🤖 AI Summary
Job-shop scheduling (JSP) suffers from poor algorithm adaptability and difficulty in jointly optimizing energy efficiency and production performance. Method: This paper proposes the first machine learning–driven algorithm selection framework tailored for green manufacturing. It establishes an energy-efficiency-oriented JSP instance feature set, integrates multi-paradigm solvers—including GUROBI, CPLEX, and GECODE—and employs XGBoost for dynamic solver recommendation. Contribution/Results: The approach innovates in three aspects: (i) pioneering the application of algorithm selection to green scheduling configuration; (ii) introducing an interpretable, energy-efficiency–sensitive feature set; and (iii) enhancing both solution efficiency for small-scale instances and scalability for large-scale scenarios. Experiments demonstrate an algorithm selection accuracy of 84.51%, significantly supporting intelligent, low-carbon decision-making in smart manufacturing.
📝 Abstract
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.