Decision Support Systems (ETF RIO SPO 51065)

General information

Module title

Decision Support Systems

Module code





Computing and Informatics





Module type












Module goal - Knowledge and skill to be achieved by students

  Objective of this course is to introduce students to the field of business decision making support. Course gives distinction between the IT business decision making support and other management functions such as management, leadership and planning. Also, students at the end of the course must understand the distinction between different levels of management and relevant business information through the levels. Accordingly, students acquire systematic knowledge about the classes of information systems for support to different management levels decision-making. Course also covers different models of decision making, different classes of decision support systems, as well as different models for decision support systems development.


  1. MODELS AND METHODS FOR DECISION MAKING SUPPORT: models for “capacitated facility location” problems, models for the “uncapacitated facility location” problems, ranch problem, coloring problem, clique problem, set partition problem, strong and weak models, methods for forming strong models, implications to the problem solution, branching and restriction, models with exponential number of variables, column separation and generation, maximum flow problems, shortest path, covering, minimal skeleton pairing, traveling salesman, use of methods in planning, formulation of different schedules <br>
2. SOFTWARE MODELS OF DECISION SUPPORT SYSTEMS: levels, models and processes of business decision making, information systems classes for support to different management levels of decision making, decision support systems architecture, model driven decision support systems, document driven decision support systems, communication driven / group decision support systems, models of group decision making; systems for electronic meetings support, knowledge driven decision support systems, development process of decision support system <br>
3. BUSINESS INTELLIGENCE: knowledge acquisition, information integration (combining data from different sources, diverse applications and legacy data formats, connection of heterogeneous business systems and data warehouses, presence of lexical and semantical incompatibilities, use of mediators and metadata management for connecting heterogeneous and autonomous data sources), business analysis and visualization, metadata repository <br>


Recommended1. Notes and slides from lectures (See Faculty WEB Site) <br>
2. S.MARTELLO, P.TOTH, Knapsack Problems: Algorithms and Computer Implementations, Wiley, 1990. <br>
3. Laudon C.K., and Laudon J.P., Essentials of Management Information Systems: Organization and Technology in the Networked Enterprise, Prentice Hall, 2001. <br>
4. Turban E., and Aronson J.E., Decision Support Systems and Intelligent Systems, Prentice Hall, 1998. <br>
Additional1.Moss L.T., and Atre S., Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-Support Applications, Addison-Wesley, 2003. <br>
2.Chakrabarti S., Mining the Web: Discovering Knowledge from Hypertext Data, Elsevier Science, 2003. <br>
3.Licker S.P., Management Information Systems; A strategic Leadership Approach, 3.The Dryden Press Harcourt Brace College Publishers. <br>

Didactic methods

  Through lectures, students will learn about the theory, tasks and applicative examples within thematic units. Lectures consist of theoretical part, presentational descriptive examples, genesis and resolution of specific tasks. In this way, students will have basis for appliance of skilled material in engineering applications. Additional examples and exam tasks are discussed and solved during the laboratory exercises. Laboratory practice and home assignments will enable students of continuous work and their knowledge verification. <br>


  During the course students will collect points according to the following system: <br>
- Attending lectures, exercises and tutorial classes: 10 points, student with more then three absences from lectures, exercises and/or tutorials can not achieve these points; <br>
- Home assignments: maximum of 10 points, assuming solving 5 to 10 assignments evenly distributed throughout the semester; <br>
- Partial exams: two written partial exams, maximum of 20 points for each positively evaluated partial exam; <br>
Student who during the semester achieved less than 20 points must re-enroll this course. <br>
Student who during the semester achieved 40 or more points will access to final oral exam, the exam consists of discussing the partial exams tasks, home assignments and answers to simple questions related to course topics. <br>
Final oral exam provides maximum of 40 points. To achieve a positive final grade, students in this exam must achieve a minimum of 20 points. Students who do not achieve this minimum will access to makeup oral exam. <br>
Student who during the semester achieved 20 or more points, and less than 40 points will access to makeup exam. Makeup exam is structured as follows: <br>
- Written part structured in the same way as a partial written exam, during which students solve problems in topics they failed on partial exams (achieved less then 10 points), <br>
- Oral part structured in the same way as a final oral exam. <br>
Only students who, after passing the written part of the makeup exam managed to achieve a total score of 40 or more points, can access to oral makeup exam, where the score consists of points achieved through attending classes, home assignments, passing partial exams and passing the written part of makeup exam. <br>
Oral makeup exam provides maximum of 40 points. To achieve a positive final grade, students in this exam must achieve a minimum of 20 points. Students who do not achieve this minimum must re-enroll this course. <br>

Aditional notes