|Module code: DFMM-MASCM-240
4VU (4 hours per week)|
|Mandatory course: yes
|Language of instruction:
Written exam and project work (120 minutes / Weighting 2:1 / Can be repeated semesterly)
DFMM-MASCM-240 Management Sciences, Master, ASPO 01.10.2018
, semester 1, mandatory 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
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):
|Recommended as prerequisite for:
Prof. Dr. Teresa Melo
|Lecturer: Prof. Dr. Teresa Melo
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.
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
- The nature of metaheuristics
- Tabu search
- Simulated annealing
- Genetic algorithms
5. Formulating and solving optimization models on a spreadsheet (Excel Solver)
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.
|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