  Constraint programming (CP) is a powerful technique for solving constraint
satisfaction and optimization problems. In CP solvers, the variable ordering
strategy used to select which variable to explore first in the solving process
has a significant impact on solver effectiveness. To address this issue, we
propose a novel variable ordering strategy based on supervised learning, which
we evaluate in the context of job shop scheduling problems. Our learning-based
methods predict the optimal solution of a problem instance and use the
predicted solution to order variables for CP solvers. \added[]{Unlike
traditional variable ordering methods, our methods can learn from the
characteristics of each problem instance and customize the variable ordering
strategy accordingly, leading to improved solver performance.} Our experiments
demonstrate that training machine learning models is highly efficient and can
achieve high accuracy. Furthermore, our learned variable ordering methods
perform competitively when compared to four existing methods. Finally, we
demonstrate that hybridising the machine learning-based variable ordering
methods with traditional domain-based methods is beneficial.
