Stochastic OR Methods for Data Scientists

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• demonstrate skills in using computer software and programming.
• demonstrate knowledge and understanding of the concepts and applications of simulation, decision theory and the basics of queueing theory.
• demonstrate skills in technical report writing.
• demonstrate skills in team working.
• implement and analyse a discrete event simulation model using Simul8 software.

Simulation: The emphasis is on simulation computing skills, which are developed in computer labs based on the SIMUL8 package.

Lectures on simulation cover: the concept of randomness; and sampling from probability distributions including discrete and continuous models.

Decision Theory: Bayes’ rule, value of information, Decision trees, and the concept of utility,

Queueing Theory: Basic elements of a queue, definition of a Markov Process and applications of queuing systems.

Six 2-hour OR techniques lectures

Five 1-hour OR techniques tutorial sessions

Two 2-hour simulation lectures

Three 1-hour simulation computer sessions

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.