| Author: | Wu, Ting |
| Title: | Modeling and optimization for electric carsharing services |
| Advisors: | Xu, Min (ISE) |
| Degree: | Ph.D. |
| Year: | 2022 |
| Subject: | Car sharing Electric automobiles Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Industrial and Systems Engineering |
| Pages: | xv, 173 pages : color illustrations |
| Language: | English |
| Abstract: | The era of shared mobility has prompted the emergence of many alternative transportation modes. A prominent one of them is carsharing, which allows users to access private cars without paying ownership costs. Driven by regulations and incentive programs exerted by governments for vehicle electrification, carsharing is undergoing electrification. However, vehicle electrification in carsharing inevitably poses new challenges to decision-makings faced by carsharing operators. These challenges generally come from the limited driving range, frequent charging needs, long charging times, and nonlinear charging profile of EVs. Efforts are highly anticipated to overcome these challenges such that carsharing services (CSSs) can be operated smoothly. In this thesis, one tactical-level and two operational-level decision-making problems are addressed for electric CSSs: fleet size problem, real-time vehicle relocation and charging strategy (RT-VR&CS) problem, and real-time vehicle relocation and staff rebalancing (RT-VR&SR) problem. The objectives of the three problems are to maximize the profit for carsharing operators. By solving the three problems, this study helps carsharing operators to overcome the decision-making challenges caused by vehicle electrification. The tactical fleet size problem aims to determine the number of electric vehicles (EVs) put into use for CSSs while considering battery degradation, on-demand charging strategy, and operational vehicle relocation as well as trip assignment. Due to the incorporation of battery wear cost, a mixed-integer nonlinear programming (MINLP) model with both concave and convex terms in the objective function is developed. A piecewise linear approximation approach and an outer-approximation method are employed to linearize the model. The resultant mixed-integer linear programming (MILP) model can be solved by state-of-the-art solvers like Gurobi to obtain an ε-optimal solution. The operational RT-VR&CS problem seeks to develop a fast yet robust algorithm to determine the real-time vehicle relocation and charging strategies. A dynamic algorithmic framework based on a rolling time horizon is established, through which the complicated RT-VR& CS problem is transformed into solving a series of static vehicle relocation and charging strategy (S-VR&CS) problems. A set-packing-type formulation and a column-generation-based solution method are adopted to solve each static problem. Based on the investigated RT-VR&CS problem, the operational RT-VR&SR problem makes an extension by including staff rebalancing. A Markov Decision Process (MDP) is formulated and an efficient concurrent-scheduler-based policy is proposed. The models and solution methods proposed for the three problems are all tested in a real-world case study. Their applicability is validated. The managerial insights are also explored. |
| Rights: | All rights reserved |
| Access: | open access |
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