{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1966"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1966","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Bi objective multi depot location routing problem with time window for EVs","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Alimoradi, Amin"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Modelling and Computational Science","degree_department":null,"school":null,"contributors":[],"advisors":["Azad, Nader"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-06-01","date_published":"2025-06-01","updated_at":"2026-07-24T05:35:30Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1966","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Azad, Nader"]},{"key":"dc:creator","label":"Author","values":["Alimoradi, Amin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-07-22T14:34:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-22T14:34:00Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-06-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Modelling and Computational Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1966"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Bi objective multi depot location routing problem with time window for EVs"]}]}],"canonical_facts":{"dc:contributor.advisor":["Azad, Nader"],"dc:creator":["Alimoradi, Amin"],"dc:date.accessioned":["2025-07-22T14:34:00Z"],"dc:date.available":["2025-07-22T14:34:00Z"],"dc:date.issued":["2025-06-01"],"dc:description.abstract":["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."],"dc:identifier.uri":["https://hdl.handle.net/10155/1966"],"dc:language.iso":["en"],"dc:title":["Bi objective multi depot location routing problem with time window for EVs"],"dc:type":["Thesis"],"thesis:degree_discipline":["Modelling and Computational Science"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:30Z"}