Design Optimization

Universidad EAFIT

Course Description

  • Course Name

    Design Optimization

  • Host University

    Universidad EAFIT

  • Location

    Medellín, Colombia

  • Area of Study

    Design, Engineering Science and Math, Industrial Engineering

  • Language Level

    Taught In English

    Hours & Credits

  • Contact Hours

    16
  • Recommended U.S. Semester Credits
    3
  • Recommended U.S. Quarter Units
    1
  • Overview

    2. JUSTIFICATION OF THE COURSE
    Limited resources and cost reduction is major goal in today's industry. Design optimization gives the student the necessary mathematical tools to achieve these purposes. Independent Study. Partnership Ruhr Universitaet Bochum, Germany. Limited resources and cost reduction is major goal in today's industry. Design optimization gives the student the necessary mathematical tools to achieve these
    purposes. Independent Study. Partnership Ruhr Universitaet Bochum, Germany.

    3. PURPOSE OR OVERALL OBJECTIVE
    3.1. To solve and evaluate optimization problems for complex technical systems. To solve and evaluate optimization problems for complex technical systems.
    3.2. To increase the social skills that are necessary to successfully complete a team project. To increase the social skills that are necessary to successfully complete a team project.
    3.3. To transfer theoretical knowledge gained from the lecture into practical solutions solved with software. To transfer theoretical knowledge gained from the lecture into practical solutions solved with software.

    4. BASIC SKILLS THE STUDENT WILL BE ABLE TO ACHIEVE

    5. ANALYTICAL DESCRIPTION OF CONTENTS: THEMES AND SUBTHEMES
    5.1. Numerical approaches, Linear optimization, Convex domains, partitioned domains Numerical approaches, Linear optimization, Convex domains, partitioned domains.
    5.2. Explicit design variables, synthesis and analysis, discrete and continuous variables, shape variables. Explicit design variables, synthesis and analysis, discrete and continuous variables, shape variables.
    5.3. Direct and indirect methods, Gradients, Hessian matrix, Kuhn-Trucker conditions. Direct and indirect methods, Gradients, Hessian matrix, Kuhn-Trucker conditions. 

    6. DIDACTIC STRATEGIES AND METHODOLOGIES

    7. RESOURCES

    8. CRITERIA AND POLICIES OF MONITORING AND ACADEMIC EVALUATION
    Article Submission. 100%
     

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