Collaborative Charging Scheduling via Balanced Bounding Box Methods

TL;DR

This paper introduces Balanced Bounding Box Methods (B3Ms) for efficient bi-objective optimization in collaborative electric vehicle charging scheduling, balancing costs between fleet operators.

Contribution

The study develops B3Ms to efficiently approximate the efficient frontier in bi-objective scheduling, enhancing computational speed and solution diversity.

Findings

B3Ms significantly reduce computational time.

Numerical case studies validate scalability and effectiveness.

Cooperative bargaining ensures balanced solutions.

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.