OPERATIONAL RESEARCH & BUSINESS INTELLIGENCE

Course Information
TitleΕΠΙΧΕΙΡΗΣΙΑΚΗ ΕΡΕΥΝΑ ΚΑΙ ΕΠΙΧΕΙΡΗΜΑΤΙΚΗ ΕΥΦΥΪΑ / OPERATIONAL RESEARCH & BUSINESS INTELLIGENCE
CodeNIS-08-02
FacultySciences
SchoolInformatics
Cycle / Level1st / Undergraduate
Teaching PeriodSpring
CoordinatorNikolaos Stylianou
CommonNo
StatusActive
Course ID40002978

Programme of Study: PPS-Tmīma Plīroforikīs (2019-sīmera)

Registered students: 85
OrientationAttendance TypeSemesterYearECTS
GENIKĪ KATEUTHYNSĪYPOCΗREŌTIKO KATA EPILOGĪ845
Class Information
Academic Year2020 – 2021
Class PeriodSpring
Instructors from Other Categories
Weekly Hours3
Class ID
600180195
Course Type 2016-2020
  • Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
  • Face to face
Digital Course Content
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
Prerequisites
General Prerequisites
Fundamental knowledge of probabilities and statistics
Learning Outcomes
Knowledge: Understanding basic concepts and design considerations in Business Intelligence development. Understanding optimization methods for business problems and machine learning methods used for business data. Skills: Solving optimization/operations research problems. Applying machine learning methods to business data. Acquaintance with the R programming environment using operations research and machine learning programming libraries to develop Business Intelligence applications.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Work in teams
  • Work in an international context
  • Work in an interdisciplinary team
  • Generate new research ideas
  • Design and manage projects
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Complex problems in management and business. Multiplicity of solutions, constraints, multiple criteria, dynamic environments. Examples of complex management problems in business. Classical methods of operations research: Mathematical modeling. Linear programming, transportation problems, assignment of works, problems in production lines, network analysis (deterministic and stochastic) in project management. Statistical methods: Exploratory data analysis. Statistical methods for quality control. Modeling and performance evaluation business with Data Envelopment Analysis (Data Envelopment Analysis-DEA). Optimization: optimization algorithms with a focus on probabilistic techniques (eg simulated annealing) with application to business/operational problems (inventory problems, transportation problems , scheduling , etc.). Case studies from companies with technical description and analysis of large amounts of data: Grouping customers based on buying habits, producing personalized recommendations, discovering correlations between products, sales forecasting , targeted advertising, investment strategies, discovery of fraud etc. Software (Lindo, R, PMML , etc.) with emphasis on free open source software. Case studies.
Keywords
Operations research, business intelligence
Educational Material Types
  • Notes
  • Slide presentations
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
Description
Software
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures39✓
Reading Assigment13✓
Written assigments56✓
Exams3✓
Literature study39✓
Total150
Student Assessment
Description
Homeworks, midterm exams and final exams
Student Assessment methods
  • Written Assignment (Summative)
Bibliography
Course Bibliography (Eudoxus)
Επιχειρησιακή έρευνα, μεθοδολογία, εφαρμογές και προβλήματα, πληροφοριακά συστήματα διοίκησης. Κώστογλου Βασίλειος. ΤΖΙΟΛΑ ΕΠΙΧΕΙΡΗΣΙΑΚΗ ΕΡΕΥΝΑ, ΕΦΑΡΜΟΓΕΣ ΣΤΗ ΣΗΜΕΡΙΝΗ ΕΠΙΧΕΙΡΗΣΗ. ΠΑΝΤΕΛΗΣ ΥΨΗΛΑΝΤΗΣ. ΠΡΟΠΟΜΠΟΣ SCHAUM'S ΕΠΙΧΕΙΡΗΣΙΑΚΗ ΕΡΕΥΝΑ. RICHARD BRONSON, GOVINDASAMI NAADIMUTHU, ΕΚΔΟΣΕΙΣ ΚΛΕΙΔΑΡΙΘΜΟΣ ΕΠΕ
Additional bibliography for study
Michalewicz, Z. et al. Adaptive business intelligence. Springer, 2006. Carlo Vercellis, Business Intelligence: Data Mining and Optimization for Decision Making, Wiley, 2009.
Last Update
03-12-2020