Developing an Algorithm Selector for Green Configuration in Scheduling Problems

TL;DR

This paper presents a machine learning-based framework for selecting optimal algorithms to solve Job Shop Scheduling Problems, improving energy efficiency and sustainability in manufacturing.

Contribution

It introduces a novel algorithm selector using XGBoost that accurately recommends suitable solvers for JSP instances based on their features.

Findings

Achieves 84.51% accuracy in algorithm recommendation

Effectively distinguishes between small and complex JSP instances

Enhances scheduling efficiency and sustainability

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.