Home health care logistics planning
Bennett, Ashlea R.
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This thesis develops quantitative methods which incorporate transportation modeling for tactical and operational home health logistics planning problems. We define home health nurse routing and scheduling (HHNRS) problems, which are dynamic periodic routing and scheduling problems with fixed appointment times, where a set of patients must be visited by a home health nurse according to a prescribed weekly frequency for a prescribed number of consecutive weeks during a planning horizon, and each patient visit must be assigned an appointment time belonging to an allowable menu of equally-spaced times. Patient requests are revealed incrementally, and appointment time selections must be made without knowledge of future requests. First, a static problem variant is studied to understand the impact of fixed appointment times on routing and scheduling decisions, independent of other complicating factors in the HHNRS problem. The costs of offering fixed appointment times are quantified, and purely distance-based heuristics are shown to have potential limitations for appointment time problems unless proposed arc cost transformations are used. Building on this result, a new rolling horizon capacity-based heuristic is developed for HHNRS problems. The heuristic considers interactions between travel times, service times, and the fixed appointment time menu when inserting appointments for currently revealed patient requests into partial nurse schedules. The heuristic is shown to outperform a distance-based heuristic on metrics which emphasize meeting as much patient demand as possible. The home health nurse districting (HHND) problem is a tactical planning problem which influences HHNRS problem solution quality. A set of geographic zones must be partitioned into districts to be served by home health nurses, such that workload is balanced across districts and nurse travel is minimized. A set partitioning model for HHND is formulated and a column generation heuristic is developed which integrates ideas from optimization and local search. Methods for estimating district travel and workload are developed and implemented within the heuristic, which outperforms local search on test instances.