An incentive algorithm for a closed stochastic network: data and mean-field analysis - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Preprints, Working Papers, ... Year : 2023

An incentive algorithm for a closed stochastic network: data and mean-field analysis

Abstract

The paper deals with a load-balancing algorithm for a closed stochastic network with two zones with different demands. The algorithm is moti- vated by an incentive algorithm for redistribution of cars in a large-scale car-sharing system. The service area is divided into two zones. When cars stay too long in the low-demand zone, users are encouraged to pick them up and return them in the high-demand zone. The zones are divided in cells called stations. The cars are the network customers. The mean-field limit solution of an ordinary differential equation (ODE) gives the large scale distribution of the station state in both clusters for this incentive policy in a discrete Markovian framework. An equilibrium point of this ODE is characterized via the invariant measure of a random walk in the quarter-plane. The proportion of empty and saturated stations measures how the system is balanced. Numerical experiments illustrate the impact of the incentive policy. Our study shows that the incentive policy helps when the high-demand zone observes a lack of cars but a saturation must be prevented especially when the high-demand zone is small.
Fichier principal
Vignette du fichier
GiftsLaMatematica-RevisionSoumise_depotHAL.pdf (1.03 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04437641 , version 1 (04-02-2024)

Licence

Identifiers

  • HAL Id : hal-04437641 , version 1

Cite

Bianca Marin Moreno, Christine Fricker, Hanene Mohamed, Amaury Philippe, Martin Trépanier. An incentive algorithm for a closed stochastic network: data and mean-field analysis. 2023. ⟨hal-04437641⟩
20 View
12 Download

Share

Gmail Mastodon Facebook X LinkedIn More