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English(EN) Large Language Model-Driven Cooperative Operator Ensemble Evolution for Permutation Flow Shop Scheduling

LLM 自动化算子集成以解决调度问题

研究人员开发了一种新方法来增强迭代贪婪(IG)算法,以解决复杂的置换流水车间调度问题(PFSP)。这种新方法 IG-DOE 利用破坏算子集成(DOE)来增强探索并防止搜索停滞。一项关键创新是 SCOE 框架,该框架采用大型语言模型(LLM)自动构建此 DOE,从而减少了手动设计算子的需求。在具有挑战性的基准和真实世界数据上的实验表明,LLM 演化的 DOE 能有效地泛化到未见过的问题实例,并且优于现有的最先进算法。 AI

影响 LLM 驱动的复杂优化任务自动化可以简化工业流程并提高效率。

排序理由 该集群包含一篇详细介绍新算法和实验结果的学术论文。

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

LLM 自动化算子集成以解决调度问题

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报道来源 [2]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Ke Tang ·

    基于大型语言模型的协作运算符集成演化用于排列流水车间调度

    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…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Ke Tang ·

    大型语言模型驱动的置换流水车间调度协同算子集成演化

    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…