用机器学习选最优调度算法，提升工厂节能效率。
Developing an Algorithm Selector for Green Configuration in Scheduling Problems
- 基于问题特征用XGBoost选择合适求解器
- 新实例推荐准确率达84.51%
- 适合制造调度与绿色生产研究者
作业车间调度问题（JSP）是运筹学核心问题，优化能源效率具有重大环境与经济意义。高效调度可提升生产指标并降低能耗，有效平衡生产与可持续目标。由于JSP实例复杂多样，且已有多种求解算法，智能算法选择工具至关重要。本文提出一个框架，通过识别关键问题特征来表征其复杂性，并指导算法选择。利用机器学习技术（特别是XGBoost），该框架可推荐GUROBI、CPLEX和GECODE等最优求解器：其中GUROBI在小规模实例中表现优异，而GECODE在复杂场景中展现出更强的可扩展性。所提算法选择器在新JSP实例上的最佳算法推荐准确率达84.51%，验证了其有效性。通过改进特征提取方法，该框架有望拓展至更多样化的JSP场景，进一步推动制造物流中的效率与可持续发展。
原文摘要 · Abstract (English)
The Job Shop Scheduling Problem (JSP) is central to operations research, primarily optimizing energy efficiency due to its profound environmental and economic implications. Efficient scheduling enhances production metrics and mitigates energy consumption, thus effectively balancing productivity and sustainability objectives. Given the intricate and diverse nature of JSP instances, along with the array of algorithms developed to tackle these challenges, an intelligent algorithm selection tool becomes paramount. This paper introduces a framework designed to identify key problem features that characterize its complexity and guide the selection of suitable algorithms. Leveraging machine learning techniques, particularly XGBoost, the framework recommends optimal solvers such as GUROBI, CPLEX, and GECODE for efficient JSP scheduling. GUROBI excels with smaller instances, while GECODE demonstrates robust scalability for complex scenarios. The proposed algorithm selector achieves an accuracy of 84.51\% in recommending the best algorithm for solving new JSP instances, highlighting its efficacy in algorithm selection. By refining feature extraction methodologies, the framework aims to broaden its applicability across diverse JSP scenarios, thereby advancing efficiency and sustainability in manufacturing logistics.
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