Bi objective multi depot location routing problem with time window for EVs

This thesis addresses a multi-depot, bi-objective Location Routing Problem for electric vehicles (EVs), modeled as a Mixed Integer Linear Programming (MILP) problem. The mathematical model integrates cost minimization with workload fairness under time windows. A custom metaheuristic algorithm, combining a hybrid Genetic Algorithm (GA) and Ant Colony Optimization (ACO), was developed to optimize depot locations and EV routes, minimizing operational costs and balancing driver workloads. The approach accounts for depot and vehicle capacities, vehicle mileage limits, and EV charging constraints. Computational experiments reveal that the model’s efficiency outperforms traditional solvers for large-scale problems, maintaining solution quality within a 5% gap. A Pareto analysis provides insight into cost vs. distance trade-offs, and sensitivity analysis examines the impacts of vehicle capacity and route distance constraints. This work contributes to sustainable logistics by offering a practical, scalable solution for EV routing, enhancing real-world deployment feasibility in urban settings.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Modelling and Computational Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Alimoradi, Amin

Advisor dc:contributor.advisor

• Azad, Nader

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1966

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1966