A Stochastic Programming Model for Service Scheduling with Uncertain Demand: An Application in Open-Access Clinic Scheduling

This paper addresses a scheduling problem which handles urgent tasks along withexisting schedules. The uncertainty in this problem comes from random situations ofexisting schedules and arrival of upcoming urgent tasks. To deal with the uncertainty,this paper proposes a stochastic integer programming (SIP) based aggregated onlinescheduling method. The method is illustrated through a study case from the outpatientclinic block-wise scheduling system which is under a hybrid scheduling policycombining regular far-in-advance policy and the open-access policy. The COVID-19pandemic brings more challenges for the healthcare system including the fluctuationsof serving time, and increasing urgent requests which this paper is designed for. TheSIP model designed in the method can easily accommodate uncertainties of theproblems, such as: no-shows, cancellations and punctuality of previously scheduledpatients as well as random arrival and preference of new patients. To solve the SIPmodel, the deterministic equivalent problem formulations are solved using theproposed bound-based sampling method.