Heterogeneous Graph Neural Network-Guided Adaptive Large Neighborhood Search for Flexible Job Shop Scheduling in Panel Furniture Production
TL;DR
An adaptive large neighborhood search method guided by a heterogeneous graph neural network (HeteroGNN) method, termed HeteroGNN-ALNS, is developed to balance completion time, waiting time, and workload during the production process.
Abstract
This study formulates panel furniture production as a flexible job shop scheduling problem (FJSP) with constraints on transport resources under varying production conditions. An adaptive large neighborhood search (ALNS) method guided by a heterogeneous graph neural network (HeteroGNN), termed HeteroGNN-ALNS, is developed to balance completion time, waiting time, and workload during the production process. The current scheduling state in ALNS is represented as a heterogeneous graph, where panel jobs and production resources are modeled as different node types, and assignment, transport, and sequence information is represented by different edge types. A search state vector is also introduced to describe the current search process. The HeteroGNN is trained using an actor–critic method to guide neighborhood operator selection and destroy set construction in ALNS. Experiments are conducted under four production conditions and five job scales. The results show that HeteroGNN-ALNS achieves better overall scheduling performance than dispatching rules and representative search methods. Statistical and ablation analyses further verify the effectiveness of the proposed method.
