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• A zonal-based flexible bus service (ZBFBS) is scheduled under dynamic stochastic demand and time-dependent travel time. • Zonal-based time-space network is proposed for flexible bus scheduling with flexibility in arrival and departure times. • A reliability-based gradient solution method with relaxations is developed to solve this inherently non-convex problem. This paper schedules the zonal-based flexible bus service (ZBFBS) considering elastic stochastic demand, stochastic location, time-dependent travel time, and passenger time window constraints based on a scheduled-based formulation. Unlike a traditional time–space network that stipulates the precise arrival and departure times on specific nodes, a zonal-based time–space network is proposed to define the routes in terms of zonal visits of the flexible buses while allowing for flexibility in their arrival and departure times to cater for randomness. The ZBFBS scheduling problem is formulated as a two-stage decision-dependent stochastic problem with recourse. The first stage schedules the zonal visits of flexible buses and the second stage matches each passenger with either flexible bus or ad hoc service, with the latter incurring extra cost to carry the unmatched passengers. To effectively solve the problem, a state-augmented network, that integrates time and zone, is proposed to reduce the number of variables. Moreover, relaxation formulations based on vehicle types and routes are introduced, with an insertion heuristic implemented for vehicle scheduling. The problem is solved by a gradient-based solution approach. Numerical studies demonstrate the efficiency and quality of the solution methods under a variety of ride requests, as well as its advantage over the frequency-based approach in substantially reducing the ad hoc service cost. The applicability of the model is validated by solving an instance of Chengdu, China, with real data. [ABSTRACT FROM AUTHOR]

This study presents an event-driven discrete-event simulation (EDDES) approach based on an O2DES framework for a one-way electric carsharing system (OECS). The developed model mainly consists of three modules: station, electric vehicle (EV), and link. The system carefully considers the impact of road congestion on travel speed and designs a detailed charging process for EVs to approximate the real world. Based on the high speed of EDDES, a simulation-optimization framework that jointly determines station capacities, fleet size, and trip pricing to maximize the net revenue of operators is proposed. A simultaneous perturbation stochastic approximation (SPSA) algorithm is adopted to solve this problem. A case of EVCard in Chengdu is conducted to demonstrate the efficiency of the proposed framework. Several control experiments focusing on various pricing schemes are introduced to verify the advantage of optimization results further. Moreover, a one-way gasoline carsharing system (OGCS) is optimized to contrast with optimal OECS. These comparisons reveal some interesting findings: (1) from the perspective of operators, employing dynamic pricing achieves at least a 57.03% increase in net revenue compared to fixed pricing strategies; (2) from service, the optimal configurations and trip pricing of OECS effectively avoids vehicle overstock at stations; and (3) in terms of greener production, the optimal OECS could sustain a larger fleet size and a higher degree of cleaner production, and its profit generated by per CO 2 emission is over six times higher than that of the optimal OGCS. • This paper proposes an event-driven simulation model for electric carsharing systems. • The charging speed, range anxiety, and state of charge (SOC) are considered. • A simulation-optimization method is proposed for seeking the optimal system designs. • A simultaneous perturbation stochastic approximation is implied to solve the issue. • The greenhouse gas emission is captured to analyze the degree of green production. [ABSTRACT FROM AUTHOR]