Optimization, Regression and Forecasting
Courses
Learning Outcome
After the successful participation in the course Linear and Non-Linear Optimization the students are able to
- Knowledge and Understanding:
- identify the objective function, the holonomic and nonholonomic constrains
- form the Lagrangian function and solve for the optimal variables and Lagrange multipliers
- form the Hessian matrix and analyze the second order sufficiency conditions of the optimization problem
- compare between optimization techniques such as the gradient descent method, Gauss-Newton method and the Levenberg-Marquardt method
- Intellectual Skills:
- formulation of the optimization problem through the ability to distinguish between the objective functions and the constrains
- ability to solve the optimization numerically
- ability to select the appropriate optimization problem based on the constrains and the dynamics of the process
Content
- Optimization analysis
- Lagrangian function and Hessian matrix
- Gradient descent method
- Gauss-Newton method and the Levenberg-Marquardt method
- Different non linear optimization methods
Details
- Lecturer: Eberhard Roos
- Teaching method: lecture, exercise
- SWS: 4
- Credit points: 5
- Examination: midterm assignments (1/3); final exam (2/3)
Learning Outcome
After the successful participation in the course Production and Operations Management the students are able to
- Knowledge and Understanding:
- define productivity analysis and its application
- describe different forecasting techniques
- describe regression techniques
- describe inventory techniques
- explain aggregate planning
- define project scheduling
- Professional and Practical skills:
- predict new demands of the globally competitive business environment emphasize the importance of change, facilitation of learning, cross-functional teamwork, knowledge capture, and analysis in manufacturing organizations
- submit a course project, in which the project process of initiating, planning, executing, controlling and closing the project is applied through case studies
- Intellectual Skills:
- develop an understanding of the strategic importance of manufacturing systems, production and operations systems
- recognize the relationship between manufacturing and related service providers and other business functions, such as human resources, purchasing, marketing, finance, etc.
- calculate forecasts using different techniques
- apply qualitative and quantitative methods of inventory models
- apply proactive and reactive planning strategies
- calculating the timing of the use of different resources in an organization
- General and Transferrable skills:
- employ critical thinking to solve problems in area of quality control
- practice independent learning required to build up knowledge base
- work in teams
Content
- Productivity analysis
- Forecasting techniques
- Regression and correlation analysis
- Inventory
- Management
- Aggregate planning
- Materials requirements planning (MRP)
- Scheduling
- It also allows more emphasis on computer solutions with excel spreadsheets
Details
- Lecturer: Eberhard Roos
- Teaching method: lecture, exercise
- SWS: 4
- Credit points: 5
- Examination: midterm assignments (1/3); final exam (2/3)