printer friendly pageOIT 245: Optimization and Simulation Modeling
This course provides basic skills in quantitative modeling. The objective is to familiarize students with the main steps in an analytical approach to business decision making: constructing an abstract model for a relevant business problem, formulating it in a spreadsheet environment such as Microsoft Excel, and using the tools of optimization, Monte Carlo simulation and sensitivity analysis to generate and interpret recommendations. The class will be taught in a lab style, with short in-class exercises done in small teams, focusing on a variety of applications drawn from online advertising, healthcare, finance, supply chain management, revenue and yield optimization.
Terms: Aut
| Units: 3
Instructors:
Saban, D. (PI)
OIT 247: Optimization and Simulation Modeling - Accelerated
The course is aimed at students who already have a background or demonstrated aptitude for quantitative analysis, and thus are comfortable with a more rapid coverage of the topics, in more depth and breadth, than in
OIT 245.
Terms: Aut
| Units: 3
Instructors:
Bimpikis, K. (PI)
OIT 248: Optimization And Simulation Modeling - Advanced
This course constitutes an advanced option in the menu of classes satisfying the Management Perspectives requirement in Optimization and Simulation Modeling (OSM). The course is an advanced version of
OIT 245 and
OIT 247, covering a similar set of basic concepts of OSM (such as modelling for optimization, math programming, Monte Carlo simulations and decision tree). But here we use Python, instead of Excel. By the end of the course, students should develop the basic mental framework of optimization and leave with a good understanding of different types of optimization and simulation. The class is taught in an interactive style, focusing on a variety of applications drawn from advertising, healthcare, finance, supply chain management, revenue management and pricing, scheduling, and risk management. In terms of software, we will use Python as the basic software, complemented with Gurobi for formulating and solving optimization models. Note well, however, that this is not a course on Python, but on modelling and optimization. Students should be comfortable using these software packages by the end of the class, but no specific prior experience with these packages is necessary. Some prior coding experience is helpful, but is not a strict requirement for the course.
Terms: Aut
| Units: 3
Instructors:
Whang, S. (PI)
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