Flow-Shop Scheduling Problem With Batch Processing Machines via Deep Reinforcement Learning for Industrial Internet of Things

Abstract

The rapidly evolving Industrial Internet of Things (IIoT) is driving the transition from conventional manufacturing to intelligent manufacturing. Intelligent shop scheduling, as one of the essential components of intelligent manufacturing in IIoT, is desired to allocate jobs on different machines to achieve specific production targets. The flow-shop scheduling problem with batch processing machines (FSSP-BPM), which widely exists in real-world manufacturing, requires two distinct but interdependent decisions: batch formation and job scheduling. Existing approaches rely on fixed search paradigms that utilize expert knowledge to find satisfactory solutions. However, these methods struggle to ensure solution quality under real-time constraints due to the varying data distribution and the complexity of large-scale practical problems. To address this challenge, we propose a deep reinforcement learning (DRL) based method. First, we formulate the FSSP-BPM decision process as a Markov Decision Process (MDP) and design the corresponding state, action, and reward. Second, we propose a basic scheduling framework based on an encoder-decoder model with the attention mechanism. Finally, we design a batch formation module and a scheduling module trained on unlabeled multi-dimensional data. Extensive experiments on public benchmark datasets and actual production data demonstrate that the proposed method outperforms baseline algorithms and improves makespan performance by an average of 8.33%.