A Benders decomposition method for locating ...
Type de document :
Pré-publication ou Document de travail
Titre :
A Benders decomposition method for locating stations in a one-way electric car sharing system under demand uncertainty
Auteur(s) :
Calik, Hatice [Auteur]
Graphes et Optimisation Mathématique [Bruxelles] [GOM]
Integrated Optimization with Complex Structure [INOCS]
Fortz, Bernard [Auteur]
Graphes et Optimisation Mathématique [Bruxelles] [GOM]
Integrated Optimization with Complex Structure [INOCS]
Graphes et Optimisation Mathématique [Bruxelles] [GOM]
Integrated Optimization with Complex Structure [INOCS]
Fortz, Bernard [Auteur]
Graphes et Optimisation Mathématique [Bruxelles] [GOM]
Integrated Optimization with Complex Structure [INOCS]
Mot(s)-clé(s) en anglais :
Location
Urban Mobility
Electric Car Sharing
Benders Decomposition
Mixed Integer Stochastic Programming
Stochastic Demand
Urban Mobility
Electric Car Sharing
Benders Decomposition
Mixed Integer Stochastic Programming
Stochastic Demand
Discipline(s) HAL :
Computer Science [cs]/Operations Research [math.OC]
Résumé en anglais : [en]
We focus on a problem of locating recharging stations in one-way station based electric car sharing systems which operate under demand uncertainty. We model this problem as a mixed integer stochastic program and develop a ...
Lire la suite >We focus on a problem of locating recharging stations in one-way station based electric car sharing systems which operate under demand uncertainty. We model this problem as a mixed integer stochastic program and develop a Benders decomposition algorithm based on this formulation. We integrate a stabilization procedure to our algorithm and conduct a large-scale experimental study on our methods. To conduct the computational experiments, we developed a demand forecasting method allowing to generate many demand scenarios. The method was applied to real data from Manhattan taxi trips. We are able to solve problems with 100 to 500 scenarios, each scenario including 1000 to 5000 individual customer requests, under high and low cost values and 5 to 15 mins of accessibility restrictions, which is measured as the maximum walking time to the operating stations.Lire moins >
Lire la suite >We focus on a problem of locating recharging stations in one-way station based electric car sharing systems which operate under demand uncertainty. We model this problem as a mixed integer stochastic program and develop a Benders decomposition algorithm based on this formulation. We integrate a stabilization procedure to our algorithm and conduct a large-scale experimental study on our methods. To conduct the computational experiments, we developed a demand forecasting method allowing to generate many demand scenarios. The method was applied to real data from Manhattan taxi trips. We are able to solve problems with 100 to 500 scenarios, each scenario including 1000 to 5000 individual customer requests, under high and low cost values and 5 to 15 mins of accessibility restrictions, which is measured as the maximum walking time to the operating stations.Lire moins >
Langue :
Anglais
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