{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1617"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1617","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Waste collection & street-sweeping route optimization using a 2-stage cluster algorithm & heuristic approaches","abstract":"Waste collection and street-sweeping play a vital role in public health, safety, and overall cleanliness. Since these processes cannot be ignored, they should be done in an efficient manner. The following thesis proposes a novel 2-stage clustering approach, namely the Static and Dynamic Clustering, to divide a municipalities road network into several operational areas in which the routes can be assigned. A method of generating optimal routes within the respective operational areas is also developed so statistics can be used to quantify the improvements made using the proposed clustering methods. The proposed algorithms were used to optimize the waste collection and street-sweeping processes in The City of Oshawa. The results of this work show that the proposed clustering algorithms can generate operational areas that better distribute the workload and overall simulated statistics when compared to existing configurations. Additionally, the proposed techniques may be applied to other routing applications, and other areas of research involving optimizing data partitions using clustering methods, such as machine learning.","abstract_html":"Waste collection and street-sweeping play a vital role in public health, safety, and overall cleanliness. Since these processes cannot be ignored, they should be done in an efficient manner. The following thesis proposes a novel 2-stage clustering approach, namely the Static and Dynamic Clustering, to divide a municipalities road network into several operational areas in which the routes can be assigned. A method of generating optimal routes within the respective operational areas is also developed so statistics can be used to quantify the improvements made using the proposed clustering methods. The proposed algorithms were used to optimize the waste collection and street-sweeping processes in The City of Oshawa. The results of this work show that the proposed clustering algorithms can generate operational areas that better distribute the workload and overall simulated statistics when compared to existing configurations. Additionally, the proposed techniques may be applied to other routing applications, and other areas of research involving optimizing data partitions using clustering methods, such as machine learning.","abstract_has_math":false,"creators":["Parsons, Tyler"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Seo, Jaho"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-04-01","date_published":"2023-04-01","updated_at":"2026-07-24T05:35:28Z","subjects":["Waste collection","Street-sweeping","Route optimization","GIS","Clustering"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1617","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Seo, Jaho"]},{"key":"dc:creator","label":"Author","values":["Parsons, Tyler"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-04-25T19:11:22Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-04-25T19:11:22Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-04-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Waste collection","Street-sweeping","Route optimization","GIS","Clustering"]}]},{"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/10155/1617"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Waste collection and street-sweeping play a vital role in public health, safety, and overall cleanliness. Since these processes cannot be ignored, they should be done in an efficient manner. The following thesis proposes a novel 2-stage clustering approach, namely the Static and Dynamic Clustering, to divide a municipalities road network into several operational areas in which the routes can be assigned. A method of generating optimal routes within the respective operational areas is also developed so statistics can be used to quantify the improvements made using the proposed clustering methods. The proposed algorithms were used to optimize the waste collection and street-sweeping processes in The City of Oshawa. The results of this work show that the proposed clustering algorithms can generate operational areas that better distribute the workload and overall simulated statistics when compared to existing configurations. Additionally, the proposed techniques may be applied to other routing applications, and other areas of research involving optimizing data partitions using clustering methods, such as machine learning."]},{"key":"dc:title","label":"Title","values":["Waste collection & street-sweeping route optimization using a 2-stage cluster algorithm & heuristic approaches"]}]}],"canonical_facts":{"dc:contributor.advisor":["Seo, Jaho"],"dc:creator":["Parsons, Tyler"],"dc:date.accessioned":["2023-04-25T19:11:22Z"],"dc:date.available":["2023-04-25T19:11:22Z"],"dc:date.issued":["2023-04-01"],"dc:description.abstract":["Waste collection and street-sweeping play a vital role in public health, safety, and overall cleanliness. Since these processes cannot be ignored, they should be done in an efficient manner. The following thesis proposes a novel 2-stage clustering approach, namely the Static and Dynamic Clustering, to divide a municipalities road network into several operational areas in which the routes can be assigned. A method of generating optimal routes within the respective operational areas is also developed so statistics can be used to quantify the improvements made using the proposed clustering methods. The proposed algorithms were used to optimize the waste collection and street-sweeping processes in The City of Oshawa. The results of this work show that the proposed clustering algorithms can generate operational areas that better distribute the workload and overall simulated statistics when compared to existing configurations. 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