Fairness-aware Strategic Design of Station-based Electric Car-Sharing Systems

TL;DR

This paper develops a fairness-aware model for designing station-based electric car-sharing systems, balancing revenue and service equity using bi-objective optimization and advanced solution algorithms.

Contribution

It introduces a novel bi-objective trajectory-based formulation incorporating fairness paradigms and demand heterogeneity, with solution methods for practical, large-scale applications.

Findings

The framework effectively balances revenue and service equity in real-world case studies.

Fairness paradigms significantly influence system design and operational trade-offs.

The proposed algorithms generate Pareto frontiers, aiding decision-makers in sustainable mobility planning.

Abstract

Electric car-sharing systems are pivotal for sustainable urban mobility, but their strategic design is complicated by operational constraints, particularly those arising from the charging needs of electric vehicles. The success of these systems hinges on integrating long-term investment decisions (such as station locations, charger capacities, and fleet size) with daily operational realities, including vehicle routing to serve user trip requests and battery management. While existing integrated models address this strategic-operational link, they have prioritized economic efficiency, overlooking the critical dimension of service equity. This paper addresses this gap by making fairness a central design principle, operationalized through two distinct paradigms, namely, service-rate disparity and max-min fairness, measured explicitly via realized group service rates rather than static spatial accessibility. To capture demand heterogeneity, we adopt a multi-day representative-demand setting, and develop a bi-objective trajectory-based formulation that jointly optimizes revenue and service equity. We develop a solution framework in which a branch-and-price algorithm solves the single-objective variants of the models, embedded within an exact bi-objective procedure to generate the Pareto frontier and complemented by a diving-heuristic-based approach for obtaining high-quality frontier approximations for larger instances. Through extensive computational experiments, including a Vienna-based real-data case study, we provide key managerial insights into the fundamental trade-offs between revenue, equity, and system design, demonstrating that the proposed framework can serve as a useful decision-support tool for designing station-based electric car-sharing systems that are both economically viable and socially inclusive.