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

arXiv: https://arxiv.org/abs/2309.09736 · HTML: https://mlsanity.com/paper/2309.09736/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 59 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 4 of 4)

- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [Benchmark of quantum-inspired heuristic solvers for quadratic unconstrained binary optimization](https://mlsanity.com/paper/2104.14096/) (2021)
- [Optimization of Robot Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms](https://mlsanity.com/paper/2206.03651/) (2022)
- [Solving the Extended Job Shop Scheduling Problem with AGVs -- Classical and Quantum Approaches](https://mlsanity.com/paper/2109.04830/) (2021)

## Built on by (most cited first, 1 of 1)

- [Solving QUBO on the Loihi 2 Neuromorphic Processor](https://mlsanity.com/paper/2408.03076/) (2024)

## Abstract (arXiv)

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.