This course covers basic concepts in optimization and heuristic search with an emphasis on process improvement and optimization. This course emphasizes the application of mathematical optimization models over the underlying mathematics of their algorithms. While the skills developed in this course can be applied to a very broad range of business problems, the practice examples and student exercises will focus on the following areas: healthcare, logistics and supply chain optimization, capital budgeting, asset management, portfolio analysis. Most of the student exercises will involve the use of Microsoft Excel’s “Solver” add-on package for mathematical optimization.
After taking this course, participants will be able to:
- Understand the optimization and search techniques underlying machine learning techniques such as neural networks and social network analysis.
- Develop and solve optimization models in a number of domains such as supply chain modeling, production, asset management, capital budgeting and financial portfolio analysis.
- Use genetic algorithm techniques to solve complex problems.
- Develop simulation models and design simulation experiments.
- Perform process mining on workflow logs.
This course develops participants’ ability to analyze a real-world problem and develop a mathematical formulation that is amenable to solution using techniques of operations research such as linear and integer programming, simulation and genetic algorithms. The ability to translate practical problems into representations that are amenable to analysis requires critical thinking and imagination and is an essential skill for analysts wishing to develop creative solutions in practice. While the emphasis is on modeling rather than mathematical algorithms, the analytical techniques learned in this course are essential building blocks for risk analysis, social network analysis and machine learning techniques such as neural networks. A final module of the course covers the analysis of workflow logs and introduces students to process data mining.
This course is a graduate-level academic course that carries 3 credits. It is part of Stevens’ Master of Science in Business Intelligence & Analytics program. The contents of this course can be customized for corporate delivery. The course is available in the following formats:
- Online via Stevens WebCampus (14 week delivery)
- Onsite at client location (35 contact hours for academic credit, can be delivered as a five-day module or spread over multiple sessions)
- On campus at Stevens’ Hoboken NJ campus (15 minutes from midtown Manhattan) (14 week delivery)
Alternative delivery modes (accelerated delivery, hybrid online + face-to-face delivery) are available upon request.
Prof. Edward A. Stohr