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Constraint programming (CP) is a powerful technique for solving constraintsatisfaction and optimization problems. In CP solvers, the variable orderingstrategy used to select which variable to explore first in the solving processhas a significant impact on solver effectiveness. To address this issue, wepropose a novel variable ordering strategy based on supervised learning, whichwe evaluate in the context of job shop scheduling problems. Our learning-basedmethods predict the optimal solution of a problem instance and use thepredicted solution to order variables for CP solvers. \added[]{Unliketraditional variable ordering methods, our methods can learn from thecharacteristics of each problem instance and customize the variable orderingstrategy accordingly, leading to improved solver performance.} Our experimentsdemonstrate that training machine learning models is highly efficient and canachieve high accuracy. Furthermore, our learned variable ordering methodsperform competitively when compared to four existing methods. Finally, wedemonstrate that hybridising the machine learning-based variable orderingmethods with traditional domain-based methods is beneficial.

Enhancing Constraint Programming via Supervised Learning for Job Shop Scheduling