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Advanced Operations Research

Module name (EN):
Name of module in study programme. It should be precise and clear.
Advanced Operations Research
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Supply Chain Management, Master, ASPO 01.04.2017
Module code: MASCM-240
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P420-0331, P420-0332, P620-0123
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
4VU (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
6
Semester: 2
Mandatory course: yes
Language of instruction:
English
Assessment:
Written exam and project work (120 minutes / Weighting 2:1 / Can be repeated semesterly)

[updated 13.09.2018]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

DFMM-MASCM-240 Management Sciences, Master, ASPO 01.10.2018 , semester 1, optional course
MASCM-240 (P420-0331, P420-0332, P620-0123) Supply Chain Management, Master, ASPO 01.04.2016 , semester 2, mandatory course
MASCM-240 (P420-0331, P420-0332, P620-0123) Supply Chain Management, Master, ASPO 01.04.2017 , semester 2, mandatory course

Suitable for exchange students (learning agreement)
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 180 hours (equivalent to 6 ECTS credits).
There are therefore 135 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Teresa Melo
Lecturer: Prof. Dr. Teresa Melo

[updated 01.04.2017]
Learning outcomes:
After having successfully completed this module, the student will:
- have obtained practice and experience in formulating realistic (integer) linear programming models,
- be aware of the applications of linear programming encountered in practice,
- have developed an appreciation for the diversity of problems that can be modeled as linear programs,
- be aware of the power and limitations of optimization methods,
- understand the concept of multicriteria decision-making and how it differs from situations and procedures involving a single criterion,
- be able to develop a goal programming model of a multiple criteria problem,
- be aware of major heuristic techniques and know when and how to apply them,
- be familiar with commercial software such as Excel Solver,
- be able to interpret the computer solution of a linear programming problem and perform a sensitivity analysis.


[updated 13.09.2018]
Module content:
1. Linear programming revisited:
 
- Building linear programming models
- Typical applications in production and distribution planning
- Economic interpretation of a solution
- Duality theory and sensitivity analysis
 
2. Multi-criteria decision problems:
 
- Motivation and examples of conflicting objectives
- Preemptive and non-preemptive goal programming
- The analytic hierarchy process (AHP)
 
3. Integer and mixed-integer linear programming:
 
- Formulation of optimization models with discrete decision variables
- Innovative uses of binary variables in model formulation
- Sample applications in logistics and supply chain planning
- The branch-and-bound technique
 
4. Metaheuristics:
 
- The nature of metaheuristics
- Tabu search
- Simulated annealing
- Genetic algorithms
 
5. Formulating and solving optimization models on a spreadsheet (Excel Solver)

[updated 13.09.2018]
Teaching methods/Media:
Lecture and discussion in a large group using transparencies (projector) and the blackboard (theory and examples).
  
The lecture will be supplemented by exercises. In order to support independent work a large number of exercise sheets covering the wide range topics in this module will be provided. Afterwards, the solutions will be discussed with the students (partly using optimization software).
 
Both the lecture notes and the exercise sheets will be available to students in electronic form.


[updated 13.09.2018]
Recommended or required reading:
Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., Cochran, J. J., Fry, M. J., Olhmann, J. W.: An Introduction to Management Science: Quantitative Approaches to Decision Making (14th edition). Cengage Learning, 2015
Hillier, F., Lieberman, G.: Introduction to Operations Research (9th edition). McGraw Hill Higher Education, 2010
Williams, H. P.: Model Building in Mathematical Programming (5th edition). Wiley, 2013
Winston, W. L.: Operations Research: Applications and Algorithms (4th edition). Cengage Learning, 2004


[updated 13.09.2018]
 
[Fri Dec 27 03:32:24 CET 2024, CKEY=saor, BKEY=scm3, CID=MASCM-240, LANGUAGE=en, DATE=27.12.2024]