Multi-objective electric bus scheduling problem considering multi-vehicle types.

The electrification of public transportation, particularly the transition from diesel/gas to electric bus fleets, poses a unique set of challenges related to efficient scheduling and operational management. The Electric Bus Scheduling Problem (EBSP) is a critical aspect of this transition, as it involves optimizing the assignment of electric buses to predetermined timetable trips to minimize fleet size and operational costs. This paper presents a systematic approach to address the multi-depot and multi-vehicle type electric bus scheduling problem (MD-MVT-EBSP) within the complex framework of urban transportation systems. The problem is tackled by developing an optimization model, which, alongside the genetic algorithm, results in finding the optimal schedule and recharging trips while the total cost of using an electric bus fleet is minimized. The proposed method not only achieves the optimal schedule but also addresses the crucial aspects of determining the required number of each vehicle type and the associated charging specifications needed to fulfill the timetable trips. A rigorous investigation of a case study is performed by employing a real-world transit network dataset from Canadian cities.