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Innovation and Problem Solving with an emphasis on TRIZ Tools

ME59700

Fall 2017

Days/Time: TTh / 4:30-5:45 pm
Credit Hours: 3

Learning Objective:
Familiarize students with methods that are easily accessible but rigorous for formulation and solution of hard (PSPACE or harder) design optimization problems to rapidly create products, services, systems or solutions to specific problems whose performance is insensitive to uncertainties in components and operating environment. It is built around a core of TRIZ ideas developed by Genrikh Altshuller, but integrates cutting edge discoveries and practice from a variety of sources: mathematical problem solving, optimization and decision theory, marketing, finance, and management research, and includes the following:
Identifying the market and value proposition
Rigorous and accessible formulation
Solution via reducing the search space
Eliminating tradeoffs to reduce dimension of optimization problems
Execution through developing strategies for experiment, construction and monetization

Description:
While creativity and innovation are popular as buzzwords, they are seldom used in any exact sense. In a decision-theoretic sense, the complexity of a problem is dependent upon the size of the space of decisions in which an optimum is sought. Building an anti-gravity device for example would require billions of experiments, while building a non-standard size bolt requires only interpolation between existing designs, i.e., one experiment. Both the preceding, are in some sense innovations, but the value and difficulty in the former far exceeds the latter. MATLAB functions needed for course project will be described in class and some core MATLAB/SIMULINK code will be provided for system analysis.
SP2018 Syllabus
Also check out the Course Preview Video
Open to undergraduates satisfying prerequisites.

Topics Covered:
Identifying markets, estimating value proposition, rigorous and accessible formulation of design problems, solution via reducing the search space, eliminating design trade-offs to reduce dimension of optimization problems, execution through developing strategies for experiment, construction and monetization.

Prerequisites:
Linear algebra, differential equations (MATH262) and probability (MATH250) or the consent of the instructor.

Web Address:
https://mycourses.purdue.edu

Homework:
Course evaluation is based on weekly quizzes to test understanding of subject material (50%) and project updates to test ability to apply class material to engineering problems chosen by the student (50%). Bonus points will be awarded for posting answers to class questions on the course blog.

Textbooks:
None. Class notes, and references supplied on Blackboard.

Computer Requirements:
Need to be able to run MATLAB/SIMULINK for quizzes and project updates.

ProEd Minimum Requirements: view

Tuition & Fees: view

SEMESTERS

Fall 2017
Spring 2018

CURRENT INSTRUCTOR(S)

Kartik Ariyur

Phone
765-494-8613

Email
kariyur@purdue.edu

Office
Purdue University
Mechanical Engineering Building
585 Purdue Mall
West Lafayette, IN 47907-2088

Instructor HomePage

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