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.

翻译：作业车间调度问题（JSP）是运筹学领域的核心课题，因其深远的环境与经济影响，主要致力于优化能源效率。高效调度不仅能提升生产指标，还能降低能耗，从而有效平衡生产效率与可持续性目标。鉴于JSP实例的复杂性和多样性，以及针对这些挑战开发的一系列算法，智能算法选择工具显得至关重要。本文提出一种框架，旨在识别表征问题复杂度的关键特征，并指导合适算法的选择。该框架利用机器学习技术（特别是XGBoost），推荐GUROBI、CPLEX和GECODE等优化求解器以实现高效JSP调度。GUROBI在处理较小规模实例时表现优异，而GECODE在复杂场景中展现出强大的可扩展性。所提出的算法选择器在为新JSP实例推荐最佳算法时达到84.51\%的准确率，彰显了其在算法选择方面的有效性。通过改进特征提取方法，该框架旨在拓展其在不同JSP场景中的适用性，从而推动制造物流领域的效率与可持续发展。