-

 

 

 




Optimization Online





 

The Urban Recharging Infrastructure Design Problem with Stochastic Demands and Capacitated Recharging Stations

Baris Yildiz(byildiz***at***ku.edu.tr)
Evren Olcaytu(eolcaytu***at***etu.edu.tr)

Abstract: In this study, we introduce the urban recharging infrastructure design problem with stochastic demands and capacitated recharging stations. Stochastic recharge demands shaped by daily transportation/logistics activity cycles, capacity limitations of recharging stations and users' route preferences need to be considered simultaneously to address this urgent problem that focus on the inner city transportation and logistics use of electric vehicles. We present scenario based stochastic programming formulations to find infrastructure designs that minimize the total investment cost while guaranteeing a target quality of service level that is crucial to facilitate widespread use of electric vehicles in logistics activities. We propose a branch and cut algorithm that can efficiently solve realistic problem instances with over 390,000 nodes, 1,200,000 edges, 76,000 recharging demand configurations, 200 candidate recharging station locations and 1,000 scenarios. Our computational experiments on different network topologies and recharging demand structures attest to the efficiency of our solution approach and present significant managerial insights for the decision makers in both business and government. In particular our results show that high quality of service levels can be achieved with a minor increase in the total investment cost and the multi period investment strategies can be viable alternatives to deal with the uncertainty related to the future demand levels.

Keywords: Recharging Station Location, Urban Logistics, Green Transportation, Infrastructure Investment, Integer Programming, Branch and Cut

Category 1: Applications -- OR and Management Sciences (Transportation )

Category 2: Combinatorial Optimization (Branch and Cut Algorithms )

Category 3: Stochastic Programming

Citation: Koc University, May 2017

Download: [PDF]

Entry Submitted: 05/16/2017
Entry Accepted: 05/16/2017
Entry Last Modified: 05/16/2017

Modify/Update this entry


  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository

 

Submit
Update
Policies
Coordinator's Board
Classification Scheme
Credits
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society