This book discusses how analytics can handle uncertainty and fairness issues in scheduling chemotherapy treatments. Specifically, the authors explore the complexities of chemotherapy scheduling, showing how analytics can be used to create fairer and more efficient chemotherapy schedules. By addressing uncertainty in infusion times and balancing the needs of both patient and staff, the book offers novel insights, practical models, and actionable solutions for healthcare professionals aiming to improve patient care in chemotherapy clinics. Readers will gain a deep understanding of the unique challenges in scheduling chemotherapy treatments, with a focus on handling uncertainty in treatment durations, coordinating clinic resources, and ensuring fairness among patients. This book uses real data from chemotherapy clinics to develop models and generate solutions representing fair and efficient schedules. Alongside methodological knowledge, readers will acquire managerial insights that can directly enhance the scheduling processes in real-world oncology settings.