Summary of Developing An Algorithm Selector For Green Configuration in Scheduling Problems, by Carlos March et al.
Developing an Algorithm Selector for Green Configuration in Scheduling Problems
by Carlos March, Christian Perez, Miguel A. Salido
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a machine learning-based framework for selecting suitable algorithms to solve Job Shop Scheduling Problems (JSPs), which is crucial for optimizing energy efficiency and balancing productivity with sustainability. The framework identifies key problem features that characterize complexity and guides the selection of optimal solvers, including GUROBI, CPLEX, and GECODE. XGBoost is used as a machine learning technique to recommend the best algorithm for solving new JSP instances, achieving an accuracy of 84.51%. The proposed algorithm selector demonstrates efficacy in recommending optimal algorithms for efficient JSP scheduling. The framework’s applicability can be broadened across diverse JSP scenarios by refining feature extraction methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us solve a big problem called Job Shop Scheduling Problem (JSP). This problem is important because it helps us make factories more energy-efficient and less wasteful. To do this, we need to choose the right algorithm to help us schedule jobs efficiently. The paper proposes a new way to choose these algorithms using machine learning techniques. It’s like a special tool that looks at the characteristics of each job scheduling problem and recommends the best algorithm to solve it. This tool is really good at choosing the right algorithm, getting it right 84.51% of the time! By making this tool better, we can use it for many different types of job scheduling problems. |
Keywords
» Artificial intelligence » Feature extraction » Machine learning » Xgboost
