Module WIW-WIN-CIN-M-7

Computational Intelligence (M, 6.0 LP)

Module Number	Module Name	CP (Effort)
WIW-WIN-CIN-M-7	Computational Intelligence	6.0 CP (180 h)

CP, Effort	6.0 CP = 180 h
Position of the semester	1 Sem. in SuSe
Level	[7] Master (Advanced)
Language	[EN] English
Module Manager	Wendt, Oliver, Prof. Dr. (PROF \| DEPT: WIW)
Lecturers	Moeini, Mahdi, Dr. habil. (WMA \| DEPT: WIW) Vigo, Daniele, Prof. Dr. (EXT \| DEPT: WIW) Wendt, Oliver, Prof. Dr. (PROF \| DEPT: WIW)
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

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

The grade of the module examination is also the module grade.

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

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

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".

Slides with in-depth references for further reading will be made available.

Slides with in-depth references for further reading will be made available.

None

None

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