Optimizing the Profitability and Quality of Service in Carshare Systems Under Demand Uncertainty

Abstract

Carsharing has been considered as an effective means to increase mobility and reduce personal vehicle usage and related carbon emissions. In this paper, we consider problems of allocating a carshare fleet to service zones under uncertain one-way and round-trip rental demand. We employ a two-stage stochastic integer programming model, in the first stage of which we allocate shared vehicle fleet and purchase parking lots or permits in reservation-based or free-floating systems. In the second stage, we generate a finite set of samples to represent demand uncertainty and construct a spatial–temporal network for each sample to model vehicle movement and the corresponding rental revenue, operating cost, and penalties from unserved demand. We minimize the expected total costs minus profit and develop branch-and-cut algorithms with mixed-integer, rounding-enhanced Benders cuts, which can significantly improve computation efficiency when implemented in parallel computing. We apply our model to a data set of Zipcar in the Boston–Cambridge, Massachusetts, area to demonstrate the efficacy of our approaches and draw insights on carshare management. Our results show that exogenously given one-way demand can increase carshare profitability under given one-way and round-trip price differences and vehicle relocation cost whereas endogenously generated one-way demand as a result of pricing and strategic customer behavior may decrease carshare profitability. Our model can also be applied in a rolling-horizon framework to deliver optimized vehicle relocation decisions and achieve significant improvement over an intuitive fleet-rebalancing policy. The online appendix is available at https://doi.org/10.1287/msom.2017.0644 .

Operations Management in the Age of the Sharing Economy: What Is Old and What Is New?

“…A review of this literature can be found in Freund et al (2019) and He et al (2019b). Recent papers include Lu et al (2017) and Kabra et al (2018).…”

Optimizing the Strategic Decisions for One-Way Station-Based Carsharing Systems: A Mean-CVaR Approach

“…Due to the complexity of the problem, they proposed a heuristic algorithm to obtain an approximate solution or to provide an initial heuristic solution. Lu et al [23] introduced a stochastic model to optimize the profitability and quality of service (QoS) considering uncertain one-way and twoway rental demand for a hybrid reservation-based and freefloating system. A desired QoS level was maintained by minimizing the expected penalty of unserved customers in a risk-neutral model.…”

“…Some studies like [4], [6], [7] merely took part of them into account and ingored the potential risk. Although Lu et al [23] briefly introduced a model that applied CVaR to reduce the risk of unserved demand, rare carsharing studies have considered the risk to avoid heavy losses. Thus, to fill in the research gaps, this paper considers a new riskaverse stochastic model to solve the joint design problem.…”

“…[23] solved a similar problem by an algorithm based on Benders decomposition. Our study, however, develops two other algorithms to handle the proposed model and compares the computation efficiency with the algorithm illustrated by Lu et al [23].…”

Exploratory data science for discovery and ex‐ante assessment of operational policies: Insights from vehicle sharing

“…Weikl and Bogenberger (2015) translate this concept into a relocation model for free‐floating carsharing with electric vehicles that combines relocations with service trips to reduce overall costs. Lu, Chen, and Shen (2018) focus on the inherent uncertainty associated with carsharing operations and introduce a zone‐based model that can be applied to both station‐based and free‐floating systems.…”