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Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center

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Zeitschriftentitel: Management Science
Personen und Körperschaften: Mandelbaum, Avishai, Momčilović, Petar, Trichakis, Nikolaos, Kadish, Sarah, Leib, Ryan, Bunnell, Craig A.
In: Management Science, 66, 2020, 1, S. 243-270
Medientyp: E-Article
Sprache: Englisch
veröffentlicht:
Institute for Operations Research and the Management Sciences (INFORMS)
Schlagwörter:
author_facet Mandelbaum, Avishai
Momčilović, Petar
Trichakis, Nikolaos
Kadish, Sarah
Leib, Ryan
Bunnell, Craig A.
Mandelbaum, Avishai
Momčilović, Petar
Trichakis, Nikolaos
Kadish, Sarah
Leib, Ryan
Bunnell, Craig A.
author Mandelbaum, Avishai
Momčilović, Petar
Trichakis, Nikolaos
Kadish, Sarah
Leib, Ryan
Bunnell, Craig A.
spellingShingle Mandelbaum, Avishai
Momčilović, Petar
Trichakis, Nikolaos
Kadish, Sarah
Leib, Ryan
Bunnell, Craig A.
Management Science
Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center
Management Science and Operations Research
Strategy and Management
author_sort mandelbaum, avishai
spelling Mandelbaum, Avishai Momčilović, Petar Trichakis, Nikolaos Kadish, Sarah Leib, Ryan Bunnell, Craig A. 0025-1909 1526-5501 Institute for Operations Research and the Management Sciences (INFORMS) Management Science and Operations Research Strategy and Management http://dx.doi.org/10.1287/mnsc.2018.3218 <jats:p> Service systems are often stochastic and preplanned by appointments, yet implementations of their appointment systems are prevalently deterministic. At the planning stage of healthcare services, for example, customer punctuality and service durations are often assumed equal their means—and this gap, between planned and reality, motivated our research. Specifically, we consider appointment scheduling and sequencing under a time-varying number of servers, in a data-rich environment where service durations and punctuality are uncertain. Our data-driven approach, based on infinite-server queues, yields tractable and scalable solutions that accommodate hundreds of jobs and servers. We successfully test our approach against near-optimal algorithms (which exist for merely single-servers). This entails the development of a data-driven robust optimization approach with novel uncertainty sets. To test for practical performance, we leverage a unique data set from a cancer center that combines real-time locations, electronic health records, and appointments log. Focusing on one of the center’s infusion units (roughly 90 daily appointments, 25+ infusion chairs), we reduce cost (waiting plus overtime) on the order of 15%–40% consistently, under a wide range of experimental setups. </jats:p><jats:p> This paper was accepted by Assaf Zeevi, stochastic models and simulation. </jats:p> Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center Management Science
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title Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center
title_unstemmed Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center
title_full Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center
title_fullStr Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center
title_full_unstemmed Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center
title_short Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center
title_sort data-driven appointment-scheduling under uncertainty: the case of an infusion unit in a cancer center
topic Management Science and Operations Research
Strategy and Management
url http://dx.doi.org/10.1287/mnsc.2018.3218
publishDate 2020
physical 243-270
description <jats:p> Service systems are often stochastic and preplanned by appointments, yet implementations of their appointment systems are prevalently deterministic. At the planning stage of healthcare services, for example, customer punctuality and service durations are often assumed equal their means—and this gap, between planned and reality, motivated our research. Specifically, we consider appointment scheduling and sequencing under a time-varying number of servers, in a data-rich environment where service durations and punctuality are uncertain. Our data-driven approach, based on infinite-server queues, yields tractable and scalable solutions that accommodate hundreds of jobs and servers. We successfully test our approach against near-optimal algorithms (which exist for merely single-servers). This entails the development of a data-driven robust optimization approach with novel uncertainty sets. To test for practical performance, we leverage a unique data set from a cancer center that combines real-time locations, electronic health records, and appointments log. Focusing on one of the center’s infusion units (roughly 90 daily appointments, 25+ infusion chairs), we reduce cost (waiting plus overtime) on the order of 15%–40% consistently, under a wide range of experimental setups. </jats:p><jats:p> This paper was accepted by Assaf Zeevi, stochastic models and simulation. </jats:p>
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author Mandelbaum, Avishai, Momčilović, Petar, Trichakis, Nikolaos, Kadish, Sarah, Leib, Ryan, Bunnell, Craig A.
author_facet Mandelbaum, Avishai, Momčilović, Petar, Trichakis, Nikolaos, Kadish, Sarah, Leib, Ryan, Bunnell, Craig A., Mandelbaum, Avishai, Momčilović, Petar, Trichakis, Nikolaos, Kadish, Sarah, Leib, Ryan, Bunnell, Craig A.
author_sort mandelbaum, avishai
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container_title Management Science
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description <jats:p> Service systems are often stochastic and preplanned by appointments, yet implementations of their appointment systems are prevalently deterministic. At the planning stage of healthcare services, for example, customer punctuality and service durations are often assumed equal their means—and this gap, between planned and reality, motivated our research. Specifically, we consider appointment scheduling and sequencing under a time-varying number of servers, in a data-rich environment where service durations and punctuality are uncertain. Our data-driven approach, based on infinite-server queues, yields tractable and scalable solutions that accommodate hundreds of jobs and servers. We successfully test our approach against near-optimal algorithms (which exist for merely single-servers). This entails the development of a data-driven robust optimization approach with novel uncertainty sets. To test for practical performance, we leverage a unique data set from a cancer center that combines real-time locations, electronic health records, and appointments log. Focusing on one of the center’s infusion units (roughly 90 daily appointments, 25+ infusion chairs), we reduce cost (waiting plus overtime) on the order of 15%–40% consistently, under a wide range of experimental setups. </jats:p><jats:p> This paper was accepted by Assaf Zeevi, stochastic models and simulation. </jats:p>
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spelling Mandelbaum, Avishai Momčilović, Petar Trichakis, Nikolaos Kadish, Sarah Leib, Ryan Bunnell, Craig A. 0025-1909 1526-5501 Institute for Operations Research and the Management Sciences (INFORMS) Management Science and Operations Research Strategy and Management http://dx.doi.org/10.1287/mnsc.2018.3218 <jats:p> Service systems are often stochastic and preplanned by appointments, yet implementations of their appointment systems are prevalently deterministic. At the planning stage of healthcare services, for example, customer punctuality and service durations are often assumed equal their means—and this gap, between planned and reality, motivated our research. Specifically, we consider appointment scheduling and sequencing under a time-varying number of servers, in a data-rich environment where service durations and punctuality are uncertain. Our data-driven approach, based on infinite-server queues, yields tractable and scalable solutions that accommodate hundreds of jobs and servers. We successfully test our approach against near-optimal algorithms (which exist for merely single-servers). This entails the development of a data-driven robust optimization approach with novel uncertainty sets. To test for practical performance, we leverage a unique data set from a cancer center that combines real-time locations, electronic health records, and appointments log. Focusing on one of the center’s infusion units (roughly 90 daily appointments, 25+ infusion chairs), we reduce cost (waiting plus overtime) on the order of 15%–40% consistently, under a wide range of experimental setups. </jats:p><jats:p> This paper was accepted by Assaf Zeevi, stochastic models and simulation. </jats:p> Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center Management Science
spellingShingle Mandelbaum, Avishai, Momčilović, Petar, Trichakis, Nikolaos, Kadish, Sarah, Leib, Ryan, Bunnell, Craig A., Management Science, Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center, Management Science and Operations Research, Strategy and Management
title Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center
title_full Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center
title_fullStr Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center
title_full_unstemmed Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center
title_short Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center
title_sort data-driven appointment-scheduling under uncertainty: the case of an infusion unit in a cancer center
title_unstemmed Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center
topic Management Science and Operations Research, Strategy and Management
url http://dx.doi.org/10.1287/mnsc.2018.3218