Marilena Merakou

The growing adoption of electric buses in public transport has intensified the need for efficient scheduling algorithms. In the context of tactical planning, public transport operators must address two interdependent scheduling problems: the Single Depot Vehicle Scheduling Problem for Electric Buses (EB-SD-VSP) and the Crew Scheduling Problem for Electric Buses (EB-CSP). This study introduces a sequential approach, solving EB-SD-VSP via a Mixed-Integer Quadratic Programming (MIQP) model, and then using its solution to generate service blocks for the EB-CSP, which is then solved as a Mixed-Integer Linear Programming (MILP) model. The proposed sequential optimization approach ultimately solves the combined problem of Vehicle and Crew Scheduling for a fleet of Electric Buses (EB-SD-VCSP). Experiments on real-world bus line data from Athens, Greece demonstrate practical applicability of the approach. When compared to a baseline scenario where the services are executed with conventional buses, the proposed method can calculate efficient vehicle timetables and crew schedules for operations with electric buses. The results highlight the benefit of decomposing joint electric bus and crew planning into tractable subproblems while preserving solution quality. These findings offer a scalable tactical-level planning tool for transit agencies transitioning to electric fleets and suggest promising directions for future extensions to multi-depot and real-time scenarios.

This work develops a scheduling tool for electric buses that accounts for daily disruptions while minimizing the operational costs. The contribution of this study lies in the development of electric bus schedules that consider many factors, such as multiple depots, multiple charging stations, and stochastic travel times, providing schedules resilient to extreme conditions. The developed model is a mixed-integer linear program (MILP) with chance constraints. The main decision variables are the assignment of electric vehicles to scheduled trips and charging events to ensure the improved operation of daily services under uncertain conditions. Numerical experiments and a sensitivity analysis based on the variation in travel times are conducted, demonstrating the performance of our solution approach. The results from these experiments indicate that the variant of the model with the chance constraint produces schedules with lower operational costs compared to the case where the chance constraints are not introduced.