Optimizing chemotherapy drug production: a multi-objective scheduling approach

Chemotherapy treatment relies on the preparation of dangerous and expensive drugs, making timely and accurate drug manufacturing crucial for patient care. To address the challenges faced by hospital pharmacies, we propose a multi-objective optimization model for scheduling chemotherapy drug production. Our method aims to jointly minimize the maximum delay in delivering treatments, reduce raw material costs, and shorten overall lead times, all while considering human and material resource constraints. The approach relies on a Mixed-Integer Linear Programming model to approximate scheduling, followed by a reconstruction algorithm to generate practical schedules for pharmacists. Additionally, the model supports the selection of medications for early preparation, increasing the production buffer for subsequent days and further minimizing waste. Computational experiments, using real-world data from a regional cancer center, highlight the method's effectiveness in reducing delays, optimizing resource usage, and enhancing productivity. This scheduling framework contributes to a more efficient and cost-effective workflow in chemotherapy clinics, ultimately improving patient care and resource management.