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Demand Forecasting and Location Optimization of Recharging Stations for Electric Vehicles in Carsharing Industries

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Demand Forecasting and Location Optimization of Recharging Stations for Electric Vehicles in Carsharing Industries

Moein Namin, Elnaz (2015) Demand Forecasting and Location Optimization of Recharging Stations for Electric Vehicles in Carsharing Industries. Masters thesis, Concordia University.

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Abstract

Carsharing is an alternative to private car usage. Using electric-vehicles as a substitute to fuel vehicles is a wiser option which leads to lower fuel emissions, more energy savings and decreased oil dependency. However, there are some barriers in using electric vehicles at large scale in carsharing companies. Battery power limitation and lack of sufficient infrastructures are some of them. Accurate demand forecasting is a must for this purpose.
In the first part of this thesis, we investigate the demand forecasting problem for carsharing industries and apply four techniques namely simple linear regression, seasonally adjusted forecast, Winter's Model and artificial neural networks to decide the right number of vehicles to be made available at each station to meet the customer requests. The results on randomly generated test datasets show that artificial neural networks perform better over the other three.
In the second part, we investigate the location planning problem of recharging stations for electric vehicles. The base model used for this study is the mathematical optimization model proposed by Wang & Lin (2013). Firstly, we improve their MIP model and solve it using AIMMS (Advanced Interactive Multidimensional Modeling System). Secondly, we propose Genetic Algorithm for the same problem and implement it in Matlab. The obtained results are compared with previous work done by Wang and Lin (2013). The comparisons show better performance of the proposed methods.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Moein Namin, Elnaz
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Quality Systems Engineering
Date:March 2015
Thesis Supervisor(s):Awasthi, Anjali
ID Code:979808
Deposited By: ELNAZ MOEIN NAMIN
Deposited On:13 Jul 2015 14:04
Last Modified:22 Jul 2019 18:01
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