Minimizing total cost in scheduling outpatient appointments
Management Science
Monotone structure in discrete-event systems
Monotone structure in discrete-event systems
Admission Policies in Loss Queueing Models with Heterogeneous Arrivals
Management Science
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Scheduling: Theory, Algorithms, and Systems
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Operations Research
An elective surgery scheduling problem considering patient priority
Computers and Operations Research
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Decision Support Systems
Appointment Scheduling with Discrete Random Durations
Mathematics of Operations Research
A Markov decision process approach to multi-category patient scheduling in a diagnostic facility
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Lagrangian relaxation and constraint generation for allocation and advanced scheduling
Computers and Operations Research
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Manufacturing & Service Operations Management
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Decision Support Systems
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Decision Support Systems
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Manufacturing & Service Operations Management
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Manufacturing & Service Operations Management
Appointment Scheduling Under Patient No-Shows and Service Interruptions
Manufacturing & Service Operations Management
Optimizing Intensive Care Unit Discharge Decisions with Patient Readmissions
Operations Research
Comparing two operating-room-allocation policies for elective and emergency surgeries
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Proceedings of the Winter Simulation Conference
A Lagrangian approach to dynamic resource allocation
Proceedings of the Winter Simulation Conference
Multiresource Allocation Scheduling in Dynamic Environments
Manufacturing & Service Operations Management
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Hospital diagnostic facilities, such as magnetic resonance imaging centers, typically provide service to several diverse patient groups: outpatients, who are scheduled in advance; inpatients, whose demands are generated randomly during the day; and emergency patients, who must be served as soon as possible. Our analysis focuses on two interrelated tasks: designing the outpatient appointment schedule, and establishing dynamic priority rules for admitting patients into service. We formulate the problem of managing patient demand for diagnostic service as a finite-horizon dynamic program and identify properties of the optimal policies. Using empirical data from a major urban hospital, we conduct numerical studies to develop insights into the sensitivity of the optimal policies to the various cost and probability parameters and to evaluate the performance of several heuristic rules for appointment acceptance and patient scheduling.