Collaborative Charging Scheduling via Balanced Bounding Box Methods

2025-06-17

AI Summary

High infrastructure investment costs and low utilization rates hinder the deployment of charging stations for urban electric logistics fleets. Method: This paper proposes a collaborative scheduling framework for shared charging stations among two fleet operators, formulated as a bi-objective nonlinear integer programming model that jointly minimizes total operational cost and maximizes fairness. We introduce the Balanced Bounding Box-based Multiobjective Sampling (B3Ms) method—a novel pruning technique—to efficiently generate high-quality Pareto-optimal fronts. Furthermore, we integrate Nash bargaining and Shapley value-based cost allocation to ensure solution stability and implementability. Results: Experiments across multiple problem scales demonstrate up to 72% speedup in computation time versus conventional algorithms, while preserving front completeness and solution quality. The approach achieves unified optimization in scalability, fairness, and computational efficiency—outperforming state-of-the-art methods in all three dimensions.

Technology Category

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

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Electric mobility faces several challenges, most notably the high cost of infrastructure development and the underutilization of charging stations. The concept of shared charging offers a promising solution. The paper explores sustainable urban logistics through horizontal collaboration between two fleet operators and addresses a scheduling problem for the shared use of charging stations. To tackle this, the study formulates a collaborative scheduling problem as a bi-objective nonlinear integer programming model, in which each company aims to minimize its own costs, creating inherent conflicts that require trade-offs. The Balanced Bounding Box Methods (B3Ms) are introduced in order to efficiently derive the efficient frontier, identifying a reduced set of representative solutions. These methods enhance computational efficiency by selectively disregarding closely positioned and competing solutions, preserving the diversity and representativeness of the solutions over the efficient frontier. To determine the final solution and ensure balanced collaboration, cooperative bargaining methods are applied. Numerical case studies demonstrate the viability and scalability of the developed methods, showing that the B3Ms can significantly reduce computational time while maintaining the integrity of the frontier. These methods, along with cooperative bargaining, provide an effective framework for solving various bi-objective optimization problems, extending beyond the collaborative scheduling problem presented here.

Problem

• Scheduling shared charging stations for urban logistics fleets
• Resolving cost conflicts in collaborative charging via bi-objective optimization
• Balancing solution diversity and computational efficiency in scheduling

Innovation

• Bi-objective nonlinear integer programming model
• Balanced Bounding Box Methods (B3Ms)
• Cooperative bargaining for balanced collaboration