{"659442":{"#nid":"659442","#data":{"type":"event","title":"PhD Defense by Connor Riley","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EThesis Title\u003C\/strong\u003E: Operating on-demand ride-sharing services\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAdvisor:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Pascal Van Hentenryck, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EThesis Committee:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Mathieu Dahan, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Alan Erera, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Pinar Keskinocak, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Antoine Legrain, , Department of Mathematical and Industrial Engineering, Polytechnique Montr\u0026eacute;al\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EDate and Time\u003C\/strong\u003E: Wednesday, July 27th, 2022, at 1pm (EDT)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ELocation\u003C\/strong\u003E: Main 126\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EMeeting Link\u003C\/strong\u003E: Click \u003Ca href=\u0022https:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_MTE2YzU2ODEtZTE3ZC00ODE0LTk2OWEtNGMwMDBhYTJiYmQ3%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%228ef983b8-2b82-4a64-9620-1ecdfe1be1cd%22%7d\u0022\u003Ehere\u003C\/a\u003E to join Teams meeting\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EPublic transit agencies are increasingly exploring mobility options to supplement their traditional rail, bus, and streetcar offerings [1, 2]. One such option is Demand Response Service, \u0026ldquo;any non-fixed route system of transporting individuals that requires advanced scheduling by the customer\u0026rdquo; [3]. These Demand Response Services present challenging design and operations problems, including fleet sizing, network design, and dispatching. In this thesis, we present optimization-based techniques centered around one such operational problem: vehicle dispatching.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn Chapter 2, we review the real-time dial-a-ride problem, a vehicle routing problem with pickups and deliveries, deviation, and capacity constraints, and present a dispatching algorithm, M-RTRS, which provides service guarantees, serving all customers with a small number of vehicles while minimizing wait times. In a computational study, we show that this algorithm scales to over 30,000 requests per hour, providing an effective way to support large-scale ride-sharing services in dense cities.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn Chapter 3, we introduce an approach for vehicle dispatching, A-RTRS, that tightly integrates a state-of-the-art dispatching algorithm, a machine-learning model to predict zone-to-zone demand over time, and a model predictive control optimization to relocate idle vehicles. This is shown to decrease the average wait time of passengers in a computational study.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn Chapter 4, we present a relocation algorithm designed to address two challenges faced when deploying a real-world real-time dial-a-ride service. The first, a lack of historic data, as initial adoption may be slow, and accumulating the amount of data needed for the machine learning approach to demand prediction presented in Chapter 3 may be impractical. The second, that vehicles may be restricted in the locations that they may idle, which must be considered when relocating them. In a computational study, we show this approach yields similar average wait time decreases to A-RTRS.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Operating on-demand ride-sharing services  "}],"uid":"27707","created_gmt":"2022-07-15 23:43:06","changed_gmt":"2022-07-15 23:43:06","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2022-07-27T14:00:00-04:00","event_time_end":"2022-07-27T16:00:00-04:00","event_time_end_last":"2022-07-27T16:00:00-04:00","gmt_time_start":"2022-07-27 18:00:00","gmt_time_end":"2022-07-27 20:00:00","gmt_time_end_last":"2022-07-27 20:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}