4 Presentations

• 
  10:30 AM - 10:55 AM

  Optimizing Last-Mile Delivery with Load-Dependent Electric Vehicle Routing Problem under Energy Constraints

  • Mansoureh Hasannia Kolaee, presenter, Université Laval
  • Maryam Darvish, Université Laval
  • Luckny Zéphyr, Laurentian University

  The growing integration of electric vehicles (EVs) in logistics reflects a global push toward reducing fossil fuel reliance, lowering greenhouse gas emissions, and fostering sustainable transport solutions. Despite their benefits, EVs face significant operational limitations, such as restricted driving range, load-sensitive energy consumption, and limited access to charging infrastructure. To address these challenges, we propose a multi-trip Load-Dependent Electric Vehicle Routing Problem (LDEVRP) model that incorporates both electric and conventional vehicles. The model aims to minimize total energy costs by capturing the combined effects of travel distance and load weight on energy usage. We demonstrate the routing decisions for last-mile delivery with EVs, offering a practical solution for addressing the challenges posed by EVs' limited battery capacities and load-dependent energy consumption.

• 
  10:55 AM - 11:20 AM

  A Vehicle Routing Problem for Recycling and Waste Reduction

  • Thomas Chabot, presenter, Vooban
  • Simon Boivin, Vooban

  Efficient planning of industrial residual material collection routes poses a complex challenge due to heterogeneous task types and diverse operational constraints. This work presents an optimization model co-developed by Vooban and Oscal.AI to address a hybrid Vehicle Routing Problem (VRP) that combines inventory routing for recurring pickups with service-based routing for on-demand customer requests. The model incorporates real-world constraints such as vehicle capacities, multi-period planning, waste inventory accumulation, strict service requirements, time windows, multiple depots, and a sophisticated penalty system that promotes the completion of as many tasks as possible according to a dynamic, priority-based framework. Customer priorities evolve throughout the planning horizon in response to the evolution of their waste inventory levels, creating a tightly coupled decision space. The optimization framework uses the Hexaly solver to minimize operational costs while maximizing fleet efficiency, vehicle-sector affinity, and priority-weighted task coverage. Preliminary results demonstrate significant improvements in task fulfillment and route optimization, offering a robust foundation for scalable deployment in industrial waste management operations. This contribution bridges practical logistics needs with advanced combinatorial optimization.

• 
  11:20 AM - 11:45 AM

  The Plug-In Hybrid Vehicle Routing Problem with Time Windows and Partial Recharge

  • Maryam Moshabaki Esfahani, presenter, Université Laval
  • Maryam Darvish, Université Laval
  • Simona Mancini, University of Palermo

  We introduce the plug-in hybrid vehicle routing problem with time windows and partial recharge. The problem extends the vehicle routing problem with time windows by incorporating dual-fuel options and partial recharges. We consider a homogeneous fleet of plug-in hybrid vehicles that can switch between propulsion modes at any given time between two consecutive nodes. A 3-index formulation is proposed, which allows recharging stations to be visited multiple times without the need for node duplication, as required in most classical formulations for electric vehicle routing problems. We propose a preprocessing technique followed by a Local Branching algorithm to solve benchmark instances derived from the literature on the Electric Vehicle Routing Problem. Through extensive computational experiments, we demonstrate that our proposed method outperforms commercial solvers in terms of solution quality and performance.

• 
  11:45 AM - 12:10 PM

  Electric Vehicle Routing with Heterogeneous Charging Stations

  • Weiquan Wang, presenter, HEC Montreal, Department of Logistics and Operations Management
  • Yossiri Adulyasak, HEC Montreal, Department of Logistics and Operations Management
  • Cordeau Jean-François, HEC Montreal, Department of Logistics and Operations Management

  The routing of an electric vehicle often requires planning stops at charging stations to recharge the vehicle's battery. This paper introduces the Electric Vehicle Routing Problem with Heterogeneous Charging Stations (E-VRP-HC). We simultaneously consider non-linear charging functions, time-dependent waiting functions, and time-of-use electricity pricing at CSs. The objective function is to minimize the sum of the route duration costs, charging costs, and vehicle fixed costs. To solve this problem, we propose a path-based mixed-integer linear programming formulation, which does not require time discretization to track charging costs. However, even optimizing the schedule of a given route is already a challenging task. To address this, we propose two methods for the route evaluation: (i) an exact method that obtains an optimal route but is computationally intensive, and (ii) a heuristic method that can provide a high-quality solution in a very short time. Building upon these two route evaluation methods, we develop an effective metaheuristic framework to solve large-scale cases.