Pollution is a major environmental issue around the world. Despite the growing use and impact of commercial vehicles, recent research has been conducted with minimizing pollution as the primary objective to be reduced. The objective of this project is to implement different optimization algorithms to solve this problem. A basic model is created using the Vehicle Routing Problem (VRP) which is further extended to the Pollution Routing Problem (PRP). The basic model is updated using a Monte Carlo Algorithm (MCA). The data set contains 180 data files with a combination of 10, 15, 20, 25, 50, 75, 100, 150, and 200 groups of cities. The optimizing techniques applied are the Discrete Differential Evolution (DDE) and, Discrete Particle Swarm Optimization (DPSO) with a Python Tkinter frontend. The objectives to be optimized are the fuel consumption rate and distance traveled and a statistical comparison is done between the different algorithm to compare effectiveness.
","abstract_html":"<p>Pollution is a major environmental issue around the world. Despite the growing use and impact of commercial vehicles, recent research has been conducted with minimizing pollution as the primary objective to be reduced. The objective of this project is to implement different optimization algorithms to solve this problem. A basic model is created using the Vehicle Routing Problem (VRP) which is further extended to the Pollution Routing Problem (PRP). The basic model is updated using a Monte Carlo Algorithm (MCA). The data set contains 180 data files with a combination of 10, 15, 20, 25, 50, 75, 100, 150, and 200 groups of cities. The optimizing techniques applied are the Discrete Differential Evolution (DDE) and, Discrete Particle Swarm Optimization (DPSO) with a Python Tkinter frontend. The objectives to be optimized are the fuel consumption rate and distance traveled and a statistical comparison is done between the different algorithm to compare effectiveness.</p>","abstract_has_math":false,"creators":["Dewan, Shivika"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":null,"degree_discipline":"Computational Science","degree_department":null,"school":null,"contributors":["Donald Davendra","Razvan Andonie","Szilárd Vajda"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-01-01T08:00:00Z","date_published":"2020-01-01T08:00:00Z","updated_at":"2026-07-24T01:37:48Z","subjects":["Optimization","Pollution Routing Problem","MCA","DDE","DPSO","Tkinter Python","Environmental Health and Protection","Oil, Gas, and Energy","Other Computer Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.cwu.edu/etd/1353","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Donald Davendra","Razvan Andonie","Szilárd Vajda"]},{"key":"dc:creator","label":"Author","values":["Dewan, Shivika"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2020-06-08T07:00:00Z"]},{"key":"dc:type","label":"Dc Type","values":["Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Optimization","Pollution Routing Problem","MCA","DDE","DPSO","Tkinter Python","Environmental Health and Protection","Oil, Gas, and Energy","Other Computer Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.cwu.edu/etd/1353"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Pollution is a major environmental issue around the world. Despite the growing use and impact of commercial vehicles, recent research has been conducted with minimizing pollution as the primary objective to be reduced. The objective of this project is to implement different optimization algorithms to solve this problem. A basic model is created using the Vehicle Routing Problem (VRP) which is further extended to the Pollution Routing Problem (PRP). The basic model is updated using a Monte Carlo Algorithm (MCA). The data set contains 180 data files with a combination of 10, 15, 20, 25, 50, 75, 100, 150, and 200 groups of cities. The optimizing techniques applied are the Discrete Differential Evolution (DDE) and, Discrete Particle Swarm Optimization (DPSO) with a Python Tkinter frontend. The objectives to be optimized are the fuel consumption rate and distance traveled and a statistical comparison is done between the different algorithm to compare effectiveness.
"]},{"key":"dc:title","label":"Title","values":["Optimizing Pollution Routing Problem"]}]}],"canonical_facts":{"dc:contributor":["Donald Davendra","Razvan Andonie","Szilárd Vajda"],"dc:creator":["Dewan, Shivika"],"dc:date.available":["2020-06-08T07:00:00Z"],"dc:description.abstract":["Pollution is a major environmental issue around the world. Despite the growing use and impact of commercial vehicles, recent research has been conducted with minimizing pollution as the primary objective to be reduced. The objective of this project is to implement different optimization algorithms to solve this problem. A basic model is created using the Vehicle Routing Problem (VRP) which is further extended to the Pollution Routing Problem (PRP). The basic model is updated using a Monte Carlo Algorithm (MCA). The data set contains 180 data files with a combination of 10, 15, 20, 25, 50, 75, 100, 150, and 200 groups of cities. The optimizing techniques applied are the Discrete Differential Evolution (DDE) and, Discrete Particle Swarm Optimization (DPSO) with a Python Tkinter frontend. The objectives to be optimized are the fuel consumption rate and distance traveled and a statistical comparison is done between the different algorithm to compare effectiveness.
"],"dc:identifier":["https://digitalcommons.cwu.edu/etd/1353"],"dc:subject":["Optimization","Pollution Routing Problem","MCA","DDE","DPSO","Tkinter Python","Environmental Health and Protection","Oil, Gas, and Energy","Other Computer Sciences"],"dc:title":["Optimizing Pollution Routing Problem"],"dc:type":["Text"],"thesis:degree_discipline":["Computational Science"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T01:37:48Z"}