Syllabus academic year 2010/2011
(Created 2010-07-25.)
Credits: 6. Grading scale: TH. Cycle: G2 (First Cycle). Main field: Technology. Language of instruction: The course will be given in Swedish. MIO310 overlaps following cours/es: MTT091. Compulsory for: I3. Optional for: M4lp. Course coordinator: Associate Professor Johan Marklund,, Production Management. Recommended prerequisits: MIO012/MIOA01 Managerial Economics, Basic Course, FMS035 Mathematical Statistics, Basic Course, FMA420 Linear Algebra (or equivalent). Assessment: Individual written exam. For a passing grade it is required (in addition to a passing written exam) that mandatory assignments are completed. The assessment of the assignments is based on the technical reports turned in for grading. All reports need passing grades in order for the student to pass the course. The assignments are solved independently in groups of 2-4 students. Each group turn in one report per assignment. Further information: For further information please contact the Department of Production Management. Home page:

The course has the overarching theme of optimization and simulation and aims to provide basic knowledge in deterministic and stochastic modelling of operational and managerial business problems.

Knowledge and understanding
For a passing grade the student must

For the optimization/mathematical programming section of the course this means:

For the discrete event simulation section of the course this means:

For the queuing section of the course this means:

Skills and abilities
For a passing grade the student must

be able to independently formulate, solve and interpret:

Furthermore, the student must be able to use established terms and unambiguously communicate problem formulation, solution and interpretation of optimization/math programming-, queuing- and discrete event simulation models. This ability is tested, for example, through three large group assignments that are solved independently by small groups of students and documented in detailed technical reports.

In the optimization/math programming section of the course, the main concern is methods for linear and integer programming. The focus is on formulation, and interpretation of the results obtained when the formulated problems are solved using a commercial software. The purpose of using mathematical models to analyse decision problems is to provide the decision maker with a solid foundation for his actions. However, in order to use information produced by the models in a correct way requires an understanding for the underlying mathematics. Consequently, a significant part of the course is devoted to clarify basic mathematical methods used within the optimization/mathematical programming area. The section’s mandatory project assignment is based on a case study describing a relatively complex business problem where an optimal decision is sought. By formulating and analysing an LP-model of the problem situation, a detailed decision proposal should be presented in a well structured technical report. Important assignment activities are: formulation of a relevant model, evaluation and optimization of the model using a commercial software, and interpretation and sensitivity analysis of the obtained results.

The simulation section of the course examines basic queuing theory as an analytical tool for analysing stochastic systems with relatively simple structure. To deal with more complex systems, a commercial software for discrete event simulation (Extend) is used. The developed models are used for analysing and improving production processes, and flow of materials and information. In order to arrive at a relevant simulation model, various types of stochastic events and processes must be characterised by appropriate distribution functions. Moreover, the output data from the simulation model must be analysed statistically in a correct way. Another important aspect is how to verify and validate the model to assure it is relevant and the results can be trusted. The section’s mandatory project assignment is structured around a case study dealing with the analysis of a small production system using simulation models. The objective is to provide an understanding for the strengths and weaknesses with discrete event simulation models as a tool for process analysis. Each project group reports their assignment work and the obtained results in a well structured technical report.

The connections and integration of the optimization and simulation sections of the course are illustrated through a project assignment. This assignment is structured around a described business case, which should be analysed using both linear programming and simulation. An important objective is to bring forward the strengths and weaknesses with the different approaches, and the value of using them in an integrated fashion to analyse a typical production planning problem. Also in this assignment, each project group is required to document their work and their conclusions in a well structured technical report.

Hillier F. S. and G. J. Lieberman, Introduction to Operations Research, 8th edition, McGraw-Hill, 2005. Customised edition for MIO310.
Laguna M. and J. Marklund, Business Process Modeling, Simulation and Design, Prentice Hall, 2005. (Includes a CD with the software Extend and other complementary material)
Course compendium