4 Presentations

• 
  03:30 PM - 03:55 PM

  Strengths of the Formulations of the Fuel Refuelling Location Problem

  • Nagisa Sugishita, presenter, Université de Montréal
  • Margarida Carvalho, Université de Montréal

  The Fuel Refueling Location Problem (FRLP) consists of finding the optimal siting of charging stations for electric vehicles (EVs), specifically considering the limited travel range of EVs. This problem becomes particularly relevant when the goal is to facilitate long-distance travel using EVs. A variant of the FRLP, known as the Deviation Fuel Refueling Location Problem (DFRLP), accounts for the possibility that drivers may deviate from their preferred routes to ensure sufficient charging for completing their trips. In this presentation, we study formulations of the FRLP and DFRLP used in the literature, examining their strengths. Furthermore, we explore approaches to tighten the formulations.

• 
  03:55 PM - 04:20 PM

  Optimal Deployment of Electric Vehicle Charging Infrastructure in Commercial Buildings

  • Kimia Khalili, presenter, Master of Applied Science Student at Concordia University
  • Fuzhan Nasiri, Concordia University, Department of Building, Civil and Environmental Engineering Montreal, Quebec, Canada
  • Po-Han Chen, Concordia University, Department of Building, Civil and Environmental Engineering Montreal, Quebec, Canada

  As electric vehicles (EVs) gain momentum worldwide, their adoption is becoming increasingly widespread. Canada is committed to decarbonizing the country's transportation sector and becoming a global leader in zero-emission vehicles. In the last few years, we have seen a steady growth of Canada’s EV market, most of these new EVs were registered in Canada’s three most populous provinces: Quebec (43%), British Columbia (28%), and Ontario (23%). However, a significant hurdle persists in the form of inadequate charging infrastructure, leading to challenges in finding suitable locations for charging stations in public places. This research initiative aims to tackle this challenge by optimally situating charging stations within commercial buildings in Quebec. By considering the perspectives of building owners and stakeholders, this initiative aims to thoroughly understand the requirements for EV charging infrastructure. Through the development of a model, this study endeavors to minimize the overall life cycle costs. In doing so, the proposed optimization-model incorporates factors such as equipment, installation, operation, maintenance cycle, and maintenance costs. Indeed, this study marks a significant advancement in surmounting the challenges presented by inadequate charging infrastructure. This approach ensures cost-effectiveness while promoting sustainable transportation. Ultimately, this research aims to facilitate a seamless transition to an electrified future.

• 
  04:20 PM - 04:45 PM

  Intermodal hub network design with commodity-dependent hub capacities

  • MARIO JOSÉ Basallo Triana, presenter, HEC Montreal - CIRRELT
  • Jean-François Cordeau, HEC Montréal, GERAD, CIRRELT
  • Navneet Vidyarthi, John Molson School of Business, Concordia University

  We study the intermodal hub network design problem with service time requirements for the service of commodities or origin-destination demands. The service time includes the total transport time along with the waiting and service time at hubs. The total service time is dependent on the total flow that is being processed at the hub due to congestion and delays in the hub service system. In this sense, to satisfy the service time requirement for a commodity in particular, the processing capacity of the hub might differ depending on the type of commodity being processed. We analyze different formulations for the hub network design problem with commodity-dependent hub capacities. We also discuss partial results regarding implementing a Benders’ decomposition algorithm to solve large-scale instances of the problem.

• 
  04:45 PM - 05:10 PM

  Intracity Charging Station Selection for New Electric Vehicle Owners

  • Steven Lamontagne, presenter, Université de Montréal
  • Emma Frejinger, DIRO and CIRRELT
  • Margarida Carvalho, Université de Montréal
  • Ribal Atallah, Hydro-Québec

  Within dense cities, the prediction of the electric vehicle charging station selection by drivers can be difficult for charging network operators. The ability to make such predictions is invaluable as it allows, for example, the integration of the demand models within a charging network design context. This motivated us to estimate discrete choice models to forecast the usage of public charging stations, applicable to unobserved individuals. The parameter values are estimated using a unique dataset of real charging sessions within the city of Montreal, Quebec. We present and discuss the predictive power of these models, considering various types of charging infrastructure and types of discrete choice models. Notably, the high number of charging options, low travel distances, and multiple possible destinations lead to many predictive challenges, particularly when predicting the selection by previously unobserved individuals.