Op­tim­iz­a­tion, Re­gres­sion and Fore­cast­ing

Over­view

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


Module type: elective module


Semester: winter


Site: Cairo (GUC)


Language: English


Workload: 120 hours course attendance; 130 hours self-study


Credits points: 10


Recommended qualifications: none

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)