  The significance of last-mile logistics in the healthcare supply chain is
growing steadily, especially in pharmacies where the growing prevalence of
medication delivery to patients' homes is remarkable. This paper proposes a
novel mathematical model for the last-mile logistics of the pharmaceutical
supply chain and optimizes a pharmacy's logistical financial outcome while
considering medication synchronization, different delivery modes, and
temperature requirements of medicines. We propose a mathematical formulation of
the problem using Mixed Integer Linear Programming (MILP) evolved from the
actual problem of an outpatient pharmacy of a Dutch hospital. We create a case
study by gathering, preparing, processing, and analyzing the associated data.
We find the optimal solution, using Python MIP package and the Gurobi solver,
which indicates the number of order batches, the composition of these batches,
and the number of staff related to the preparation of the order batches. Our
results show that our optimal solution increases the pharmacy's logistical
financial outcome by 34 percent. Moreover, we propose other model variations
and perform extensive scenario analysis to provide managerial insights
applicable to other pharmacies and distributors in the last step of cold supply
chains. Based on our scenario analysis, we conclude that improving medication
synchronization can significantly enhance the pharmacy's logistical financial
outcome.
