Solving a real-world modular logistic scheduling problem with a quantum-classical metaheuristics

TL;DR

This paper compares quantum-classical metaheuristics and classical solvers on industry-relevant AGV scheduling problems, revealing the impact of modeling techniques and introducing a novel quantum-compatible optimization model.

Contribution

It introduces a new binary optimization model suitable for quantum-classical metaheuristics and provides a detailed performance comparison with traditional solvers.

Findings

Quantum-classical metaheuristics benefit from new modeling approaches.

Solver performance varies significantly with modeling techniques.

Quantum methods show potential for complex industrial scheduling problems.

Abstract

This study evaluates the performance of a quantum-classical metaheuristic and a traditional classical mathematical programming solver, applied to two mathematical optimization models for an industry-relevant scheduling problem with autonomous guided vehicles (AGVs). The two models are: (1) a time-indexed mixed-integer linear program, and (2) a novel binary optimization problem with linear and quadratic constraints and a linear objective. Our experiments indicate that optimization methods are very susceptible to modeling techniques and different solvers require dedicated methods. We show in this work that quantum-classical metaheuristics can benefit from a new way of modeling mathematical optimization problems. Additionally, we present a detailed performance comparison of the two solution methods for each optimization model.