  Chemotherapy appointment scheduling is a challenging problem due to the
uncertainty in pre-medication and infusion durations. In this paper, we
formulate a two-stage stochastic mixed integer programming model for the
chemotherapy appointment scheduling problem under limited availability and
number of nurses and infusion chairs. The objective is to minimize the expected
weighted sum of nurse overtime, chair idle time, and patient waiting time. The
computational burden to solve real-life instances of this problem to optimality
is significantly high, even in the deterministic case. To overcome this burden,
we incorporate valid bounds and symmetry breaking constraints. Progressive
hedging algorithm is implemented in order to solve the improved formulation
heuristically. We enhance the algorithm through a penalty update method, cycle
detection and variable fixing mechanisms, and a linear approximation of the
objective function. Using numerical experiments based on real data from a major
oncology hospital, we compare our solution approach with several scheduling
heuristics from the relevant literature, generate managerial insights related
to the impact of the number of nurses and chairs on appointment schedules, and
estimate the value of stochastic solution to assess the significance of
considering uncertainty.
