Large Language Model-Driven Cooperative Operator Ensemble Evolution for Permutation Flow Shop Scheduling

• URL: http://arxiv.org/abs/2606.15334v2
• Date: Tue, 16 Jun 2026 10:50:52 GMT
• Title: Large Language Model-Driven Cooperative Operator Ensemble Evolution for Permutation Flow Shop Scheduling
• Abstract summary: This work proposes a multi-triggered IG algorithm, termed IG-DOE, which enhances exploration by switching among heterogeneous destruction operators along a single search trajectory.<n> Experiments on the challenging VRF-hard-large benchmark show that the DOE evolved from smaller problem instances to larger unseen instances.<n>Additional experiments on real-world industrial-data-derived instances further show that the evolved DOE can generalize effectively to different data without additional adaptation.
• Score: 10.238655892058295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The permutation flow shop scheduling problem (PFSP) is a classical NP-hard combinatorial optimization problem in intelligent manufacturing. In practice, PFSP is commonly addressed using metaheuristic algorithms, among which the iterated greedy (IG) algorithm is widely adopted due to its simplicity and strong empirical performance. However, classical IG relies on a single fixed destruction operator, which often limits exploration and leads to search stagnation on large and complex problem instances. To address this issue, this work proposes a multi-operator IG algorithm, termed IG-DOE, which enhances exploration by switching among heterogeneous destruction operators along a single search trajectory. The core mechanism, called stagnation-triggered sequential switching, activates the next destruction operator in an ordered destruction operator ensemble (DOE) when stagnation is detected, thereby enriching the perturbation behavior of classical IG. Moreover, to reduce reliance on expert-crafted operators, a large language model (LLM)-assisted framework, termed SCOE, is introduced to automatically construct a high-quality DOE through stagewise evolution, state-awareness, and cooperative evaluation. Experiments on the challenging VRF-hard-large benchmark show that the DOE evolved from smaller problem instances generalizes well to larger unseen instances. Under the same CPU-time limit, IG-DOE obtained much better average performance than QIG, a state-of-the-art IG algorithm. Additional experiments on real-world industrial-data-derived instances further show that the evolved DOE can generalize effectively to different data distributions without additional adaptation.

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