{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/88233"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/88233","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A large-scale neighborhood search approach to vehicle routing pick-up and delivery problem with time windows under uncertainty","abstract":"The vehicle routing problem with shipment pick-up and delivery with time windows (VRPPDTW) is one of the core problems that is addressed by a package delivery company in its operations. Most often, this problem has been addressed from the point of view of cost-cutting, to achieve the lowest cost possible under a given/predicted demand and service time scenario. This thesis aims to study a real-world VRPPDTW problem with side-constraints and build solutions that are cost-effective as well as robust to stochasticity in demands and service times. Even without the additional side constraints, the VRPPDTW is NP-hard. In particular, we consider the solution of VRPPDTW with side-constraints adopted by a carrier. Because of the nature as well as the size of the problem and the network, we demonstrate that the problem is combinatorially explosive. We therefore develop a large-scale neighbourhood search heuristic combined with a break-and-join heuristic and a clustering heuristic. We use this heuristic to build a set of schedules with far lower operating costs than the existing solution and effectively decrease the costs by 15% by reducing the number of routes needed to serve the shipments. We then build a framework to evaluate the performance of the solutions under stochasticity, and present results related to under stochasticity in service times.","abstract_html":"The vehicle routing problem with shipment pick-up and delivery with time windows (VRPPDTW) is one of the core problems that is addressed by a package delivery company in its operations. Most often, this problem has been addressed from the point of view of cost-cutting, to achieve the lowest cost possible under a given/predicted demand and service time scenario. This thesis aims to study a real-world VRPPDTW problem with side-constraints and build solutions that are cost-effective as well as robust to stochasticity in demands and service times. Even without the additional side constraints, the VRPPDTW is NP-hard. In particular, we consider the solution of VRPPDTW with side-constraints adopted by a carrier. Because of the nature as well as the size of the problem and the network, we demonstrate that the problem is combinatorially explosive. We therefore develop a large-scale neighbourhood search heuristic combined with a break-and-join heuristic and a clustering heuristic. We use this heuristic to build a set of schedules with far lower operating costs than the existing solution and effectively decrease the costs by 15% by reducing the number of routes needed to serve the shipments. We then build a framework to evaluate the performance of the solutions under stochasticity, and present results related to under stochasticity in service times.","abstract_has_math":false,"creators":["Tumuluri, Praveen"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Lavanya, Marla"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-29T20:50:26Z","date_published":"2015-09-29T20:50:26Z","updated_at":"2026-07-22T22:26:31Z","subjects":["vehicle routing","large-scale neighbourhood search"],"languages":["en"],"rights":["Copyright 2015 Praveen Tumuluri"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/88233","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Lavanya, Marla"]},{"key":"dc:creator","label":"Author","values":["Tumuluri, Praveen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-29T20:50:26Z","2017-09-30T09:15:21Z","2015-08","2015-07-24","2015-8"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["vehicle routing","large-scale neighbourhood search"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Praveen Tumuluri"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/88233"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The vehicle routing problem with shipment pick-up and delivery with time windows (VRPPDTW) is one of the core problems that is addressed by a package delivery company in its operations. Most often, this problem has been addressed from the point of view of cost-cutting, to achieve the lowest cost possible under a given/predicted demand and service time scenario. This thesis aims to study a real-world VRPPDTW problem with side-constraints and build solutions that are cost-effective as well as robust to stochasticity in demands and service times. Even without the additional side constraints, the VRPPDTW is NP-hard. In particular, we consider the solution of VRPPDTW with side-constraints adopted by a carrier. Because of the nature as well as the size of the problem and the network, we demonstrate that the problem is combinatorially explosive. We therefore develop a large-scale neighbourhood search heuristic combined with a break-and-join heuristic and a clustering heuristic. We use this heuristic to build a set of schedules with far lower operating costs than the existing solution and effectively decrease the costs by 15% by reducing the number of routes needed to serve the shipments. We then build a framework to evaluate the performance of the solutions under stochasticity, and present results related to under stochasticity in service times.","Submission published under a 24 month embargo labeled 'U of I only', the embargo will last until 2017-08-01","The student, Praveen Tumuluri, accepted the attached license on 2015-07-24 at 14:56.","The student, Praveen Tumuluri, submitted this Thesis for approval on 2015-07-24 at 15:05.","This Thesis was approved for publication on 2015-07-24 at 15:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8638 on 2015-09-29 at 15:01:02","Made available in DSpace on 2015-09-29T20:50:26Z (GMT). No. of bitstreams: 2 TUMULURI-THESIS-2015.pdf: 1651416 bytes, checksum: 56112d387b93a01a72fc3dda4fc77318 (MD5) LICENSE.txt: 4213 bytes, checksum: 4311ce167f6ae1ebca44ba127ea55d91 (MD5) Previous issue date: 2015-07-24","Embargo set by: Seth Robbins for item 89513 Lift date: 2017-09-29T20:50:34Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 89513 on 2017-09-30T09:15:21Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A large-scale neighborhood search approach to vehicle routing pick-up and delivery problem with time windows under uncertainty"]}]}],"canonical_facts":{"dc:contributor":["Lavanya, Marla"],"dc:creator":["Tumuluri, Praveen"],"dc:date":["2015-09-29T20:50:26Z","2017-09-30T09:15:21Z","2015-08","2015-07-24","2015-8"],"dc:description":["The vehicle routing problem with shipment pick-up and delivery with time windows (VRPPDTW) is one of the core problems that is addressed by a package delivery company in its operations. Most often, this problem has been addressed from the point of view of cost-cutting, to achieve the lowest cost possible under a given/predicted demand and service time scenario. This thesis aims to study a real-world VRPPDTW problem with side-constraints and build solutions that are cost-effective as well as robust to stochasticity in demands and service times. Even without the additional side constraints, the VRPPDTW is NP-hard. In particular, we consider the solution of VRPPDTW with side-constraints adopted by a carrier. Because of the nature as well as the size of the problem and the network, we demonstrate that the problem is combinatorially explosive. We therefore develop a large-scale neighbourhood search heuristic combined with a break-and-join heuristic and a clustering heuristic. We use this heuristic to build a set of schedules with far lower operating costs than the existing solution and effectively decrease the costs by 15% by reducing the number of routes needed to serve the shipments. We then build a framework to evaluate the performance of the solutions under stochasticity, and present results related to under stochasticity in service times.","Submission published under a 24 month embargo labeled 'U of I only', the embargo will last until 2017-08-01","The student, Praveen Tumuluri, accepted the attached license on 2015-07-24 at 14:56.","The student, Praveen Tumuluri, submitted this Thesis for approval on 2015-07-24 at 15:05.","This Thesis was approved for publication on 2015-07-24 at 15:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8638 on 2015-09-29 at 15:01:02","Made available in DSpace on 2015-09-29T20:50:26Z (GMT). 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