Abstract
This paper addressed a scheduling problem which handles urgent tasks along with existing schedules. The uncertainties in this problem come from random process of existing schedules and unknown upcoming urgent tasks. To deal with the uncertainties, this paper proposes a stochastic integer programming (SIP) based aggregated online scheduling method. The method is illustrated through a study case from the outpatient clinic block-wise scheduling system which is under a hybrid scheduling policy combining regular far-in-advance policy and the open-access policy. The COVID-19 pandemic brings more challenges for the healthcare system including the fluctuations of service time and increasing urgent requests which this paper is designed for. The schedule framework designed in the method is comprehensive to accommodate various uncertainties in the healthcare service system, such as: no-shows, cancellations and punctuality of patients as well as preference of patients over time slots and physicians.Supplementary InformationThe online version contains supplementary material available at 10.1007/s43069-021-00089-6.
Highlights
Unscheduled jobs that need prompt services often complicate a scheduling problem
We proposed a framework where the stochastic integer programming is the base which contains the robust optimization as a special case
This paper suggests the clinic administrators who are practicing the open-access policy and block-wise assignment to adopt the aggregated assignment with stochastic integer programming (SIP) model
Summary
Unscheduled jobs that need prompt services often complicate a scheduling problem. This is a common problem in several industries. The unscheduled repairs of airplanes, automobiles and locomotives; or unscheduled visit of patients/customers; or breaking news for broadcasting industry.
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