Staffing Problems with Random Patient Demand: Solutions Using Analytic Optimization Techniques

This chapter includes examples of optimization problems using analytic techniques rather than numeric trial and error discrete event simulation optimization. The problem of staffing with minimal cost of under- and overstaffing with random patient demand is presented in Sect. 4.1 using the so-called “newsvendor” framework. A somewhat related problem of PACU (post-anesthesia care unit) optimal staffing is presented in Sect. 4.2. The issue is a highly variable day-to-day and hour-to-hour patient census. The optimal staffing plan is developed using multi-criteria optimization in which both the percentage of patients cared for by the scheduled staff and the staff usage should be maximized. Section 4.3 describes an approach for minimizing the number of tests per specimen at relatively low disease prevalence by pooling several specimens into one batch.