Delivered at:: Department of Management

Course type:: Elective course

When:: 2 year, 1, 2 module

Instructor

Sloev, Igor

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Abstract

This course covers topics in dynamic optimization methods which might be relevant for applied business research: investment decisions, pricing etc. It discovers and explores cases both in discrete and in continuous time. The methodological approaches address methods in dynamic programming and optimal solutions across infinite/finite time horizons. The course illustrates how dynamic optimization is useful for business strategies development on a rigorous analytical base.

Learning Objectives

introduce students to the main types of optimization problems and approaches to their solution;
master the methods of solving optimization dynamic problems in discrete time
teach students to apply dynamic optimization methods to solve applied problems in management

Expected Learning Outcomes

to differentiate dynamic optimisation methods
to distinguish situations where dynamic optimisation methods can be used
to apply dynamic optimization techniques for different business problems
to construct Bellman equation and find close-form solution if possible
to estimate Bellman equation

Course Contents

Programming modelling for business solutions
Bellman equation for value and profit functions
SML and SMM for problems without close-form solutions

Assessment Elements

Control work
Problem-solving discussions
Labs
exam
Control work
Problem-solving discussions
Labs
exam

Interim Assessment

Interim assessment (2 module)
0.1 * Control work + 0.5 * exam + 0.3 * Labs + 0.1 * Problem-solving discussions

Bibliography

Recommended Core Bibliography

Blot, J., & Hayek, N. (2014). Infinite-Horizon Optimal Control in the Discrete-Time Framework. New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=662694

Recommended Additional Bibliography

Akram Chibani, Xavier Delorme, Alexandre Dolgui, & Henri Pierreval. (2018). Dynamic optimisation for highly agile supply chains in e-procurement context. International Journal of Production Research, (17), 5904. https://doi.org/10.1080/00207543.2018.1458164

Master’s Programme 'Management and Analytics for Business'

Contacts

Qtem

Dynamic optimization for Business research