Module Handbook

  • Dynamischer Default-Fachbereich geändert auf WIW

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)


CP, Effort 6.0 CP = 180 h
Position of the semester 1 Sem. in SuSe
Level [7] Master (Advanced)
Language [EN] English
Module Manager
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
Presence-Time /
SL SL is
required for exa.
PL CP Sem.
Introduction to Computational Intelligence
P 30 h 60 h - - PL1 3.0 SuSe
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.


  • 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

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".


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)


Requirements for attendance (formal)


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