Hybrid Quantum-Classical Branch-and-Price for Intra-Day Electric Vehicle Charging Scheduling via Partition Coloring

2026-03-22

AI Summary

This study addresses the intra-day electric vehicle charging scheduling problem in public parking lots and fleet operations, where limited charger capacity and short vehicle dwell times pose significant challenges. The problem is modeled as a partition coloring problem, and a branch-and-price framework integrating quantum-inspired optimization is proposed. The master problem, solved by Gurobi, selects feasible charging time-slot combinations, while the subproblem is reformulated as a maximum independent set problem and encoded into a QUBO formulation, which is then solved using quantum-inspired algorithms—BSA and SimCIM—from MindQuantum. This work represents the first integration of quantum-inspired heuristics into a classical branch-and-price scheme, demonstrating superior performance over a pure Gurobi baseline on large-scale, hard instances by effectively closing the optimality gap and proving optimality within the same time limit.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

The rapid deployment of electric vehicles (EVs) in public parking facilities and fleet operations raises challenging intra-day charging scheduling problems under tight charger capacity and limited dwell times. We model this problem as a variant of the Partition Coloring Problem (PCP), where each vehicle defines a partition, its candidate charging intervals are vertices, and temporal and resource conflicts are represented as edges in a conflict graph. On this basis, we design a branch-and-price algorithm in which the restricted master problem selects feasible combinations of intervals, and the pricing subproblem is a maximum independent set problem. The latter is reformulated as a quadratic unconstrained binary optimization (QUBO) model and solved by quantum-annealing-inspired algorithms (QAIA) implemented in the MindQuantum framework, specifically the ballistic simulated branching (BSB) and simulated coherent Ising machine (SimCIM) methods, while the master problem is solved by Gurobi. Computational experiments on a family of synthetic EV charging instances show that the QAIA-enhanced algorithms match the pure Gurobi-based branch-and-price baseline on small and medium instances, and clearly outperform it on large and hard instances. In several cases where the baseline reaches the time limit with non-zero optimality gaps, the QAIA-based variants close the gap and prove optimality within the same time budget. These results indicate that integrating QAIA into classical decomposition schemes are a promising direction for large-scale EV charging scheduling and related PCP applications.

Problem

• electric vehicle charging scheduling
• Partition Coloring Problem
• intra-day scheduling
• charger capacity constraints
• dwell time limitations

Innovation

• Hybrid Quantum-Classical Optimization
• Branch-and-Price
• Quadratic Unconstrained Binary Optimization (QUBO)
• Partition Coloring Problem
• Quantum-Annealing-Inspired Algorithms