Simulation
Simulering

ETS061F, 7.5 credits

Valid from: Spring 2017
Decided by: Professor Thomas Johansson
Date of establishment: 2017-05-04

General Information

Division: Electrical and Information Technology
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course code: ETS061
Teaching language: English

Aim

The purpose of the course is to give an introduction to discrete event simulation, basic optimization approaches, and heuristic methods such as simulated annealing, tabu search, evolutionary algorithms and GRASP.

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• Have some knowledge on different kinds of dynamic models that are used in engineering
• Describe the event-scheduling and the process-oriented approach to writing simulation programs
• Know how to estimate the accuracy of simulation results
• Know the basic notions in optimization theory
• Know how to solve linear and integer optimization problems
• Know basic concepts of the computational complexity theory
• Know the most common heuristic methods for optimization

Competences and Skills

For a passing grade the doctoral student must

• Write well-structured simulation programs in a general programming language
• Estimate the accuracy of simulation results
• Be able to verify and validate simulation programs
• Know what is a general static optimization problem, a general convex problem and its dual, and a combinatorial optimization problem
• Understand how duality applies to linear programming (LP) problems and column generation in LP
• Be able to apply the simplex algorithm to linear programming problems
• Apply LP approximation to nonlinear objective functions
• Understand the connection between integer programming (IP) and LP
• Be able to apply the branch-and-bound method to IP, and understand what is the cutting plane method in IP
• Understand the basic notions in computational complexity, including polynomial problems and NP-hardness
• Have a basic knowledge of heuristic methods in combinatorial optimization
• Be able to implement simulated annealing and evolutionary algorithms
• Be familiar with Monte Carlo techniques

Judgement and Approach

For a passing grade the doctoral student must

• Show knowledge of the possibilities and limitations of simulation experiments
• Be able to independently construct models for optimization problems and to apply the GUROBI optimization package (or alike) for resolving them with full understanding of the solution process and output data
• Be able to choose and apply a heuristic method to solve an optimization problems

Course Contents

In the course we start by studying discrete event simulation. Students learn to write process-oriented and event-scheduling simulation programs in general programming languages. Estimation of accuracy, random number generation, methods for studying rare events, verification and validation are also covered. Then we proceed to optimization techniques. We study convex problems and their duals. Further, we go to linear programs (LP), the simplex algorithm, and the column generation technique. We show how to model non-linearity. After that we consider integer programming (IP), its relation to LP, and the branch-and-bound method for IP. We also mention the cutting plane method for IP and sketch the computational complexity theory, including the notions of polynomial problems and NP-hardness. Finally, we consider heuristic methods for combinatorial optimization problems viewed as optimization through simulation. We explain the local search and the role of randomness. We explain the basic meta-heuristics such as simulated annealing, evolutionary algorithms, and GRASP. We also illustrate the Monte Carlo techniques.

Course Literature

Nyberg, C.: Kompendium i simulering..

Instruction Details

Types of instruction: Lectures, exercises

Examination Details

Examination formats: Written exam, written assignments. A pass grade requires passed assignments and passed home exam
Grading scale: Failed, pass
Examiner:

Admission Details

Assumed prior knowledge: Programming, Basic probability, Statistical methods, Mathematical analysis.

Further Information

Course coordinator, professor Björn Landfeldt

Course Occasion Information

Contact and Other Information

Course coordinators:
Web page: http://www.eit.lth.se/kurs/ets061