Google vs IBM: A Constraint Solving Challenge on the Job-Shop Scheduling Problem
This work provides a practical comparison for researchers and practitioners choosing solvers for industrial scheduling problems, though it is incremental as it focuses on benchmarking existing tools.
The paper compared the performance of Google's OR-Tools and IBM's CP Optimizer constraint solvers on the job-shop scheduling problem, finding that CP Optimizer generally produced better solutions but OR-Tools was faster on some instances.
The job-shop scheduling is one of the most studied optimization problems from the dawn of computer era to the present day. Its combinatorial nature makes it easily expressible as a constraint satisfaction problem. In this paper, we compare the performance of two constraint solvers on the job-shop scheduling problem. The solvers in question are: OR-Tools, an open-source solver developed by Google and winner of the last MiniZinc Challenge, and CP Optimizer, a proprietary IBM constraint solver targeted at industrial scheduling problems. The comparison is based on the goodness of the solutions found and the time required to solve the problem instances. First, we target the classic benchmarks from the literature, then we carry out the comparison on a benchmark that was created with known optimal solution, with size comparable to real-world industrial problems.