  The number of new cancer cases is expected to increase by about 50% in the
next 20 years, and the need for chemotherapy treatments will increase
accordingly. Chemotherapy treatments are usually performed in outpatient cancer
centers where patients affected by different types of tumors are treated. The
treatment delivery must be carefully planned to optimize the use of limited
resources, such as drugs, medical and nursing staff, consultation and exam
rooms, and chairs and beds for the drug infusion. Planning and scheduling
chemotherapy treatments involve different problems at different decision
levels. In this work, we focus on the patient chemotherapy multi-appointment
planning and scheduling problem at an operational level, namely the problem of
determining the day and starting time of the oncologist visit and drug infusion
for a set of patients to be scheduled along a short-term planning horizon. We
use a per-pathology paradigm, where the days of the week in which patients can
be treated, depending on their pathology, are known. We consider different
metrics and formulate the problem as a multi-objective optimization problem
tackled by sequentially solving three problems in a lexicographic
multi-objective fashion. The ultimate aim is to minimize the patient's
discomfort. The problems turn out to be computationally challenging, thus we
propose bounds and ad-hoc approaches, exploiting alternative problem
formulations, decomposition, and $k$-opt search. The approaches are tested on
real data from an Italian outpatient cancer center and outperform
state-of-the-art solvers.
