A Stochastic Maximal Covering Formulation for a Bike Sharing System

Claudio Ciancio*, Giuseppina Ambrogio, Demetrio Laganá

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


This paper discusses a maximal covering approach for bike sharing systems under deterministic and stochastic demand. Bike sharing is constantly becoming a more popular and sustainable alternative transportation system. One of the most important elements for the design of a successful bike sharing system is given by the location of stations and bikes. The demands in each zone for each period is however uncertain and can only be estimated. Therefore, it is necessary to address this problem by taking into account the stochastic features of the problem. The proposed model determines the optimal location of bike stations, and the number of bikes located initially in each station, considering an initial investment lower than a given predetermined budget. The objective of the model is to maximize the percentage of covered demand. Moreover, during the time horizon, it is possible to relocate a certain amount of bikes in different stations with a cost proportional to the traveled distance. Both deterministic and stochastic models are formulated as mixed integer linear programs.

Original languageEnglish
Title of host publicationOptimization and Decision Science
Subtitle of host publicationMethodologies and Applications, ODS
EditorsAntonio Sforza, Claudio Sterle
PublisherSpringer New York LLC
Number of pages9
ISBN (Print)9783319673073
Publication statusPublished - 1 Jan 2017
Externally publishedYes
EventInternational Conference on Optimization and Decision Science, ODS 2017 - Sorrento, Italy
Duration: 4 Sep 20177 Sep 2017

Publication series

NameSpringer Proceedings in Mathematics and Statistics
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017


ConferenceInternational Conference on Optimization and Decision Science, ODS 2017


  • Bike sharing system
  • Stochastic model


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