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Unformatted text preview: IE426: Optimization Models and Applications: Lecture 19 Jeff Linderoth Department of Industrial and Systems Engineering Lehigh University November 14, 2006 Jeff Linderoth IE426:Lecture 19 Case Study Will be extra credit . You can increase your overall score by between two and five percentage points. Grading will be entirely subjective and will take into account the degree of difficulty Two Options 1 Create your own. (From your own experiences) 2 I will post two or three as I have time to create them The Rules 1 Work alone 2 Little (if any) help from me 3 Due: December 15. Jeff Linderoth IE426:Lecture 19 Case Study Ideas... Work Experience Problems? Assigning people to fraternity activities. Optimal gambling strategy Portfolio Selection / Real Portfolio Planning Create and Solve “real” large TSP or VRP’s Jeff Linderoth IE426:Lecture 19 Where are we? Five more (real) lectures... 3 Stochastic Programming. 2 Nonlinear Programming. Homework #4 – Last assignment – handed out 11/16. Due 11/28. NO CLASS on 11/30. Review Session 12/5... The Final Last day of class, December 7. Room: (Subject to change) – 355 Mohler Lab Hours: 4:30–7:30 Jeff Linderoth IE426:Lecture 19 WHAT IS SP? Jeff Linderoth IE426:Lecture 19 Etymology program: (3) An ordered list of events to take place or procedures to be followed; a schedule Late Latin programma, public notice, from Greek programma, programmat, from prographein, to write publicly stochastic: (1b) Involving chance or probability Greek stokhastikos, from stokhasts, diviner, from stokhazesthai, to guess at, from stokhos, aim, goal. Source: The American Heritage Dictionary of the English Language, Fourth Edition. Jeff Linderoth IE426:Lecture 19 Stochastic Programming A tool used in planning under uncertainty More specifically: Mathematical Programming , or Optimization , in which some of the parameters defining a problem instance are random , or uncertain In stochastic programming, we assume that a probability distribution for the uncertainty is known or can be approximated. We also assume that probabilities are independent of the decisions that are taken. Jeff Linderoth IE426:Lecture 19 Sources of Uncertainty Financial Market price movements: stock price, interest rates, foreign exchange rate Defaults by a business partner Operational Customer demands, Resource availability Travel times. Technology related Will a new technology be ready “in time” Jeff Linderoth IE426:Lecture 19 Sources of Uncertainty Market Related Shifts in tastes Competition What will your competitors strategy by next year? Political Outbreak of hostilities Acts of God Weather Equipment failure Jeff Linderoth IE426:Lecture 19 Probability Theory(?) This notion of having to know a probability distribution for the randomness is troubling, since in reality, very few people know items such as......
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This note was uploaded on 02/29/2008 for the course IE 426 taught by Professor Linderoth during the Spring '08 term at Lehigh University .
 Spring '08
 Linderoth
 Optimization

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