In this paper, we establish the Electric Vehicle Routing Problem with Time Windows Based on Driving Cycles (EVRPTW-DC) to optimize the delivery routing of electric vehicles (EVs). As energy consumption may affect the maximal driving range and the recharging behavior of EVs, we first develop a nonlinear electric energy consumption model based on typical driving cycles of suburban and urban areas, with consideration of vehicle load, travel distance, and speed. An adaptive particle swarm optimization algorithm is then designed to solve the problem. Moreover, we study cases built from the actual operational data of Company J and compare the optimal delivery schemes of EVRPTW-DC and EVRPTW under the traditional linear electric energy consumption law. The results show that our nonlinear energy consumption model, which provides a better simulation of energy consumption, can lead to a more realistic delivery plan. Finally, we explore the applicability of the proposed EVPRTW-DC and discuss the conditions of using a linear electric energy consumption coefficient.

The adoption of all-electric vehicles (EVs) has grown rapidly in the transportation industry, particularly for urban parcel deliveries. However, the limited driving range of EVs and the high investment cost of establishing charging infrastructures are still the holdbacks to routing these EVs. In this paper, we present the vehicle routing problem of a mixed fleet of EVs and conventional vehicles (CVs) via intermediate depots as an alternative strategy to address the challenges, with CVs delivering parcels from the central depot to intermediate depots and EVs delivering parcels from intermediate depots to customers. In addition, we propose an intelligent dispatching scheme to allow EVs to be used for multiple routes. A two-phase approach is developed to first cluster the customers to the intermediate depots and then route the mixed fleet. The strategy is implemented for both small- and large-sized instances, and the results show that using an intelligent dispatching scheme can significantly reduce the number of EVs used. Furthermore, the use of smaller-range EVs is also investigated., and a discussion of potential implementation issues is provided.

Long-span bridges located in thunderstorm-prone areas can potentially be struck by downburst transient winds. In this study, the downburst time-varying mean wind was simulated by an impinging jet model based on computational fluid dynamics (CFD). To make the simulation results fit well with the measurements, a parameter optimization method was developed. The objective function was established based on the errors between the simulated characteristic points and the target values from the measurement data. To increase the effectiveness, a Kriging surrogate model that was trained using data from numerical simulations was used. The parameter optimization method and the Kriging model were verified using five groups of test samples. The optimization efficiency was significantly increased by replacing the numerical model with a surrogate model during the optimization iteration. The simulation accuracy was clearly improved by the numerical modeling of a downburst based on optimized parameters. Subsequently, the nonstationary turbulent downburst wind was obtained by the combination of the Hilbert-based nonstationary fluctuations and the CFD-based time-varying trend. Finally, the dynamic response of a long-span bridge subjected to the moving downburst was presented. The results based on the simulation validate the optimized downburst wind field and highlight the significant influence on the bridge’s aerodynamics and buffeting response.