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Coordinating the charging scheduling of electric vehicles for dynamicdial-a-ride services is challenging considering charging queuing delays andstochastic customer demand. We propose a new two-stage solution approach tohandle dynamic vehicle charging scheduling to minimize the costs of dailycharging operations of the fleet. The approach comprises two components: dailyvehicle charging scheduling and online vehicle-charger assignment. A newbattery charge scheduling model is proposed to obtain the vehicle chargingschedules by minimizing the costs of vehicle daily charging operations whilesatisfying vehicle driving needs to serve customers. In the second stage, anonline vehicle-charger assignment model is developed to minimize the totalvehicle idle time for charges by considering queuing delays at the level ofchargers. An efficient Lagrangian relaxation algorithm is proposed to solve thelarge-scale vehicle-charger assignment problem with small optimality gaps. Theapproach is applied to a realistic dynamic dial-a-ride service case study inLuxembourg and compared with the nearest charging station charging policy andfirst-come-first-served minimum charging delay policy under different charginginfrastructure scenarios. Our computational results show that the approach canachieve significant savings for the operator in terms of charging waiting times(-74.9%), charging times (-38.6%), and charged energy costs (-27.4%). Asensitivity analysis is conducted to evaluate the impact of the different modelparameters, showing the scalability and robustness of the approach in astochastic environment.

Two-stage battery recharge scheduling and vehicle-charger assignment policy for dynamic electric dial-a-ride services