Course syllabus OVA - Operational Research (FBE - SS 2019/2020)

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Course code: OVA
Course title in language of instruction: Operational Research
Course title in Czech: Operational Research
Course title in English: Operational Research
Mode of completion and number of credits: Exam (6 credits)
(1 ECTS credit = 28 hours of workload)
Mode of delivery/Timetabled classes: full-time, 2/2 (hours of lectures per week / hours of seminars per week)
part-time, 16/0 (lectures per period / seminars per period)
Language of instruction: English
Level of course: master continuing
Semester: SS 2019/2020
Name of lecturer: Ing. Martina Čampulová, Ph.D. (examiner, instructor, lecturer)
doc. Ing. Mgr. Jitka Janová, Ph.D. (supervisor)
Prerequisites: Final Bachelor Exam
Aims of the course:
The aim of the course is to present advanced mathematical methods of operational research which can be directly used as a managerial decision making support tool. This course results from Economic Mathematical Methods and continues with more sophisticated parts of Operations Research. On the end of this course students should have general knowledge of intermediate Operations research methods. Part of this course is also focused on practical applications and use of specific software.
Course contents:
1.Determination, characterization and possibilities of application of OR (allowance 2/0)
2.Mathematical programming--linear, non-linear stochastic and dynamic (allowance 10/10)
a.Linear programming
b.Convex programming
c.Quadratic programming
d.Stochastic programming
e.Dynamic programming

3.Renewal theory (allowance 4/4)
a.Recovery of outdating equipment -- deterministic model
b.Recovery of failing products -- stochastic model

4.Inventory management (allowance 2/4)
a.Deterministic models of inventory
b.Stochastic models of inventory

5.Queueing models (allowance 4/4)
a.Exponential queueing models&emdash;M/M/1 and M/M/c
b.Optimization in the queueing models

6.Simulation models (allowance 2/2)
7.Project management (allowance 4/4)
b.CPM and PERT methods
c.Cost and probability costing

Learning outcomes and competences:
Generic competences:
-Ability to apply knowledge
-Ability to make decisions
-Ability to solve problems
-Basic computing skills

Specific competences:
-Student can explain the principles of advanced operations research techniques
-Student can choose appropriate and effective method of decission problem solving
-Student is able to build and solve appropriate optimization model
-Student is able to describe the solution process of decision making problem

Type of course unit: required
Year of study: Not applicable - the subject could be chosen at anytime during the course of the programme.
Work placement: There is no compulsory work placement in the course unit.
Recommended study modules: -
Learning activities and study load (hours of study load):
Type of teaching methodDaily attendanceCombined form
Direct teaching
     lecture28 h0 h
     practice28 h0 h
     preparation for exam62 h0 h
     preparation for regular assessment20 h0 h
     preparation for regular testing30 h0 h
Total168 h0 h
Assessment methods:
Written test (including theoretical and practical part) at the end of the course. Additional requirements (attendance, seminar work, etc..) are not set. Students can take on the exam calculator and official formulas (no other materials are permitted). To pass the exam, students must obtain at least 50% of available points.
Recommended reading:
TypeAuthorTitlePublished inPublisherYearISBN
RQANDERSON, D R. et al.An introduction to management science: quantitative approaches to decision makingLondonCengage Learning978-1-84480-595-2
RQHILLIER, F S.Introduction to management science: a modeling and case studies approach with spreadsheetsBoston [u.a.McGraw-Hill/Irwin2008978-007-125927-9
RQOptimization in operations researchUpper Saddle River, N.J.Prentice Hall0-02-398415-5
RQSTEVENSON, W J. -- OZGUR, C.Introduction to management science with spreadsheetsBostonMcGraw-Hill/Irwin2007978-0-07-299066-9


Last modification made by Ing. Jiří Gruber on 12/05/2019.

Type of output: