Micro-mobility dispatch optimization via quantum annealing incorporating historical data

2026-01-28

AI Summary

This work proposes a quantum optimization approach for the combinatorial problem of urban micro-mobility vehicle scheduling, integrating historical passenger flow data to enhance decision-making. By employing Bayesian modeling to characterize demand uncertainty at docking stations, the method uniquely embeds this probabilistic representation into a Quadratic Unconstrained Binary Optimization (QUBO) framework, enabling dynamic allocation of idle vehicles and coordinated charging schedules. The resulting QUBO formulation is solved using quantum annealing, augmented with a reverse annealing strategy to improve solution quality. Simulation experiments demonstrate that the proposed approach outperforms conventional vehicle routing models in both operational efficiency and sustainability, thereby validating the practical potential of quantum annealing for real-world scheduling applications.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• micro-mobility
• dispatch optimization
• quantum annealing
• historical data
• QUBO

Innovation

• Quantum Annealing
• Micro-mobility Dispatch
• QUBO Formulation
• Historical Data Integration
• Reverse Annealing