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)

Basedata

CP, Effort 6.0 CP = 180 h
Position of the semester 1 Sem. in WiSe/SuSe
Level [7] Master (Advanced)
Language [DE] German
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 WiSe
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.

Prüfungsleistung in den Modul-Lehrveranstaltungen, gewichtet mit den LP der belegten Modul-Lehrveranstaltung.

Contents

  • 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

Mit erfolgreichem Abschluss des Moduls werden die Studierenden in der Lage sein,
  • einzuschätzen, von welchem heuristischen Verfahren das jeweilige Problem gelöst werden kann.
  • zu bewerten, wie diese Verfahren im Anwendungskontext der Modellierung und zur Lösung von Logistikproblemen eingesetzt werden können.
  • zu evaluieren, wie sich naturanaloge Suchverfahren und maschinelles Lernen einsetzten lassen.
  • zu unterscheiden, welche Problemstellungen nicht mehr mit exakten Verfahren gelöst werden können, da sie eine mit der Problemgröße exponentiell anwachsende Zahl von Handlungsalternativen aufweisen.

Literature

Zu den in der Vorlesung behandelten Themen werden je ein bis zwei grundlegende Artikel angegeben.
Skriptum mit vertiefenden Literaturhinweisen wird verfügbar gemacht.

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