Technology Category
Planning, Routing, and Scheduling: Learning for Planning and SchedulingConstraint Satisfaction and Optimization: Distributed CSP/OptimizationSearch and Optimization: Mixed Discrete/Continuous Search
Collaborative Charging Scheduling via Balanced Bounding Box Methods
🤖 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
Application Category
📝 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
Research questions and friction points this paper is trying to address.
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
Methods, ideas, or system contributions that make the work stand out.
Bi-objective nonlinear integer programming model
Balanced Bounding Box Methods (B3Ms)
Cooperative bargaining for balanced collaboration
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