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Developing an Algorithm Selector for Green Configuration in Scheduling Problems

Carlos March, Christian Perez, Miguel A. Salido

TL;DR

This work addresses solver selection for energy-aware JSP by proposing a feature-driven algorithm selector trained with multiple learners, notably XGBoost. The framework uses diverse JSP features and a triad of solvers (GUROBI, CPLEX, GECODE) to learn which solver yields the best objective outcome, combining makespan, energy, and tardiness into a normalized scalar. The study demonstrates an overall selector accuracy of 84.51% on unseen instances and reveals solver strengths across instance classes, with GUROBI excelling on smaller to mid-sized problems and GECODE exhibiting strong scalability for complex cases. The findings offer a practical pathway to improve scheduling efficiency and sustainability in manufacturing by routing JSP instances to the most suitable solver based on measurable instance characteristics, with future potential in richer feature extraction and broader JSP scenarios.

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.

Developing an Algorithm Selector for Green Configuration in Scheduling Problems

TL;DR

This work addresses solver selection for energy-aware JSP by proposing a feature-driven algorithm selector trained with multiple learners, notably XGBoost. The framework uses diverse JSP features and a triad of solvers (GUROBI, CPLEX, GECODE) to learn which solver yields the best objective outcome, combining makespan, energy, and tardiness into a normalized scalar. The study demonstrates an overall selector accuracy of 84.51% on unseen instances and reveals solver strengths across instance classes, with GUROBI excelling on smaller to mid-sized problems and GECODE exhibiting strong scalability for complex cases. The findings offer a practical pathway to improve scheduling efficiency and sustainability in manufacturing by routing JSP instances to the most suitable solver based on measurable instance characteristics, with future potential in richer feature extraction and broader JSP scenarios.

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.
Paper Structure (13 sections, 23 equations, 5 figures, 2 tables)

This paper contains 13 sections, 23 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Problem Description and Model Formulation
  3. Mixed Integer Programming
  4. Mono-objective optimization
  5. Algorithm Selector
  6. Feature processing
  7. Evaluation
  8. Instances
  9. Results
  10. Complexity analysis
  11. Algorithm selector results
  12. Conclusions
  13. Acknowledgments

Figures (5)

  • Figure 1: Structure of the proposed recommender system.
  • Figure 2: Distribution of timeout and relationship between time and energy.
  • Figure 3: Quantity of optimum, satisfied and unresolved instances by each solver.
  • Figure 4: Number of instances solved
  • Figure 5: Confusion matrix for the algorithm selector predictions (true algorithms on the y-axis, predicted algorithms on the x-axis).