The shift towards environmentally friendly and efficient electric bus transportation systems oftentimes raises unexpected operational issues. This study models the Electric Bus Charging Station Location Problem (EB-CSLP) to develop a more resilient charging infrastructure, focusing on time-related and energy consumption uncertainties, specifically inter-station travel time delays. The model accommodates various charger types and maintains time continuity in the charging of electric buses. Initially formulated as a mixed-integer nonlinear program (MINLP), our stochastic optimization model is reformulated into a mixed-integer linear program (MILP) which minimizes both deadheading times and queue waiting times at the charging locations. The stochastic optimization model is tested in a real-world case study in Athens, Greece, considering multiple scenarios with varying inter-station travel times and energy consumption. The results demonstrate its effectiveness as a potential decision-support tool for selecting the optimal charger types and charging station locations under travel time and energy-related uncertainties.

Public transport agencies need to leverage on emerging technologies to remain competitive in a mobility landscape that is increasingly subject to disruptive mobility services ranging from ride-hailing to shared micro-mobility. Customized bus (CB) is an innovative transit system that provides advanced, personalized, and flexible demand-responsive transit service by using digital travel platforms. One of the challenging tasks in planning and operating a CB system is to efficiently and practically schedule a set of CB vehicles while meeting passengers’ personalized travel demand. Previous studies assume that CB passengers’ preferred pickup or delivery time is within a pre-defined hard time window, which is fixed and cannot change. However, some recent studies show that introducing soft flexible time windows can further reduce operational costs. Considering soft flexible time windows, this study first proposes a nearest neighbour-based passenger-to-vehicle assignment algorithm to assign CB passengers to vehicle trips and generate the required vehicle service trips. Then, a novel bi-objective integer programming model is proposed to optimize CB operation cost (measured by fleet size) and level of service (measured by passenger departure time deviation penalty cost). Model reformulations are conducted to make the bi-objective model solvable by using commercial optimization solvers, together with a deficit function-based graphical vehicle scheduling technique. A novel two-stage human–machine collaborative optimization methodology, which makes use of both machine intelligence and human intelligence to collaboratively solve the problem, is developed to generate more practical Pareto-optimal CB scheduling results. Computation results of a real-world CB system demonstrate the effectiveness and advantages of the proposed optimization model and solution methodology.