Операционная аналитика

Бакалавриат 2024/2025

Статус: Курс по выбору (Совместная программа по экономике НИУ ВШЭ и РЭШ)

Направление: 38.03.01. Экономика

Кто читает: Отдел сопровождения учебного процесса в бакалавриате

Где читается: Факультет экономических наук

Когда читается: 4-й курс, 1 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для своего кампуса

Язык: русский

Кредиты: 3

Полная версия программы учебной дисциплины

Аннотация

We will focus on two themes: making optimal decisions anddealing with uncertainty and risk. We will start with basic business process analysis and find out how it could be analyzed using linear optimization techniques. Next, we will introduce uncertainty in our models of business processes and find out how Markov modeling techniques can help us understand and manage the resulting inefficiencies. (For example, how should an intensive care unit admit its patients?) After that, we will combine the two perspectives by looking at inventory management under uncertainty. For example, how should a fashion retailer decide on the order quantity for a new cool T-shirt? Developing these ideas, we will arrive at basic models of stochastic optimization, and apply them to strategic problems: for example, if acompany is investing in two new car factories, should these factories be able to produce multiple car models or should they be focused on a single one? Finally, we will arrive at multi-period models that could be solved with dynamic programming, which have applications from retailassortment planning to plane ticket selling to patient appointment scheduling.

Цель освоения дисциплины

This course aims to develop hands-on analytics skills by combining lecture-based classes focused on mathematical modeling with live coding sessions and programming-based homeworks. Additionally, a group project assignment encourages you to look for creative applications of the methods we explore – focused at a problem of your own choosing.

Планируемые результаты обучения

Implement optimization and simulation models in code.
Formalize business problems mathematically.
Apply linear and integer programming techniques to optimize business processes.
Model and manage uncertainty in business scenarios using Markov modeling and stochastic optimization.
Implement simulation models to evaluate complex systems under uncertainty.
Develop and analyze stochastic optimization models for decision-making under uncertainty, and understand their strategic implications.