This paper proposes a novel dispatch formulation for micro-mobility vehicles using a Quantum Annealer (QA). In recent years, QA has gained increasing attention as a high-performance solver for combinatorial optimization problems. Meanwhile, micro-mobility services have been rapidly developed as a promising means of realizing efficient and sustainable urban transportation. In this study, the dispatch problem for such micro-mobility services is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, enabling efficient solving through QA. Furthermore, the proposed formulation incorporates historical usage data to enhance operational efficiency. Specifically, customer arrival frequencies and destination distributions are modeled into the QUBO formulation through a Bayesian approach, which guides the allocation of vacant vehicles to designated stations for waiting and charging. Simulation experiments are conducted to evaluate the effectiveness of the proposed method, with comparisons to conventional formulations such as the vehicle routing problem. Additionally, the performance of QA is compared with that of classical solvers to reveal its potential advantages for the proposed dispatch formulation. The effect of reverse annealing on improving solution quality is also investigated.

翻译：本文提出了一种利用量子退火器（QA）进行微移动车辆调度的新模型。近年来，量子退火作为一种高性能的组合优化问题求解器受到越来越多的关注。与此同时，微移动服务作为实现高效、可持续城市交通的一种有前景的方式得到了快速发展。在本研究中，此类微移动服务的调度问题被构建为二次无约束二进制优化（QUBO）问题，从而能够通过量子退火高效求解。此外，所提出的模型结合了历史使用数据以提高运营效率。具体而言，通过贝叶斯方法将客户到达频率和目的地分布建模到QUBO公式中，从而引导空闲车辆分配至指定站点进行等待和充电。通过仿真实验评估了所提方法的有效性，并与车辆路径问题等传统模型进行了比较。此外，还将量子退火的性能与经典求解器进行了对比，以揭示其在所提调度模型中的潜在优势。本文还研究了反向退火对提高解质量的影响。