Successful management requires the ability to recognize a decision situation, understand its essential features, and make a smart choice. However, many of these situations – particularly those involving uncertainty and/or complex interactions – may be too difficult to grasp intuitively, and the stakes may be too high to learn by experience. In these cases, we may benefit from using decision models – simplified representations of these situations that allow you to consider the different possible scenarios (i.e., ask “what if”) and learn more about the problem. This course introduces several commonly used modeling tools and provides an introduction to the art of modeling.

In this course, participants will learn how to develop decision-making and problem-solving skills in conjunction with a quantitative model-building approach. The course will emphasize how structured modeling techniques, probability forecasts, simulations, and computer optimization models are used in making decisions in complex settings. The course is divided into two parts. In the first part, we discuss optimization models including linear programming, integer programming, and network models. In the second part, we introduce simulation, and discuss its application in manufacturing and service operations.

After completing this course, the learner should have a better understanding of the following concepts: 

• Constrained optimization
• Linear programing
• Network models
• Integer programming
• Simulation