Stochastic Scheduling

Stochastic scheduling addresses the challenge of optimizing schedules when task durations or resource availability are uncertain. Current research focuses on developing robust algorithms, such as those based on constraint programming, Thompson sampling, and decision-focused learning, to handle this uncertainty, often incorporating machine learning techniques to improve prediction and adaptation. These advancements are impacting diverse fields, from resource allocation in social services and healthcare to optimizing autonomous robot operations and improving efficiency in manufacturing and logistics. The ultimate goal is to create schedules that are both efficient and resilient to unexpected events.