Optimization, Regression and Forecasting

Renewable Energy and Energy Efficiency - Management, Engineering and Application, Master (PO-2026)

Unique module numberETechn-9700-M
Module number / codeW-GUC-01
Module nameOptimization, Regression and Forecasting
Type of moduleElective module
Learning outcomes, acquired competencies and qualification goals

Fundamental theories and computational methodologies used in (computer aided) optimization analysis, productivity analysis, forecasting techniques, regression and correlation analysis, management, scheduling and aggregate planning

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

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
Course typesVLmP+Ü (4 SWS); VLmP+Ü (4 SWS)
Content

Linear and Non-Linear Optimization

  • Optimization analysis
  • Lagrangian function and Hessian matrix
  • Gradient descent method
  • Gauss-Newton method and the Levenberg-Marquardt method
  • Different non linear optimization methods

Production and Operations Management

  • 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
Title of coursesLinear and Non-Linear Optimization; Production and Operations Management
Teaching and learning methodslecture, exercise
Usabilitiy in other programs
Duration1 Semester
Frequency of module offerannually in winter semester
Teaching languageEnglish
Recommended (knowledge) prerequisites
Required prerequisites for participationnone
Student workload300 hours (120 h course attendance; 180 h self-study)
Required course worknone
Prerequisites for examination(s)none
Module examination(s)Examination P1: Linear and Non-Linear Optimization - midterm assignments (1/3), final written exam (90 min) (2/3)
Grade weighting P1: 50%
Examination P2: Production and Operations Management - midterm assignments (1/3), final written exam (90 min) (2/3)
Grade weighting P2: 50%
Credit points (ECTS)10 cp
Teaching unitElektrotechnik
Responsible personProf. Dahlhaus
Lecturer(s)Eberhard Roos
Media usedblack board and beamer, case studies in groups, lab experiments, measurements
Recommended literature
Comments

10 cp (5 cp - Linear and Non-Linear Optimization; 5 cp - Production and Operations Management)