This paper presents a method for determining the deployment of one-way electric carsharing services within a designated region that maximizes the total profit of the operator. A mixed integer non-linear program model is built, with a strategic planning level that decides the fleet size and the station capacity and an operational level that decides on the required relocation operations. The state of charge (SOC) of the vehicles parked in one station is assumed to follow a continuous distribution. A rolling horizon method is used to optimize the operational decisions over the course of a day, considering demand fluctuations and the limited battery capacity of the vehicles. A golden section line search method and a shadow price algorithm are developed to optimize the fleet size and station capacity, with the results feeding back to the carsharing operations. To demonstrate the applicability of the formulated models and solution algorithms, a large-scale case study is conducted for Suzhou Industrial Park, China as the region of operation. A two-step verification method that combines an optimization model via tracking of individual vehicle SOC and a discrete event simulation, demonstrates the accuracy of the SOC distribution model. Managerial insights from the application are also presented.

In this paper, we study the dial-a-ride problem of ride-sharing automated taxis (ATs) in an urban road network, considering the traffic congestion caused by the ATs. This shared automated mobility system is expected to provide a seamless door-to-door service for urban travellers, much like what the existing transportation network companies (TNC) do, but with decreased labour cost and more flexible relocation operations due to the vehicles’ automation. We propose an integer non-linear programming (INLP) model that optimizes the routing of the ATs to maximize the system profit, depending on dynamic travel times, which are a non-linear function of the ATs’ flows. It is important to involve traffic congestion in such a routing problem since for a growing number of ATs circulating in the city their number will lead to delays. The model is embedded within a rolling horizon framework, which divides a typical day into several horizons to deal with the real-time travel demand. In each horizon, the routing model is solved with the demand at that interval and assuring the continuity of the trips between horizons. Nevertheless, each horizon model is hard to solve given its number of constraints and decision variables. Therefore, we propose a solution approach based on a customized Lagrangian relaxation algorithm, which allows identifying a near-optimal solution for this difficult problem. Numerical experiments for the city of Delft, The Netherlands, are used to demonstrate the solution quality of the proposed algorithm as well as obtaining insights about the AT system performance. Results show that the solution algorithm can solve the proposed model for hard instances. Ride-sharing makes the AT system more capable to provide better service regarding delay time and the number of requests that can be attended by the system. The delay penalty on the profit objective function is an effective control parameter on guaranteeing the service quality while maintaining system profitability.

One-way station-based carsharing systems allow users to return a rented car to any designated station, which could be different from the origin station. Existing research has been mainly focused on the vehicle relocation problem to deal with the travel demand fluctuation over time and demand imbalance in space. However, the strategic planning of the stations’ location and their capacity for one-way carsharing systems has not been well studied yet, especially when considering vehicle relocations simultaneously. This paper presents a Mixed-integer Non-linear Programming (MINLP) model to solve the carsharing station location and capacity problem with vehicle relocations. This entails considering several important components which are for the first time integrated in the same model. Firstly, relocation operations and corresponding relocation costs are taken into consideration to address the imbalance between trip requests and vehicle availability. Secondly, the flexible travel demand at various time steps is taken as the input to the model avoiding deterministic requests. Thirdly, a logit model is constructed to represent the non-linear demand rate by using the ratio of carsharing utility and private car utility. To solve the MINLP model, a customized gradient algorithm is proposed. The application to the SIP network in Suzhou, China, demonstrates that the algorithm can solve a real world large scale problem in reasonable time. The results identify the pricing and parking space rental costs as the key factors influencing the profitability of carsharing operators. Also, the carsharing station location and fleet size impact the vehicle relocation and carsharing patronage.