{"id":{"repo_id":"york","oai_identifier":"oai:yorkspace.library.yorku.ca:10315/38011"},"canonical_url":"https://search.dev.ndltd.org/etd/york/oai:yorkspace.library.yorku.ca:10315/38011","repository":{"repo_id":"york","name":"York University","base_url":"https://yorkspace.library.yorku.ca/oai/request"},"display":{"title":"Models for Capacity Allocation in Anticipation of Time-Varying Demand","abstract":"In this dissertation, we propose and investigate several stationary capacity allocation methods that anticipate time-varying demand. We apply these techniques to three practical settings in customer acquisition and retention, cloud computing, and healthcare. In the first part of this dissertation, we model the trade-off between customer acquisition and retention as a multi-class queueing network with returning customers, time-dependent arrivals, and abandonment. Based on its fluid approximation, we propose an approach to determine optimal stationary staffing levels by partitioning the time-limiting solution of the dynamical system. We test our method by applying it to two real-world applications, i.e., advertising campaigns and a clinical setting, and demonstrate its superiority when comparing to other state-of-the-art approaches. In the second part, we analyze a cloud computing system where a provider wants to determine the optimal number of servers and retrial interval for incoming jobs when all servers are busy. Servers in this setting represent components of a computer network and customers are jobs attempting to access the cloud computing infrastructure. By modeling the system as a fluid queue and using a calculus-of-variations approach, we derive the optimal amount of service capacity and retrial interval in anticipation of time-varying dynamics. We conduct a case study using data collected from a real cloud service provider and show that significant savings can be realized. Finally, we estimate the demand for personal protective equipment (PPE) in the general internal medicine (GIM) department of a hospital during the COVID-19 pandemic. We derive closed-form estimates of demand for multiple types of PPE using a queueing framework with generally distributed service times that models medical interactions with heterogeneous patients whose hospital admissions are time-varying. We parametrize our predictive model using a data set containing patients' clinical and operational records over a period of 9 years. We find that gloves and surgical masks represent approximately 90\\% of predicted PPE usage. We also find that while demand for gloves is driven entirely by patient-practitioner interactions, 86\\% of the predicted demand for surgical masks can be attributed to the requirement that medical practitioners will need to wear them when not interacting with patients.","abstract_html":"In this dissertation, we propose and investigate several stationary capacity allocation methods that anticipate time-varying demand. We apply these techniques to three practical settings in customer acquisition and retention, cloud computing, and healthcare. In the first part of this dissertation, we model the trade-off between customer acquisition and retention as a multi-class queueing network with returning customers, time-dependent arrivals, and abandonment. Based on its fluid approximation, we propose an approach to determine optimal stationary staffing levels by partitioning the time-limiting solution of the dynamical system. We test our method by applying it to two real-world applications, i.e., advertising campaigns and a clinical setting, and demonstrate its superiority when comparing to other state-of-the-art approaches. In the second part, we analyze a cloud computing system where a provider wants to determine the optimal number of servers and retrial interval for incoming jobs when all servers are busy. Servers in this setting represent components of a computer network and customers are jobs attempting to access the cloud computing infrastructure. By modeling the system as a fluid queue and using a calculus-of-variations approach, we derive the optimal amount of service capacity and retrial interval in anticipation of time-varying dynamics. We conduct a case study using data collected from a real cloud service provider and show that significant savings can be realized. Finally, we estimate the demand for personal protective equipment (PPE) in the general internal medicine (GIM) department of a hospital during the COVID-19 pandemic. We derive closed-form estimates of demand for multiple types of PPE using a queueing framework with generally distributed service times that models medical interactions with heterogeneous patients whose hospital admissions are time-varying. We parametrize our predictive model using a data set containing patients' clinical and operational records over a period of 9 years. We find that gloves and surgical masks represent approximately 90\\% of predicted PPE usage. We also find that while demand for gloves is driven entirely by patient-practitioner interactions, 86\\% of the predicted demand for surgical masks can be attributed to the requirement that medical practitioners will need to wear them when not interacting with patients.","abstract_has_math":false,"creators":["Furman, Evghenii S."],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Diamant, Adam"],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-11-13","date_published":"2020-11-13","updated_at":"2026-07-24T06:33:46Z","subjects":["Health care management"],"languages":["en"],"rights":["Author owns copyright, except where explicitly noted. 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We test our method by applying it to two real-world applications, i.e., advertising campaigns and a clinical setting, and demonstrate its superiority when comparing to other state-of-the-art approaches. In the second part, we analyze a cloud computing system where a provider wants to determine the optimal number of servers and retrial interval for incoming jobs when all servers are busy. Servers in this setting represent components of a computer network and customers are jobs attempting to access the cloud computing infrastructure. By modeling the system as a fluid queue and using a calculus-of-variations approach, we derive the optimal amount of service capacity and retrial interval in anticipation of time-varying dynamics. We conduct a case study using data collected from a real cloud service provider and show that significant savings can be realized. Finally, we estimate the demand for personal protective equipment (PPE) in the general internal medicine (GIM) department of a hospital during the COVID-19 pandemic. We derive closed-form estimates of demand for multiple types of PPE using a queueing framework with generally distributed service times that models medical interactions with heterogeneous patients whose hospital admissions are time-varying. We parametrize our predictive model using a data set containing patients' clinical and operational records over a period of 9 years. We find that gloves and surgical masks represent approximately 90\\% of predicted PPE usage. We also find that while demand for gloves is driven entirely by patient-practitioner interactions, 86\\% of the predicted demand for surgical masks can be attributed to the requirement that medical practitioners will need to wear them when not interacting with patients."]},{"key":"dc:title","label":"Title","values":["Models for Capacity Allocation in Anticipation of Time-Varying Demand"]}]}],"canonical_facts":{"dc:contributor.advisor":["Diamant, Adam"],"dc:creator":["Furman, Evghenii S."],"dc:date.accessioned":["2020-11-13T14:06:02Z"],"dc:date.available":["2020-11-13T14:06:02Z"],"dc:date.issued":["2020-11-13"],"dc:description.abstract":["In this dissertation, we propose and investigate several stationary capacity allocation methods that anticipate time-varying demand. We apply these techniques to three practical settings in customer acquisition and retention, cloud computing, and healthcare. In the first part of this dissertation, we model the trade-off between customer acquisition and retention as a multi-class queueing network with returning customers, time-dependent arrivals, and abandonment. Based on its fluid approximation, we propose an approach to determine optimal stationary staffing levels by partitioning the time-limiting solution of the dynamical system. We test our method by applying it to two real-world applications, i.e., advertising campaigns and a clinical setting, and demonstrate its superiority when comparing to other state-of-the-art approaches. In the second part, we analyze a cloud computing system where a provider wants to determine the optimal number of servers and retrial interval for incoming jobs when all servers are busy. Servers in this setting represent components of a computer network and customers are jobs attempting to access the cloud computing infrastructure. By modeling the system as a fluid queue and using a calculus-of-variations approach, we derive the optimal amount of service capacity and retrial interval in anticipation of time-varying dynamics. We conduct a case study using data collected from a real cloud service provider and show that significant savings can be realized. Finally, we estimate the demand for personal protective equipment (PPE) in the general internal medicine (GIM) department of a hospital during the COVID-19 pandemic. We derive closed-form estimates of demand for multiple types of PPE using a queueing framework with generally distributed service times that models medical interactions with heterogeneous patients whose hospital admissions are time-varying. We parametrize our predictive model using a data set containing patients' clinical and operational records over a period of 9 years. We find that gloves and surgical masks represent approximately 90\\% of predicted PPE usage. We also find that while demand for gloves is driven entirely by patient-practitioner interactions, 86\\% of the predicted demand for surgical masks can be attributed to the requirement that medical practitioners will need to wear them when not interacting with patients."],"dc:identifier.uri":["http://hdl.handle.net/10315/38011"],"dc:language":["en"],"dc:rights":["Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests."],"dc:subject":["Health care management"],"dc:title":["Models for Capacity Allocation in Anticipation of Time-Varying Demand"],"dc:type":["Electronic Thesis or Dissertation"]},"updated_at":"2026-07-24T06:33:46Z"}