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

TL;DR

This paper compares the performance of Google’s open-source OR-Tools and IBM’s CP Optimizer on job-shop scheduling problems, evaluating solution quality and solving time on classic and industrial-sized benchmarks.

Contribution

It provides a comparative analysis of two leading constraint solvers on job-shop scheduling, highlighting their strengths and limitations in solving real-world industrial problems.

Findings

OR-Tools and CP Optimizer show different strengths in solution quality and speed.

CP Optimizer performs better on industrial-sized problems.

Both solvers effectively solve classic benchmark instances.

Abstract

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.