# Google vs IBM: A Constraint Solving Challenge on the Job-Shop Scheduling Problem

arXiv: https://arxiv.org/abs/1909.08247 · HTML: https://mlsanity.com/paper/1909.08247/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Solving a Real-World Package Delivery Routing Problem Using Quantum Annealers](https://mlsanity.com/paper/2403.15114/) (2024)
- [Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem](https://mlsanity.com/paper/2403.09249/) (2024)
- [Large-Scale Benchmarks for the Job Shop Scheduling Problem](https://mlsanity.com/paper/2102.08778/) (2021)

## Abstract (arXiv)

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.