  Coordinating the charging scheduling of electric vehicles for dynamic
dial-a-ride services is challenging considering charging queuing delays and
stochastic customer demand. We propose a new two-stage solution approach to
handle dynamic vehicle charging scheduling to minimize the costs of daily
charging operations of the fleet. The approach comprises two components: daily
vehicle charging scheduling and online vehicle-charger assignment. A new
battery charge scheduling model is proposed to obtain the vehicle charging
schedules by minimizing the costs of vehicle daily charging operations while
satisfying vehicle driving needs to serve customers. In the second stage, an
online vehicle-charger assignment model is developed to minimize the total
vehicle idle time for charges by considering queuing delays at the level of
chargers. An efficient Lagrangian relaxation algorithm is proposed to solve the
large-scale vehicle-charger assignment problem with small optimality gaps. The
approach is applied to a realistic dynamic dial-a-ride service case study in
Luxembourg and compared with the nearest charging station charging policy and
first-come-first-served minimum charging delay policy under different charging
infrastructure scenarios. Our computational results show that the approach can
achieve significant savings for the operator in terms of charging waiting times
(-74.9%), charging times (-38.6%), and charged energy costs (-27.4%). A
sensitivity analysis is conducted to evaluate the impact of the different model
parameters, showing the scalability and robustness of the approach in a
stochastic environment.

Optimization and Control;Systems and Control;Systems and Control