{"420031":{"#nid":"420031","#data":{"type":"event","title":"PhD Defense by Chenxi Zeng","body":[{"value":"\u003Cp\u003EStudent: \u003Cstrong\u003EChenxi Zeng\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAdvisor\/Chairperson: Prof. Chelsea C. White III\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ECommittee Members: Prof. Turgay Ayer (ISYE), Prof. Alan Erera (ISYE), Prof. Julie Swann (ISYE) and Prof. David A. Bader (CSE)\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThesis Title:\u003Cstrong\u003E A MINIMUM COST AND RISK MITIGATION APPROACH FOR BLOOD COLLECTION\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDate\/Time\/Location: July 14 2015, 11am-2pm, Groseclose Building, Rm 402\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003Efac\u0026nbsp; Due to the limited supply and perishable nature of blood products, effective man-\u003Cbr \/\u003E agement of blood collection is critical for high quality healthcare delivery. Whole\u003Cbr \/\u003E blood is typically collected over a 6 to 8 hour collection window from volunteer donors\u003Cbr \/\u003E at sites, e.g., schools, universities, churches, companies, that are a significant distance\u003Cbr \/\u003E from the blood products processing facility and then transported from collection site\u003Cbr \/\u003E to processing facility by a blood mobile.\u003Cbr \/\u003E \u003Cbr \/\u003E The length of time between collecting whole blood and processing it into cryo-\u003Cbr \/\u003E precipitate (\u0022cryo\u0022), a critical blood product for controlling massive hemorrhaging,\u003Cbr \/\u003E cannot take longer than 8 hours (the 8 hour collection to completion constraint),\u003Cbr \/\u003E while the collection to completion constraint for other blood products is 24 hours. In\u003Cbr \/\u003E order to meet the collection to completion constraint for cryo, it is often necessary\u003Cbr \/\u003E to have a \u0022mid-drive collection\u0022; i.e., for a vehicle other than the blood mobile to\u003Cbr \/\u003E pickup and transport, at extra cost, whole blood units collected during early in the\u003Cbr \/\u003E collection window to the processing facility.\u003Cbr \/\u003E \u003Cbr \/\u003E In this dissertation, we develop Markov decision process (MDP) models to: (1)\u003Cbr \/\u003E analyze which collection sites should be designated as cryo collection sites to mini-\u003Cbr \/\u003E mize total collection costs while satisfying the collection to completion constraint and\u003Cbr \/\u003E meeting the weekly production target (the non-split case), (2) analyze the impact of\u003Cbr \/\u003E changing the current process to allow collection windows to be split into two intervals\u003Cbr \/\u003E and then determining which intervals should be designated as cryo collection intervals\u003Cbr \/\u003E (the split case), (3) use several methods to insure that the weekly production target\u003Cbr \/\u003E is met, and then build a decision support tool to provide operational decision support\u003Cbr \/\u003E to plan collection schedules.\u003Cbr \/\u003E \u003Cbr \/\u003E These problems lead to MDP models with large state and action spaces and con-\u003Cbr \/\u003E straints to guarantee that the weekly production target is met with high probability.\u003Cbr \/\u003E These models are computationally intractable for problems having state and action\u003Cbr \/\u003E spaces of realistic cardinality.\u003Cbr \/\u003E \u003Cbr \/\u003E We consider two approaches to guarantee that the weekly production target is met\u003Cbr \/\u003E with high probability: (1) a penalty function approach and (2) a chance constraint\u003Cbr \/\u003E approach. For the MDP with penalty function approach, we ?rst relax a constraint\u003Cbr \/\u003E that signi?cantly reduces the cardinality of the state space and provides a lower bound\u003Cbr \/\u003E on the optimal expected weekly cost of collecting whole blood for cryo while satisfying\u003Cbr \/\u003E the collection to completion constraint. We then present an action elimination proce-\u003Cbr \/\u003E dure that coupled with the constraint relaxation leads to a computationally tractable\u003Cbr \/\u003E lower bound. We then develop several heuristics that generate sub-optimal policies\u003Cbr \/\u003E and provide an analytical description of the difference between the upper and lower\u003Cbr \/\u003E bounds in order to determine the quality of the heuristics.\u003Cbr \/\u003E \u003Cbr \/\u003E For the multiple decision epoch MDP model with chance constraint approach, we\u003Cbr \/\u003E first note by example that a straightforward application of dynamic programming can\u003Cbr \/\u003E lead to a sub-optimal policy. We then restrict the model to a single decision epoch.\u003Cbr \/\u003E We then use a computationally tractable rolling horizon procedure for policy deter-\u003Cbr \/\u003E mination. We also present a simple greedy heuristic (another rolling horizon decision\u003Cbr \/\u003E making procedure) based on ranking the collection intervals by mid-drive pickup cost\u003Cbr \/\u003E per unit of expected cryo collected, which results in a competitive sub-optimal solu-\u003Cbr \/\u003E tion and leads to the development of a practical decision support tool (DST). Using\u003Cbr \/\u003E real data from the American Red Cross (ARC), we estimate that this DST reduces\u003Cbr \/\u003E total cost by about 30% for the non-split case and 70% for the split case, compared\u003Cbr \/\u003E to the current practice. Initial implementation of the DST at the ARC Southern\u003Cbr \/\u003E regional manufacturing and service center supports our estimates and indicates the\u003Cbr \/\u003E potential for significant improvement in current practice.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"A MINIMUM COST AND RISK MITIGATION APPROACH FOR BLOOD COLLECTION"}],"uid":"27707","created_gmt":"2015-07-01 15:43:00","changed_gmt":"2016-10-08 02:12:22","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2015-07-14T12:00:00-04:00","event_time_end":"2015-07-14T15:00:00-04:00","event_time_end_last":"2015-07-14T15:00:00-04:00","gmt_time_start":"2015-07-14 16:00:00","gmt_time_end":"2015-07-14 19:00:00","gmt_time_end_last":"2015-07-14 19:00:00","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":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}