Collaborative Charging Scheduling via Balanced Bounding Box Methods
This work provides an efficient framework for bi-objective optimization in shared charging scheduling, benefiting fleet operators and urban logistics, though it is an incremental improvement over existing multi-objective optimization methods.
The paper addresses collaborative charging scheduling for two fleet operators, formulating it as a bi-objective nonlinear integer programming model. The Balanced Bounding Box Methods (B3Ms) reduce computational time while preserving solution diversity, with cooperative bargaining ensuring balanced collaboration.
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.