Optimal Mixed Fleet and Charging Infrastructure Planning to Electrify Demand Responsive Feeder Services with Target Co2 Emission Constraints

Abstract

Electrifying demand-responsive transport systems requires planning the charging infrastructure carefully, considering the trade-offs of charging efficiency and charging infrastructure costs. This study addresses the joint fleet size and charging infrastructure planning for a demand-responsive feeder service under stochastic demand, given a user-defined targeted CO2 emission reduction policy. We propose a bi-level optimization model where the upper-level determines charging station configuration given stochastic demand patterns, whereas the lower-level solves a mixed fleet dial-a-ride routing problem under the CO2 emission and capacitated charging station constraints. An efficient deterministic annealing metaheuristic is proposed to solve the CO2-constrained mixed fleet routing problem. The performance of the algorithm is validated by a series of numerical test instances with up to 500 requests. We apply the model for a real-world case study in Bettembourg, Luxembourg, with different demand and customised CO2 reduction targets. The results show that the proposed method provides a flexible tool for joint charging infrastructure and fleet size planning.

Keywords: mixed fleet, charging infrastructure planning, demand responsive transport, electric vehicle, bi-level optimization