Micro-mobility dispatch optimization via quantum annealing incorporating historical data

TL;DR

This paper introduces a quantum annealing-based dispatch optimization method for micro-mobility vehicles that incorporates historical data, demonstrating potential advantages over classical solvers through simulation experiments.

Contribution

It formulates the micro-mobility dispatch problem as a QUBO and integrates historical usage data using a Bayesian approach, enabling efficient quantum annealing solutions.

Findings

Quantum annealing shows potential advantages over classical solvers.

Incorporating historical data improves dispatch efficiency.

Reverse annealing enhances solution quality.

Abstract

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.