Integrating operational decisions into the planning of one-way vehicle-sharing systems under uncertainty

Abstract

This study proposes a systematic approach for planning and operating a one-way vehicle-sharing system (VSS) under demand uncertainty. It investigates the distribution of parking spaces and vehicles considering stochastic demand and interactions with the major operational decisions, namely vehicle redistribution activities. An optimization model is formulated that aims to determine the best deployment strategy for minimizing overall cost while achieving a certain level of service (LoS). Then, a simulation-based solution approach based on a discrete-event simulator (DES), Particle Swarm Optimization (PSO), and Optimal Computing Budget Allocation (OCBA) is devised to solve the mathematical model. The methodology is then applied to a car-sharing system in Singapore. Results demonstrate that considering rebalancing activities is imperative in making deployment strategies. The case study also provides managerial insights regarding designing and operating one-way VSS.

An Evaluation Framework for the Planning of Electric Car-Sharing Systems: A Combination Model of AHP-CBA-VD

“…Compared to public transit, which is not adequately provided in some Chinese cities, it is very convenient for these students to rent out or return the shared cars since they live on the campus. According to previous studies [68], there exist positive relationships between the capacity of a station (or called service unit, in terms of parking areas and charging facilities) [16] and shared car usage. To satisfy the CS demands, the capacity in each station belonging to set B should be more than the average capacity of the stations belonging to set C. This principle is combined with the factor of 'number ratio of stations to EVs' for evaluation.…”

“…For example, Cepolina et al [47] developed an object-oriented simulator to assess the different impacts of three relocation procedure systems on the level of service and efficiency of urban CS systems. Deng and Cardin [16] developed a simulation-based solution approach to explore how to minimize the overall daily and maintain a certain service level through vehicle redistribution. Nourinejad et al [46] not only considered the optimization of car relocation, but also explored the optimization of staff relocation.…”

“…The construction of stations in this study refers to the station network layout, which involves many interrelating decisions such as the location, capacity and number of stations, and fleet size [57]. This is an indispensable factor for comprehensively evaluating the planning of an ECSS [16] because it greatly influences both the attractiveness level of the ECSS for consumers and the costs for operators. A relatively dense network distribution and more available EVs can provide better service for consumers, whereas these would result in more construction and operation costs for operators.…”

“…For ECS operators, planning a good station-based ECSS with a one-way trip is critical for successful business, however, this is not easy because it is very difficult to achieve a perfect balance between revenue and cost [16]. A flexible ECSS with more stations, more parking areas, and charging facilities are more attractive to ECS users and can in turn create more revenue for its operators.…”

Optimal Model for Carsharing Station Location Based on Multi-Factor Constraints

“…First, the total demand for electric carsharing in the region can be obtained through the macro demand forecast. Then, according to the population density, land use, and other parameters of each small grid, the total demand for regional electric carsharing is allocated to each small area, and the demand matrix of the entire area can be obtained [24]. In accordance with Equation (1), the total demand of electric carsharing in the area is obtained on the basis of the urban resident population in consideration of the average daily trip volume, travel mode rate, and electric carsharing travel demand.…”