reliant on programmable logical controllers fall short in handling the influx of data generated by the Internet of Things layer and the array of IT systems integral to modern operations. This article presents technical developments made within MESLedger, a collaborative project aiming to enhance manufacturing execution by integrating blockchain technology and artificial intelligence. The first part of our work investigates how deep reinforcement learning, particularly the proposed Dual Attention Network for Multi-Objective Proximal Policy Optimization, can optimize job scheduling by balancing objectives such as makespan and energy consumption. This approach formulates the Flexible Job Shop Scheduling Problem as a Markov decision process and uses actor-critic networks enhanced with dual attention mechanisms to generate adaptive and context-aware scheduling strategies. The second part explores how blockchain technology, specifically Hyperledger Fabric, can be used to secure communication within collaborative industrial environments by managing critical manufacturing processes through smart contracts and decentralized architectures. Together, these components form the foundation of an intelligent manufacturing execution systems environment capable of dynamically analyzing data and orchestrating operations based on customizable performance priorities.