发表机构
Qunevo GmbH（Qunevo有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
APEX提出一个可扩展的生产调度框架，结合通用模型与混合多目标搜索，通过智能体辅助数据准备、自然语言交互及编码扩展，在69个实例上优于NSGA-II等算法，并验证了GPT-6-astra交互层的有效性。
AI 中文摘要
生产调度需要反映运营约束的现实模型以及平衡竞争目标的高效方法。将这些方法付诸实践还需要数据集成、模型调整和专家专业知识。我们提出了APEX，一个可扩展的生产调度框架，围绕通用模型和混合多目标搜索构建。智能体辅助支持调度和模型细化：智能体准备数据并以自然语言探索场景，而编码智能体帮助实现和测试新的约束和目标。共享的构建和检查程序将这些调整连接到调度核心。我们在69个公共作业车间、柔性作业车间和置换流水车间实例上，将八种APEX配置与NSGA-II、SPEA2、MOEA/D和SMS-EMOA进行基准测试，评估工作负载完成时间（完工时间）、总作业流程时间和计算时间。混合配置实现了最佳的总体解质量，尽管领先方法取决于问题类别和目标。一项单独的合成工作流研究使用OpenAI的GPT-6-astra作为人类规划者与算法核心之间的交互层，测试规则添加、计划和目标更改以及假设比较。所有24个会话都完成了请求的更改，并通过了对保存模型和调度的独立检查。一项单独的编码评估通过预定义的扩展钩子产生了六个额外目标或硬约束的原生实现。所有实现均通过独立检查，无需修改核心。
英文摘要
Production scheduling requires realistic models that reflect operational constraints and efficient methods that balance competing goals. Putting these methods into use also requires data integration, model adaptation and specialist expertise. We present APEX, an extensible production scheduling framework built around a general model and hybrid multiobjective search. Agent assistance supports both scheduling and model refinement: agents prepare data and explore scenarios in natural language, while coding agents help implement and test new constraints and objectives. Shared construction and checking procedures connect these adaptations to the scheduling core. We benchmark eight APEX configurations against NSGA-II, SPEA2, MOEA/D and SMS-EMOA on 69 public job-shop, flexible job-shop and permutation flow-shop instances, assessing workload completion time (makespan), total job flowtime and computation time. Hybrid configurations achieve the best aggregate solution quality, although the leading method depends on the problem class and objective. A separate synthetic workflow study uses OpenAI's GPT-6-astra as an interaction layer between the human planner and the algorithmic core, testing rule additions, plan and objective changes, and what-if comparisons. All 24 sessions completed the requested changes and passed independent checks of saved models and schedules. A separate coding evaluation produced six native implementations of an additional objective or hard constraint through predefined extension hooks. All passed independent checks without modifying the core.
