Research on Scheduling Optimization for Flexible Flow Shops with Tooling Matching Based on Hier-seq2seq

Abstract

Flexible flow shop scheduling with tooling matching constraints is a complex combinatorial optimization problem with significant practical importance in manufacturing. This paper proposes a hierarchical sequence-to-sequence (Hier-seq2seq) model for scheduling optimization in flexible flow shops considering tooling matching requirements. The approach leverages deep learning to effectively handle the complex dependencies between jobs, machines, and tooling assignments, demonstrating superior performance compared to conventional methods.