An Optimization Case Study for solving a Transport Robot Scheduling Problem on Quantum-Hybrid and Quantum-Inspired Hardware

TL;DR

This paper compares quantum-hybrid, quantum-inspired, and classical solvers on a real-world transport robot scheduling problem, highlighting their performance differences and providing insights into their application suitability.

Contribution

It offers a comprehensive comparison of quantum, quantum-inspired, and classical optimization methods on an industrial problem, including multiple modeling approaches.

Findings

Digital annealer shows promising solution quality and runtime.

Hybrid quantum annealer has potential but needs further optimization.

Classical solver Gurobi remains competitive in solution quality.

Abstract

We present a comprehensive case study comparing the performance of D-Waves' quantum-classical hybrid framework, Fujitsu's quantum-inspired digital annealer, and Gurobi's state-of-the-art classical solver in solving a transport robot scheduling problem. This problem originates from an industrially relevant real-world scenario. We provide three different models for our problem following different design philosophies. In our benchmark, we focus on the solution quality and end-to-end runtime of different model and solver combinations. We find promising results for the digital annealer and some opportunities for the hybrid quantum annealer in direct comparison with Gurobi. Our study provides insights into the workflow for solving an application-oriented optimization problem with different strategies, and can be useful for evaluating the strengths and weaknesses of different approaches.