Course Page

Stochastic Modeling and Simulation

Course Number:

70462


Faculty

Alexandre Jacquillat, ajacquil@andrew.cmu.edu


Program

Undergraduate Business


Concentrations

Operations Management


Course Description

This is a hands-on course on modeling and simulation of business systems under uncertainty. It takes the perspective of the consultant whose job is to analyze existing or potential business processes and provide recommendations for managerial decision-making. Recognizing that most businesses are subject to high levels of variability, risk and uncertainty, it will adopt a stochastic approach to characterize the behavior of business systems and processes, and explore the effects of alternative decisions in this context.

Two modeling methodologies will be covered: (i) stochastic modeling, and (ii) stochastic simulation. Examples are drawn from different managerial domains, such as supply chain management, risk management, marketing, and project management. The lectures, homework assignments, exam and term project will focus on modeling, computational, and analytical skills. Computational implementations will be done in Excel using the @Risk add-in (during the first half of the course to build simple simulation models) and the Arena software (during the second half of the course to build more complex models based on discrete-event simulation). 

Course objectives:

1. Recognize uncertainty in business systems and processes, and their impact on managerial decisions (e.g., demand uncertainty, financial risk, etc.) 

2. Model uncertainty and risk quantitatively using probabilistic tools

3. Specify probabilistic distributions for inputs from available data

4. Generate probabilistic distributions for outputs and relevant performance metrics (e.g., average, standard deviation, distribution tails) 

5. Develop computational models to simulate complex stochastic processes using appropriate software

6. Communicate outputs of uncertainty analyses and implications for managerial decision- making.


Format

Lecture: 160min/wk


Pre-requisites

None