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Institute of Information Systems
Instituteof Information
Systems

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Computational Planning

Type and Deduction

compulsory module WI-CLCGP within MSc. studies in Information Systems
compulsory course within „Allgemeine Betriebswirtschaft und Methoden“ within MSc. studies BA
optional course in other MSc. studies


Requirements 
no requirements given.
For the lecture the number of participents is unlimited.

Language

German

Deduction Scope
6 CP
3 SWS (2 SWS of lecture, 1 SWS for tutorial)

Lecturers
   
Kai Brüssau

Course dates

 Thursday, 02:00 pm - 05:00 pm, WiWi 2098/2194

Registration

In order to participate in this course it is obligatory to register in STiNE during the STiNE registration periods.


Course Evaluation

Evaluation type: exam (90 minutes)

As soon as they are known the exact examination dates will be announced in STiNE.

Registration for the exams in STiNE well within the registration periods is mandatory (also for students who repeat the exam!).

In this course, business planning problems from different domains of supply chain management are examined while employing different solution approaches. Optimizing with exact mathematical methods, algorithms of graph theory and heuristics is at the forefront, nevertheless, forecasting problems are also considered.

The course goal is to analyze planning problems, to find suitable solution methods and to implement them prototypically. The implementation is done in the programming language Python. Basic knowledge in and an understanding of programming (not necessarily in Python) are advantageous for this course.

Outline

  • Introduction to computer-aided planning
  • Optimization methods of business planning
  • Demand planning and forecasting
    • Exponential smoothing techniques
    • Artificial neural networks
  • Material requirements planning
  • Problems in logistics
    • Route planning problems
    • Transport problems
    • Port logistics
  • Graph theory
    • Modeling of planning problems
    • Dijkstra algorithm
    • A*-Algorithm
  • Production planning
    • Local search methods for machine scheduling
    • Genetic algorithms
  • Wolfgang Domschke and Armin Scholl: Heuristische Verfahren, 2006
  • Ke-Lin Du, M. N. S. Swamy: Neural Networks and Statistical Learning
  • Johann Dréo, Patrick Siarry, Alain Pétrowski, Eric Taillard: Metaheuristics for Hard Optimization, 2006
  • Michel Gendreau,  Jean-Yves Potvin (Eds.): Handbook of Metaheuristics, 2010
  • Klaus Neusser: Zeitreihenanalyse in den Wirtschaftswissenschaften, 2011
  • Stefan Voß, Andreas Fink: Hybridizing Reactive Tabu Search with Simulated Annealing. In: Learning and Intelligent Optimization, Lecture Notes in Computer Science 2012, pp 509-512

Slides and other materials will be provided in STiNE .