-
Planning:
- Easy vs. Complex Optimization Problems
-
Problem Space Search:
- Best-First-Search and A*-Algorithm
-
Solution Space Search:
- Local Improvement Search,
- Variable Neighborhood Search
- Simulated Annealing,
- Tabu Search,
- Genetic Algorithms,
- Cooperative Simulated Annealing,
- Ants & Swarm Optimization
-
Knowledge Representation & Learning
- Symbolic approaches (Reasoning in Conceptual Structures) versus sub-symbolic approaches (Artificial Neural Networks)
- Knowledge Discovery & Data Mining
- Introduction to programming with Python and solver Gurobi
Module WIW-WIN-CIN-M-7
Computational Intelligence (M, 6.0 LP)
Module Identification
Module Number | Module Name | CP (Effort) |
---|---|---|
WIW-WIN-CIN-M-7 | Computational Intelligence | 6.0 CP (180 h) |
Basedata
CP, Effort | 6.0 CP = 180 h |
---|---|
Position of the semester | 1 Sem. in SuSe |
Level | [7] Master (Advanced) |
Language | [EN] English |
Module Manager | |
Lecturers | |
Area of study | [WIW-WIN] Business Information Systems and Operations Research |
Reference course of study | [WIW-88.789-SG] M.Sc. Business Studies with Technical Qualifications |
Livecycle-State | [NORM] Active |
Courses
Type/SWS | Course Number | Title | Choice in Module-Part | Presence-Time / Self-Study | SL | SL is required for exa. | PL | CP | Sem. | |
---|---|---|---|---|---|---|---|---|---|---|
2K | WIW-WIN-CIN-K-7 | Introduction to Computational Intelligence
| P | 30 h | 60 h | - | - | PL1 | 3.0 | SuSe |
2K | WIW-WIN-OLS-K-7 | Optimization of Logistics Systems
| P | 30 h | 60 h | - | - | PL1 | 3.0 | SuSe |
- About [WIW-WIN-CIN-K-7]: Title: "Introduction to Computational Intelligence"; Presence-Time: 30 h; Self-Study: 60 h
- About [WIW-WIN-OLS-K-7]: Title: "Optimization of Logistics Systems"; Presence-Time: 30 h; Self-Study: 60 h
Examination achievement PL1
- Form of examination: written exam (Klausur) (180 Min.)
- Examination Frequency: each semester
Evaluation of grades
The grade of the module examination is also the module grade.
Contents
-
Introduction to Logistics Optimization
- Demand Analysis
- Logistic demand analysis and data procurement.
- Basic concepts of demand forecasting (time series)
- Problems involved in getting other data (costs, maps, position of vehicles etc)
-
Logistic Network Design
- Location problems, single and multi-echelon transportation and location models.
- Inventory management. Basic concepts and introduction to single stocking and multi stocking inventory problems
- Warehouse design and management. Internal structure, storage infrastructures and mechanism, Warehouse design and dimensioning, Tactical and operational problems
-
Long vs. Short Haul Freight Transportation
- Vehicle Loading Issues
- Freight assignment, service network design, shipment consolidation, vehicle and driver assignment problems
- Distribution and collection of goods to local destinations.
- The Vehicle Routing Problem family
- Main solution techniques
Competencies / intended learning achievements
Upon successful completion of the module, the students will be able to:
- assess which heuristic method can be used to solve which kind of problems.
- evaluate how these methods can be used in the application context; in particular, for solving logistics problems.
- evaluate how nature-inspired search methods and machine learning can be used.
- distinguish which problems can no longer be solved using exact algorithms; in particular, due to the computational complexity of problems,
where the number of feasible solutions in a search space grows exponentially with the size of a given problem,
- model and solve optimization problems using the programming language Python and the solver "Gurobi Optimization".
Literature
Slides with in-depth references for further reading will be made available.
Slides with in-depth references for further reading will be made available.
Requirements for attendance (informal)
None
Requirements for attendance (formal)
None
References to Module / Module Number [WIW-WIN-CIN-M-7]
Course of Study | Section | Choice/Obligation |
---|---|---|
[MAT-88.276-SG] M.Sc. Business Mathematics | Computer Science and Computational Methods | [WP] Compulsory Elective |
Module-Pool | Name | |
[WIW-WIN-MPOOL-7] | Field of Specialization: Business Information Systems & OR |