Rebalancing in Vehicle-sharing Systems with Service Availability Guarantees

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00347445" target="_blank" >RIV/68407700:21230/20:00347445 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.23919/ACC45564.2020.9147303" target="_blank" >https://doi.org/10.23919/ACC45564.2020.9147303</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/ACC45564.2020.9147303" target="_blank" >10.23919/ACC45564.2020.9147303</a>

Result language

angličtina

Original language name

Rebalancing in Vehicle-sharing Systems with Service Availability Guarantees

Original language description

A station-based vehicle sharing system consists of a fleet of vehicles (usually bikes or cars) that can be rented at one station and returned at another station. We study how to achieve guaranteed service availability in such systems. Specifically, we are interested in determining a) the fleet size and b) a vehicle rebalancing policy that guarantees that a) every customer will find an available vehicle at the origin station and b) the customer will find a free parking spot at the destination station. We model the evolution of the number of vehicles at each station as a stochastic process. The proposed rebalancing strategy iteratively solves a chance-constrained optimization problem to find a rebalancing schedule that ensures that no service failures will occur in the future with a given level of confidence. We show that such a chance-constrained optimization problem can be converted into a linear program and efficiently solved. As a case study, we apply the proposed method to control a simulated bike-sharing system in Boston using real-world historical demand. Our results demonstrate that our method can indeed ensure the desired level of service availability even when the demand does not fully conform to the assumptions of the underlying stochastic model. Moreover, compared with a state-of-the art rebalancing method, the proposed method can achieve nearly full service availability while making less than half of the rebalancing trips.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of 2020 American Control Conference

ISBN

978-1-5386-8266-1

ISSN

0743-1619

e-ISSN

2378-5861

Number of pages

8

Pages from-to

2635-2642

Publisher name

IEEE

Place of publication

Anchorage, Alaska

Event location

Denver

Event date

Jul 1, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000618079802097