Scheduling is the process of allocating scarce resources to a set of tasks over time. In this course, we look at practical scheduling problems, solution techniques, and algorithms in both manufacturing and service industries. Specifically, we look at job shop scheduling, timetabling, project scheduling, supply chain scheduling, workforce scheduling, healthcare scheduling, and sports scheduling and discuss various solution procedures including heuristics, constraint programming, local search, and dispatching rules. We apply our knowledge to investigate one case study concerning real world scheduling problems and learn from one guest speaker who discusses interesting scheduling challenges and opportunities

Analytics is not a new invention, but rather a coming together of several technologies and fields of science including data warehousing and management, data mining, statistical modeling, forecasting, optimization, and most importantly management decision making under uncertainty.

 

In this course we first discuss predictive analytics that provides techniques to model the relationships between inputs and outcomes, and construct predictions about future outcomes. Then, we cover the prescriptive analytics that provides tools to optimize actions against a complex set of objectives to find best practices and design best policies under all circumstances. We also look at practical problems, solution techniques, and algorithms. Specifically, we look at examples in supply chains, service industries, healthcare systems, revenue management, inventory management, and sports. Finally, we apply our knowledge to investigate several case studies concerning real world problems and learn from a couple of guest speakers who discusses interesting challenges and opportunities that data analytics has presented.