A Data-Driven Approach for Vehicle Relocation in Car-Sharing Services with Balanced Supply-Demand Ratios

Li, Xiaoming; Gao, Jie; Wang, Chun; Huang, Xiao

doi:10.1007/s13177-021-00269-y

Cited by 3 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Li et al. (2022b) design a data‐driven optimization‐based approach for both determining the initial number of cars at each station and relocating the vehicles, considering stochastic demands. The proposed approach combines the non‐parametric learning algorithm and the two‐stage stochastic programming, and it is tested on instances taken from the New York taxicab trip record data set.…”

Section: State‐of‐the‐art Of Classic Optimization Problems In Csssmentioning

confidence: 99%

“…Li et al. (2022b) design a data‐driven optimization‐based approach for VR, considering stochastic demands, as detailed in Section 2.2.2. For the same problem but with EVs, Lai et al.…”

Section: New Trends In the Optimization Of Car‐sharing Systemsmentioning

confidence: 99%

“…In order to efficiently address large-sized instances, an ALNS is also designed and tested on a real scenario taken from Oslo (Norway). Li et al (2022b) design a data-driven optimization-based approach for VR, considering stochastic demands, as detailed in Section 2.2.2. For the same problem but with EVs, Lai et al (2022) develop a rolling-horizon decision framework, considering also the users' spatial flexibility as explained more in detail in Section 2.2.2.…”

Section: Time-dependent Car-sharingmentioning

confidence: 99%

“…Determining the sequence of pickups and deliveries, for each operator, respecting several constraints (e.g., the time window within each car has to be delivered at the station where it is required) is another challenge to address as well as determining the appropriate number of vehicles initially available at each station. For example, Li et al (2022b) design a data-driven optimizationbased approach for both determining the initial number of cars at each station and relocating the vehicles, considering stochastic demands. The proposed approach combines the non-parametric learning algorithm and the two-stage stochastic programming, and it is tested on instances taken from the New York taxicab trip record data set.…”

Section: Operator-based Vehicle Relocation In One-way Car-sharing Sys...mentioning

confidence: 99%

See 3 more Smart Citations

A survey on emergent trends in the optimization of car‐sharing systems

Bruglieri

Pisacane

2023

Int Trans Operational Res

View full text Add to dashboard Cite

This paper reviews the most recent literature on the optimization of car‐sharing systems. Unlike other surveys, we do not focus on a single aspect of car‐sharing systems, but we consider a wide range of optimization problems with a global view. Our aim is threefold. First, we revise the classic decision problems arising in car‐sharing systems with conventional and electric vehicles, such as the fleet size, the station location, and the vehicle relocation. Then, we discuss some of the most recent decision problems arising in more innovative car‐sharing systems, like those with autonomous vehicles, with mixed fleets, and the multi‐modal and the time‐dependent ones. Finally, we focus on new perspectives arising in the optimization of some innovative car‐sharing services that are not yet (or little) investigated in the literature and that are worthy of attention from the operations research point of view. For each of them, we also highlight new and challenging open decision problems that deserve academic attention.

show abstract

Section: State‐of‐the‐art Of Classic Optimization Problems In Csssmentioning

confidence: 99%

“…Li et al. (2022b) design a data‐driven optimization‐based approach for VR, considering stochastic demands, as detailed in Section 2.2.2. For the same problem but with EVs, Lai et al.…”

Section: New Trends In the Optimization Of Car‐sharing Systemsmentioning

confidence: 99%

Section: Time-dependent Car-sharingmentioning

confidence: 99%

Section: Operator-based Vehicle Relocation In One-way Car-sharing Sys...mentioning

confidence: 99%

See 2 more Smart Citations

A survey on emergent trends in the optimization of car‐sharing systems

Bruglieri

Pisacane

2023

Int Trans Operational Res

View full text Add to dashboard Cite

show abstract

“…Although the market for car-sharing services is rapidly expanding yearly, there are still numerous difficulties to be addressed. For instance, due to the unbalanced demand patterns of users, on-demand car-sharing service often suffers demand-supply imbalance of vehicle allocation [4,5]. Additionally, in urban areas, the available space for car stations is strictly limited [6].…”

Section: Introductionmentioning

confidence: 99%

Communication‐aware distributed rebalancing for cooperative car‐sharing service

Hayashi

Sakurama

2022

IET Control Theory & Appl

View full text Add to dashboard Cite

This study presents a cooperative car‐sharing system in which various service providers jointly operate a sharing service. A distributed algorithm is proposed for rebalancing control to reduce deadhead vehicles as much as possible under the constraints of dispatchable vehicles and parking slots at car stations. Each provider cooperatively searches for the optimal solution by exchanging the estimated values of the optimal dual solutions over the communication networks between providers. Event‐driven communication is considered in which information exchange with the neighboring providers is performed only when the amount of change of the estimated optimal dual solution exceeds a threshold. Numerical instances explain that the estimations of all service providers meet the optimal rebalancing solutions.

show abstract