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Tue, 28 Aug 2002EChew: ISE330: Lecture 11 ISE330: Introduction to Operations Research Fall 2002 University of Southern California DJE-ISE Department.

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Presentation on theme: "Tue, 28 Aug 2002EChew: ISE330: Lecture 11 ISE330: Introduction to Operations Research Fall 2002 University of Southern California DJE-ISE Department."— Presentation transcript:

1 Tue, 28 Aug 2002EChew: ISE330: Lecture 11 ISE330: Introduction to Operations Research Fall 2002 University of Southern California DJE-ISE Department

2 Tue, 28 Aug 2002EChew: ISE330: Lecture 12 Handouts Today General Course Information Occupational Outlook Linear Algebra Quiz

3 Tue, 28 Aug 2002EChew: ISE330: Lecture 13 Administrative Details Classes: TR 2:00pm-3:20pm Location: KAP 148 Section: #35018D Instructor OH: T 3:30pm-5:30pm Help Sessions: Tan Xumei Website: http://www-classes.usc.edu/engr/ise/330

4 Tue, 28 Aug 2002EChew: ISE330: Lecture 14 Contact Information Instructor: Elaine Chew GER room 245 (tel: 213.8212414) Course Assistant: Tan Xumei GER room 309

5 Tue, 28 Aug 2002EChew: ISE330: Lecture 15 Textbook Text: Introduction to Operations Research, 7 th ed., by Hillier & Lieberman, McGrawhill, 2000. Reference: Schaum’s Outline Series: Operations Research by R.Bronson, McGraw-Hill, 1982.

6 Tue, 28 Aug 2002EChew: ISE330: Lecture 16 Course Outline Introduction to Operations Research (today) Module 1: (Aug 29 – Oct 3) Linear Programming Module 2: (Oct 8 – Oct 31) Network Optimization Module 3: (Nov 5–7) Game Theory Module 4: (Nov 12–14) Integer Programming Module 5: (Nov 19–21) Dynamic Programming Module 6: (Nov 26–Dec 5) Nonlinear Prog Review

7 Tue, 28 Aug 2002EChew: ISE330: Lecture 17 Grade Allocation Homework: 30% Quiz (Sep 12): 10% Midterm (Oct 24):25% Final Exam (cumulative) : 30%

8 Tue, 28 Aug 2002EChew: ISE330: Lecture 18 Pre-Requisite: MATH 225 Linear Algebra Differential Equations Pop Quiz

9 Tue, 28 Aug 2002EChew: ISE330: Lecture 19 History of OR Britain, WWII (1938). Multi-disciplinary team of scientists explore how to use radar information to deploy and use fighter planes. United States. Mathematical models (Search Theory) used to develop optimal air search patterns for anti-submarine tactics. Bombing Japan, Hokadate, Japan, from the Defense Visual Information Center downloaded from www.webshots.com

10 Tue, 28 Aug 2002EChew: ISE330: Lecture 110 Evolution of OR OR moves into industrial domain (1950’s), parallels computers’ growth as business planning/management tool. Focus on development of mathematical modeling techniques to improve or optimize real-world systems. 1951 computer core memory

11 Tue, 28 Aug 2002EChew: ISE330: Lecture 111 What is Operations Research? Before: application of mathematics and the scientific method to military operations Today: scientific approach to decision making. Seeks to determine best way to design and operate system, usually requiring allocation of scarce resources Winchester Warehouse, Winchester, KY photo from www.winchesterwarehouse.com

12 Tue, 28 Aug 2002EChew: ISE330: Lecture 112 Career Opportunities Accounting Actuarial Work Computer Services Corporate Planning Economic Analysis Financial Modeling Industrial Engineering Investment Analysis Logistics Manufacturing Services Management Consulting Management Training Market Research Operations Research Policy Planning Production Engineering Quantitative Methods Strategic Planning Systems Analysis Transportation

13 Tue, 28 Aug 2002EChew: ISE330: Lecture 113 The OR Methodology Formulate Problem Observe System Verify model and use it for Prediction Formulate a Mathematical Model of Problem Select a Suitable Alternative Present Results to Organization Implement and Evaluate Recommendations

14 Tue, 28 Aug 2002EChew: ISE330: Lecture 114 The OR Methodology Formulate Problem Observe System Verify model and use it for Prediction Formulate Mathematical Model, Gather Data Select a Suitable Alternative Present Results to Organization Implement and Evaluate Recommendations

15 Tue, 28 Aug 2002EChew: ISE330: Lecture 115 Mathematical Model An idealized representation of a real world problem Decision variables: (x 1,x 2,…,x n ) Objective function: z = c 1 x 1 +c 2 x 2 +…+c n x n Constraints: a 11 x 1 +a 13 x 3 ≤ b 1 Goal: Choose values of the decision variables that maximize the objective function subject to the constraints.


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