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Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course ETS061F valid from Spring 2017

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General
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.
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.
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
Types of Instruction
  • Lectures
  • Exercises
Examination Formats
  • Written exam
  • Written assignments
  • A pass grade requires passed assignments and passed home exam
  • Failed, pass
Admission Requirements
Assumed Prior Knowledge
  • Programming, Basic probability, Statistical methods, Mathematical analysis.
Selection Criteria
Literature
  • Nyberg, C.: Kompendium i simulering..
Further Information
  • Course coordinator, professor Björn Landfeldt
Course code
  • ETS061F
Administrative Information
  • 2017-05-04
  • Professor Thomas Johansson

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