A Benders decomposition method for locating stations in a one-way electric car sharing system under demand uncertainty. (July 2019)

• Record Type: Journal Article
• Authors: Çalık, Hatice; Fortz, Bernard
• Is Part Of: Transportation research. Volume 125(2019)
• Page Start: 121
• Page End: 150
• Publication Date: 2019-07
• Subjects: Location -- Urban mobility -- Electric car sharing -- Benders decomposition -- Mixed integer stochastic programming -- Stochastic demand; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2019.05.004
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: A strategic decision-making problem with demand uncertainty is introduced. A mixed integer stochastic model and a Benders decomposition algorithm are developed. A stabilization procedure adapted improves the performance of the proposed algorithm. Large-scale real life instances are solved in reasonable amount of time. Abstract: 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 develop a demand forecasting method allowing to generate many demand scenarios. The method is applied to real data from Manhattan taxi trips. We are able to solve problems with 100–500 scenarios, each scenario including 1000–5000 individual customer requests, under high and low cost values and 5–15 min of accessibility restrictions, which is measured as the maximum walking time to the operating stations.