Operations research

Course description

Algorithms, time and memory complexity, data structures. Graph theory (representation, selected graph properties, basic graph algorithms).

Introduction to operations research and optimization. Optimization task (formulation, objective function, and set of solutions). Linear and integer programming (simplex method, selected known problems). Network analysis (maximal flow, minimal cost, shortest path). Nonlinear optimization (gradient and Newton method, constraint optimization). Combinatorial optimization. Game theory. Markov chains (classification of states, ergodicity). Time series. Queuing theory. Heuristic optimization techniques. Measuring QoE and user opinion. Basics of business intelligence in TC. Selected optimization problems in telecommunications (topology design, optimal resource assignment, optimal routing, yield management).

Course is carried out on study programme

Objectives and competences

Basic understanding of optimization problem formulation and solving. Understanding the relationship between problem formulation and computer aided solving. Recognizing the optimization problem type related to existing computer solvers. Understanding end user satisfaction together with business model in term of optimization objective function. Getting to know measuring and modeling users. Getting to know user experiments.

Learning and teaching methods

Auditorium lectures, consultations, supervised project work

Intended learning outcomes

Develop and apply the conceptual basis and the practical skills in problem solving. Formulate, recognize and solve complex optimization problems. Select the appropriate optimization problem formulation and the select the optimal existing computer tool to solve it.

Reference nosilca

  1. ASLAN OĞUZ, Evin, STRLE Gregor, KOŠIR Andrej. Multimedia ad exposure scale: measuring short-term impact of online ad exposure, Multimed Tools Appl 83, (2023). https://doi.org/10.1007/s11042-023-14401-5.
  2. Droftina U, Šular M, Košir A (2015) A diffusion model for churn prediction based on sociometric theory. Advances in data analysis and classification, vol. 9, iss. 3, pp 341-365
  3. Vodlan T, Tkalčič N, Košir A (2014) The impact of hesitation, a social signal, on a user’s quality of experience in multimedia content retrieval. Multimedia Tools and Applications, vol. 74, no. 17, pp 6871-6896
  4. Odić A, Tkalčič M, Tasič J, Košir A (2013) Predicting and detecting the relevant contextual information in a movie-recommender system. Interact. comput., vol. 25, no. 1, pp 74-90
  5. Tkalčič M, Odić A, Košir A (2013) The impact of weak ground truth and facial expressiveness on affect detection accuracy from time-continuous videos of facial expressions. Information sciences, vol. 249, pp 13-23

Study materials

[1] P. Saengudomlert: Optimization for Communications and Networks, CRC Press, 2012.

[2] A. Dutta, H. Schulzrinne: Mobility Protocols and Handover Optimization: Design, Evaluation and Application, John Wiley & Sons, 2014.

[3] R. Srikant, L. Ying: Communication Networks: An Optimization, Control and Stochastic Networks Perspective, Cambridge university press, 2014.

[4] M. W. Carter, C. C. Price: Operations Research, A Practical Introduction, CRC Press, 2000.

[5]D. C. Montgomery. Design and Analysis of Experiments. John Wiley & Sons, 2008.

[6] M. G.C. Resende and P. Pardalos: Handbook of Optimization in Telecommunications, Springer, 2006.

[7] A. Košir: Operacijske raziskave v telekomunikacijah, Založba FE in FRI, 2013.

Bodi na tekočem

Univerza v Ljubljani, Fakulteta za elektrotehniko, Tržaška cesta 25, 1000 Ljubljana

E:  dekanat@fe.uni-lj.si T:  01 4768 411