A Multi-Graph Attributed Reinforcement Learning based Optimization Algorithm for Large-scale Hybrid Flow Shop Scheduling Problem

Abstract

Hybrid Flow Shop Scheduling Problem (HFSP) is an essential problem in the automated warehouse scheduling, aiming at optimizing the sequence of jobs and the assignment of machines to utilize the makespan or other objectives. Existing algorithms adopt fixed search paradigm based on expert knowledge to seek satisfactory solutions. However, considering the varying data distribution and large scale of the practical HFSP, these methods fail to guarantee the quality of the obtained solution under the real-time requirement, especially facing extremely different data distribution. To address this challenge, we propose a novel Multi-Graph Attributed Reinforcement Learning based Optimization (MGRO) algorithm to better tackle the practical large-scale HFSP and improve the existing algorithm. Owing to incorporating the reinforcement learning-based policy search approach with classic search operators and the powerful multi-graph based representation, MGRO is capable of adjusting the search paradigm according to specific instances and enhancing the search efficiency. Specifically, we formulate the Gantt chart of the instance into the multi-graph-structured data. Then Graph Neural Network (GNN) and attention-based adaptive weighted pooling are employed to represent the state and make MGRO size-agnostic across arbitrary sizes of instances. In addition, a useful reward shaping approach is designed to facilitate model convergence. Extensive numerical experiments on both the publicly available dataset and real industrial dataset from Huawei Supply Chain Business Unit demonstrate the superiority of MGRO over existing baselines.