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Intelligent Decision Making

Intelligent Decision Making. Host Institution – Elon University Team taught: Hollingsworth, Elon University Holliday, Western Carolina University Powell, Elon University Classrooms Eight students at Elon Three students at Western Carolina

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Intelligent Decision Making

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  1. Intelligent Decision Making • Host Institution – Elon University • Team taught: Hollingsworth, Elon University Holliday, Western Carolina University Powell, Elon University • Classrooms • Eight students at Elon • Three students at Western Carolina • Five students at Appalachian State • Ideal course that integrates OOD design, multiple programming languages, calculus, linear algebra and problem solving. • Code reuse, code enhancement, performance enhancement

  2. Communication Enhancements • Instant Messenger, Video Streaming, Pairwise Programming, email, Netmeeting, Skype. • Blackboard: Digital dropbox, syllabus, discussion boards, grades, class lectures. • Ten Homeworks: 45% Project: 25% • Communication Challenges: Staggered spring break, ASU snow days, Easter Holidays.

  3. Major Topics • A Mathematical Programming Language (AMPL) • Object wrapping with adapter design pattern • Condor-G for multiple and parallel job submission • Grid Services (Globus 3.2) • Introduction to Formulation and Classification of Optimization Models • Elements of Improving Search Based Optimization Algorithms • Formulation and Classification of Linear Problems • Simplex algorithm • Sensitivity analysis • Formulation of Unconstrained NLP • Golden Section and Gradient Search • Formulation of Constrained NLP • Penalty Methods • Formulation of Mixed Integer Problems

  4. Programming Projects • Used the Façade Design Pattern to wrap the AMPL commercial code and called it from a customized Java Swing GUI. • Developed three grid services: calendar, math, and optimization. Optimization service demonstrated with two separate client GUIs. • Coupled a third party nonlinear constrained optimizer to remote AMPL grid service. • Developed Condor-G scripts to submit and execute optimization from multiple starting points on Elon 8 node grid running Globus 3.2.

  5. Research Outcomes • Hollingsworth and Powell submitted paper, “Globus Grid Computing and AMPL: a Pragmatic Educational Environment for Real World, Engineering Design Optimization” to “The Fifth IASTED International Conference on Modeling, Simulation and Optimization”.Status: Under review. • Hollingsworth and Powell submitted paper, “Leveraging Grid Computing in an Intelligent Decision Making Course” to “The Consortium for Computing Sciences in Colleges Nineteenth Annual Southeastern Conference”. Status: Under review. • Identified AMPL Commercial Tool Capable for being extended to do multiple objective optimization. • Coupled multiple objective optimization package, NSGA2, with AMPL package. • Coupled simulated annealing optimization package, ASA, with AMPL package.

  6. Future Plans • Install the new release of Globus Toolkit, GT4 and investigate assortment of third party parallel job submission tools from LSF and Sun Microsystems. • Extend NSGA2 AMPL coupling to evaluate populations in parallel.

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