Integrated Home Care Staffing and Capacity Planning: Stochastic Optimization Approaches

Abstract

We propose stochastic optimization methodologies for a staffing and capacity planning problem arising from home care practice. Specifically, we consider the perspective of a home care agency that must decide the number of caregivers to hire (staffing) and the allocation of hired caregivers to different types of services (capacity planning) in each day within a specified planning horizon. The objective is to minimize the total cost associated with staffing (i.e., employment), capacity allocation, over-staffing, and under-staffing. We propose two-stage stochastic programming (SP) and distributionally robust optimization (DRO) approaches to model and solve this problem considering two types of decision-makers, namely an everything in advance decision-maker (EA) and a flexible adjustment decision-maker (FA). In the EA models, we determine the staffing and capacity allocation decisions in the first stage before observing the demand. In the FA models, we decide the staffing decisions in the first stage. Then, we determine the capacity allocation decisions based on demand realizations in the second stage.We derive equivalent mixed-integer linear programming (MILP) reformulations of the proposed nonlinear DRO model for the EA decision-maker that can be implemented and efficiently solved using off-the-shelf optimization software. We propose a computationally efficient column-andconstraint generation algorithm with valid inequalities to solve the proposed DRO model for the FA decision-maker. Finally, we conduct extensive numerical experiments comparing the operational and computational performance of the proposed approaches and discuss insights and implications for home care staffing and capacity planning.

A Robust Framework for Addressing Routing and Scheduling Challenges in Home Health Care

“…A hybrid simulated annealing algorithm was used to solve the model (Du and Zhang [46]). Wang et al [47] also proposed SO methodologies for a staffing and capacity planning problem for HHC services. Their goal was to minimize the total cost of staffing, capacity allocation, over-, and under-staffing.…”

“…The authors presented two-stage SO and distributionally RO approaches considering two types of decision-makers. To enhance the applicability of the nonlinear RO model, they derived equivalent mixed-integer linear programming reformulations (Wang et al [47]). Furthermore, a HHCRSP considered patients' priorities and times uncertainty.…”