Robust charging station location and routing-scheduling for electric modular autonomous units
Dongyang Xia, Lixing Yang, Yahan Lu, Shadi Sharif Azadeh
TL;DR
This paper formulates a robust, integrated planning framework for electric modular autonomous units by combining charging-station location design, fleet sizing, and route-scheduling under demand uncertainty in an inter-modal transit setting. It introduces a path-based MILP on a space-time-SoC network and develops a double-decomposition solution that blends column-and-constraint generation with column generation, augmented by network-downsizing and super-arc techniques to achieve scalability. Key contributions include a polyhedral uncertainty-based robust model, three generalizable acceleration methods for the pricing problem, and extensive real-world-inspired numerical experiments showing substantial gains in service quality and significant reductions in passenger costs when using flexible-composition E-MAUs versus fixed-format buses. The results demonstrate that en-route fast-charging and depot charging are both necessary for full-day operations, and the proposed framework delivers near-optimal solutions for large-scale instances with entire-day planning horizons, offering practical guidance for deploying robust electrified transit systems with flexible vehicle technologies.
Abstract
Problem definition: Motivated by global electrification targets and the advent of electric modular autonomous units (E-MAUs), this paper addresses a robust charging station location and routing-scheduling problem (E-RCRSP) in an inter-modal transit system, presenting a novel solution to traditional electric bus scheduling. The system integrates regular bus services, offering full-line or sectional coverage, and short-turning services. Considering the fast-charging technology with quick top-ups, we jointly optimize charging station locations and capacities, fleet sizing, as well as routing-scheduling for E-MAUs under demand uncertainty. E-MAUs can couple flexibly at different locations, and their routing-scheduling decisions include sequences of services, as well as charging times and locations. Methodology: The E-RCRSP is formulated as a path-based robust optimization model, incorporating the polyhedral uncertainty set. We develop a double-decomposition algorithm that combines column-and-constraint generation and column generation armed with a tailored label-correcting approach. To improve computational efficiency and scalability, we propose a novel method that introduces super travel arcs and network downsizing methodologies. Results: Computational results from real-life instances, based on operational data of advanced NExT E-MAUs with cutting-edge batteries provided by our industry partner, indicate that charging at both depots and en-route fast-charging stations is necessary during operations. Moreover, our algorithm effectively scales to large-scale operational cases involving entire-day operations, significantly outperforming state-of-the-art methods. Comparisons with fixed-composition buses under the same fleet investment suggest that our methods are able to achieve substantial reductions in passengers' costs by flexibly scheduling units.