Real World Application of Quantum-Classical Optimization for Production Scheduling

TL;DR

This paper benchmarks quantum-classical optimization methods on a real-world industrial scheduling problem, demonstrating competitive and sometimes faster solutions with quantum approaches for large problem sizes.

Contribution

It presents a comprehensive comparison of classical and quantum-classical solvers on a large-scale, real-world scheduling problem, highlighting the potential of quantum methods in industry.

Findings

Quantum-classical hybrid solver achieves competitive solutions.

Quantum approach sometimes provides speedups over classical methods.

Benchmarking on large problem sizes up to 150,000 variables.

Abstract

This work is a benchmark study for quantum-classical computing method with a real-world optimization problem from industry. The problem involves scheduling and balancing jobs on different machines, with a non-linear objective function. We first present the motivation and the problem description, along with different modeling techniques for classical and quantum computing. The modeling for classical solvers has been done as a mixed-integer convex program, while for the quantum-classical solver we model the problem as a binary quadratic program, which is best suited to the D-Wave Leap's Hybrid Solver. This ensures that all the solvers we use are fetched with dedicated and most suitable model(s). Henceforth, we carry out benchmarking and comparisons between classical and quantum-classical methods, on problem sizes ranging till approximately 150000 variables. We utilize an industry grade classical solver and compare its results with D-Wave Leap's Hybrid Solver. The results we obtain from D-Wave are highly competitive and sometimes offer speedups, compared to the classical solver.