From Predictive to Prescriptive Analytics: A Framework for Repairable Stochastic Scheduling

Everyday Language Explanation: Imagine you are planning a busy day at work with multiple tasks to complete, but some of the tasks take longer than expected. You want to make sure you allocate your time and resources efficiently without overcommitting. One way to do this is by using stochastic programming, which is like playing a game of chance where you solve an optimization problem to find the best schedule based on the probability of each task taking different amounts of time. However, this approach can be computationally complex and may result in overly cautious solutions. Another option is robust optimization, which involves adding buffer zones to your schedule to account for uncertainty. This approach can be more practical but might lead to overspending on resources if the buffers are not carefully designed. A simpler approach is deterministic constraint programming, which involves computing scenario averages of the unknown optimization coefficients and using constraint programming to model the task scheduling problem. By considering uncertainty in your decision-making process, you can avoid overspending on resources and make sure you complete all your tasks on time.