Simulation Modeling and Analysis
Simulation Modeling and Analysis
Dynamic Allocation of Kidneys to Candidates on the Transplant Waiting List
Operations Research
Dynamic routing of customers with general delay costs in a multiserver queuing system
Probability in the Engineering and Informational Sciences
Dynamic Multipriority Patient Scheduling for a Diagnostic Resource
Operations Research
Reducing Delays for Medical Appointments: A Queueing Approach
Operations Research
Revenue Management for a Primary-Care Clinic in the Presence of Patient Choice
Operations Research
A Comparison of Traditional and Open-Access Policies for Appointment Scheduling
Manufacturing & Service Operations Management
Adaptive Appointment Systems with Patient Preferences
Manufacturing & Service Operations Management
Outpatient appointment scheduling with variable interappointment times
Modelling and Simulation in Engineering
Manufacturing & Service Operations Management
Manufacturing & Service Operations Management
Performance-Based Contracts for Outpatient Medical Services
Manufacturing & Service Operations Management
Appointment Scheduling Under Patient No-Shows and Service Interruptions
Manufacturing & Service Operations Management
Optimization-based decision support system for crew scheduling in the cruise industry
Computers and Industrial Engineering
Multiresource Allocation Scheduling in Dynamic Environments
Manufacturing & Service Operations Management
| Hi-index | 0.00 |
This paper develops a framework and proposes heuristic dynamic policies for scheduling patient appointments, taking into account the fact that patients may cancel or not show up for their appointments. In a simulation study that considers a model clinic, which is created using data obtained from an actual clinic, we find that the heuristics proposed outperform all the other benchmark policies, particularly when the patient load is high compared with the regular capacity. Supporting earlier findings in the literature, we find that the open access policy, a recently proposed popular scheduling paradigm that calls for “meeting today's demand today,” can be a reasonable choice when the patient load is relatively low.