DATA SCIENCE IN LOGISTICS MANAGEMENT
| Title | ΕΠΙΣΤΗΜΗ ΤΩΝ ΔΕΔΟΜΕΝΩΝ ΣΤΗ ΔΙΟΙΚΗΣΗ ΕΦΟΔΙΑΣΤΙΚΗΣ / DATA SCIENCE IN LOGISTICS MANAGEMENT |
| Code | ΕΔΕ11 |
| Faculty | Social and Economic Sciences |
| School | Economics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Alexandros Diamantidis |
| Common | No |
| Status | Active |
| Course ID | 600026525 |
Programme of Study: Data Science in Economics and Finance
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Core | Elective Courses | 1 | 1 | 6 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Faculty Instructors | |
| Class ID | 600273582
|
Course Type 2021
Specialization / Direction
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600273582
- eLearning (Moodle):
Language of Instruction
- Greek (Instruction, Examination)
Learning Outcomes
Upon completion of the course, students will be able to:
•understand the basic problems that supply chain managers face in their daily work
•apply data science tools to solve problems in the context of supply chain management, such as product distribution, fleet routing, and supply chain network design and optimization
•utilize tools and programming languages such as R and Python to identify data-driven solutions
•analyze case studies involving real-world applications of Data Science in Supply Chain Management
•measure and compare the effectiveness of operational units in a supply chain network
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 in an international context
- Work in an interdisciplinary team
- Advance free, creative and causative thinking
Course Content (Syllabus)
The course presents the basic principles of Supply Chain Management, the value of data for making relevant decisions, as well as the sources and types of relevant data. In addition, basic algorithms are presented for solving problems of measuring and comparing the effectiveness of units, product distribution, fleet routing, and supply chain network design and optimization, as well as machine learning techniques that can provide effective solutions to the above problems by utilizing quantitative data. The above methods are implemented using appropriate tools in programming languages such as R and Python.
Lectures:
1.Introduction: Introductory concepts of Logistics and Supply Chain
2.Core Problems in Supply Chain Management: Traveling Salesman Problem (TSP), Vehicle Routing Problem (VRP), and Network Design. Popular solution algorithms for large-scale problems (e.g., Further Insertion, Large Neighborhood Search) and their implementation in Python & R
3.Solving Key Supply Chain Management Problems Using Machine Learning Algorithms: e.g., Self-Organizing Map (SOM), Policy-Based Reinforcement Learning, Supervised Learning Approaches with Neural Networks
4.Lab Session: Applications of machine learning methods in Python & R
5.Lab Session: Applications of machine learning methods in Python & R
6.Lab Session: Applications of machine learning methods in Python & R
7. Introduction to basic concepts of linear programming and modeling
8. Modeling of supply chain networks using mixed integer linear programming
9. Design and optimization of supply chain networks using the R language
10. Measuring unit efficiency using the DEA (Data Envelopment Analysis) method, modeling using linear programming, and solving using the R language
11.Lab Session: Applications of machine learning methods in Python & R
12.Lab Session: Applications of machine learning methods in Python & R
13.Lab Session: Applications of machine learning methods in Python & R
Keywords
Supply Chain Management – Machine Learning – Vehicle Routing Problem (VRP) – Data-Driven Solutions – Python & R – Optimization Algorithms-measuring and comparing the effectiveness of units- optimization of Supply chain_
Educational Material Types
- Notes
- Slide presentations
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | ||||
| Reading Assigment | 65 | ||||
| Written assigments | 76 | ||||
| Total | 180 |
Student Assessment
Description
Written Exam (Extended Response Questions) – 50%, Written Assignment – 50%
Student Assessment methods
- Written Exam with Extended Answer Questions (Summative)
- Written Assignment (Summative)
Bibliography
Additional bibliography for study
Cooper, W.W., Seiford, L.M., & Zhu, J. (2011),Handbook on Data Envelopment Analysis (2nd Edition),Springer
Pistikopoulos, E.N., M.C. Georgiadis and V. Dua (2010), Process Systems Engineering, Supply Chain Optimization, Wiley-VCH, ISBN: 978-3-527-31693-9.
James G., Witten D., Hastie T., Tibshirani R. (2021), An Introduction to Statistical Learning with applications in R (2nd edition), Springer.
James G., Witten D., Hastie T., Tibshirani R., Taylor J. (2023), An Introduction to Statistical Learning with applications in Python.Springer.
Last Update
04-09-2025
