Course syllabus

Simulering
Simulation

ETS061, 7,5 credits, A (Second Cycle)

Valid for: 2015/16
Decided by: Education Board A
Date of Decision: 2015-04-10

General Information

Elective for: C4-ks, D4-ks, E4-ks, I4, I4-pvs, Pi4
Language of instruction: The course will be given in English on demand

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.

Learning outcomes

Knowledge and understanding
For a passing grade the 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 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 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

 

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.

Examination details

Grading scale: TH
Assessment: To pass the course the student must pass the laboratory lessons, the home assignments and a written exam.

Parts
Code: 0115. Name: Examination.
Credits: 5,5. Grading scale: TH. Assessment: Written examination Contents: Written examination
Code: 0215. Name: Home Assignments and Laboratory Lessons.
Credits: 2. Grading scale: UG. Assessment: Home assignments well done and passed the laboratory lessons Contents: Home assignments and laboratory lessons

Admission

Admission requirements:

• Programming, Basic probability, Statistical methods, Mathematical analysis

The number of participants is limited to: No
The course overlaps following course/s: ETS060, ETS120

Reading list

• Nyberg, C, Compendium in simulation.
• Michal Pioro: Network Optimization Techniques, Chapter 18 in E. Serpedin, E., Chen, T., and Rajan, D. (eds.): Signal Processing, Communications, and Networking,. CRC Press, 2012, ISBN: 978-1-4398-5513-3.

Contact and other information

Course coordinator: Christian Nyberg, Christian.Nyberg@eit.lth.se
Course homepage: http://www.eit.lth.se/course/ets061