DSevolve: Enabling Real-Time Adaptive Scheduling on Dynamic Shop Floor with LLM-Evolved Heuristic Portfolios

2026-03-29

AI Summary

This study addresses the challenge of maintaining scheduling optimality in dynamic manufacturing environments, where frequent disruptions such as machine failures and incoming orders render existing methods ineffective for real-time adaptation. The authors propose a novel offline-online collaborative framework: in the offline phase, a diverse repository of high-quality scheduling rules is evolved within the MAP-Elites behavioral space using multi-role-guided initialization and topology-aware evolutionary operators; in the online phase, a probe-based state fingerprinting mechanism coupled with rapid forward simulation enables sub-second retrieval and deployment of the most suitable rule. Integrating large language model–driven heuristic design, the approach significantly outperforms state-of-the-art automated heuristics, classical dispatching rules, genetic programming, and deep reinforcement learning methods across 500 dynamic flexible job shop instances derived from real industrial data.

Abstract

In dynamic manufacturing environments, disruptions such as machine breakdowns and new order arrivals continuously shift the optimal dispatching strategy, making adaptive rule selection essential. Existing LLM-powered Automatic Heuristic Design (AHD) frameworks evolve toward a single elite rule that cannot meet this adaptability demand. To address this, we present DSevolve, an industrial scheduling framework that evolves a quality-diverse portfolio of dispatching rules offline and adaptively deploys them online with second-level response time. Multi-persona seeding and topology-aware evolutionary operators produce a behaviorally diverse rule archive indexed by a MAP-Elites feature space. Upon each disruption event, a probe-based fingerprinting mechanism characterizes the current shop floor state, retrieves high-quality candidate rules from an offline knowledge base, and selects the best one via rapid look-ahead simulation. Evaluated on 500 dynamic flexible job shop instances derived from real industrial data, DSevolve outperforms state-of-the-art AHD frameworks, classical dispatching rules, genetic programming, and deep reinforcement learning, offering a practical and deployable solution for intelligent shop floor scheduling.

Problem

• dynamic scheduling
• adaptive dispatching
• shop floor disruptions
• heuristic selection
• real-time response

Innovation

• LLM-evolved heuristics
• adaptive scheduling
• MAP-Elites
• dispatching rule portfolio
• real-time shop floor response