A. Formulate a linear programming model for Julia that will help you to advise her if she should lease the booth. Let‚ X1 =No. of pizza slices‚ X2 =No. of hot dogs‚ X3 = No. of barbeque sandwiches * Objective function co-efficient: The objective is to maximize total profit. Profit is calculated for each variable by subtracting cost from the selling price. For Pizza slice‚ Cost/slice=$4.5/6=$0.75 | X1 | X2 | X3 | SP | $1.50 | $1.60 | $2.25 | -Cost | 0.75 | $0.50 | $1.00 |
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Scottsville Textile Mill Case MGM 350 Production Schedule and loom assignment Decision Variable X1: Yards of fabric 1 on dobbie loom X2: Yards of fabric 2 on dobbie loom X3: Yards of fabric 3 on dobbie loom X4: Yards of fabric 4 on dobbie loom X5: Yards of fabric 5 on dobbie loom X6: Yards of fabric 3 on regular loom X7: Yards of fabric 4 on regular loom X8: Yards of fabric 5 on regular loom X9: Yards of fabric 1 purchased X10: Yards of fabric 2 purchased
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CHAPTER 1: INTRODUCTION TO OPERATIONS RESEARCH (OR) 1.1 INTRODUCTION Operation Research‚ an approach to decision making based on the scientific method‚ makes extensive use of quantitative approaches to decision making. In addition to operation research‚ two other widely known and accepted names are management sciences and decision science interchangeably. The scientific management revolution of the early 1900s‚ initiated by Frederic W. Taylor‚ provided the foundation for the use of quantitative
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Sample Paper for IEEE Sponsored Conferences & Symposia Derong Liu‚ Fellow‚ IEEE‚ and MengChu Zhou‚ Fellow‚ IEEE Abstract— The abstract goes here. What you need to do is to insert your abstract here. Please try to make it less than 150 words. We suggest that you read this document carefully before you begin preparing your manuscript. IEEE does not want conference papers to have keywords so you should remove your keyword list. Also‚ at this time‚ IEEE only has some general guidelines about
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Chapter 2 An Introduction to Linear Programming 18. a. Max 4A + 1B + 0S1 + 0S2 + 0S3 s.t. 10A + 2B + 1S1 = 30 3A + 2B + 1S2 = 12 2A + 2B + 1S3 = 10 A‚ B‚ S1‚ S2‚ S3 0 b. c. S1 = 0‚ S2 = 0‚ S3 = 4/7 23. a. Let E = number of units of the EZ-Rider produced L = number of units of the Lady-Sport produced Max 2400E + 1800L s.t. 6E + 3L 2100 Engine time L 280 Lady-Sport maximum
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Case Problem: Textile Mill Scheduling Assuming‚ X1 = Yards of fabric 1 purchased X2 = Yards of fabric 1 on dobbie looms X3 = Yards of fabric 2 purchased X4 = Yards of fabric 2 on dobbie looms X5 = Yards of fabric 3 purchased X61 = Yards of fabric 3 on dobbie looms X62 = Yards of fabric 3 on regular looms X7 = Yards of fabric 4 purchased X81 = Yards of fabric 4 on dobbie looms X82 = Yards of fabric 4 on regular looms X9 = Yards of fabric
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Sensitivity Analysis Source: Introduction to Management Science 10 e‚ Anderson Sweeney Williams Example 1 Max s.t. 5x1 + 7x2 x1 < 6 2x1 + 3x2 < 19 x1 + x2 < 8 x1‚ x2 > 0 x2 8 7 6 5 4 3 2 1 x1 + x2 < 8 Max 5x1 + 7x2 x1 < 6 Optimal: x1 = 5‚ x2 = 3‚ z = 46 2x1 + 3x2 < 19 x1 1 2 3 4 5 6 7 8 9 10 x2 8 7 6 5 4 3 2 1 5 5 Feasible Region 1 1 1 2 3 4 4 4 3 3 2 2 5 6 7 8 9 10 x1 Example 1 • Range of Optimality for c1 The slope of the objective
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An Example of Two Phase Simplex Method Consider the following LP problem. max z = 2x1 + 3x2 + x3 s.t. x1 + x2 + x3 · 40 2x1 + x2 ¡ x3 ¸ 10 ¡x2 + x3 ¸ 10 x1; x2; x3 ¸ 0 It can be transformed into the standard form by introducing 3 slack variables x4‚ x5 and x6. max z = 2x1 + 3x2 + x3 s.t. x1 + x2 + x3 + x4 = 40 2x1 + x2 ¡ x3 ¡ x5 = 10 ¡x2 + x3 ¡ x6 = 10 x1; x2; x3; x4; x5; x6 ¸ 0 There is no obvious initial basic feasible solution‚ and it is not even known whether there exists one
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Yu Liu 4.17.2012 Why? What? How? USCT‚ Dept. EEIS‚ Yu Liu 3 1. Image Matching in a common and important problem in computer vision. 2. Application in: ◦ ◦ ◦ ◦ ◦ Object or scene recognition 3D reconstruction Stereo correspondence Motion tracking Image Searching USCT‚ Dept. EEIS‚ Yu Liu 4 3. Traditional method: simple corner detectors is not stable when you have images of different scales and rotations. 4. We need a method can solve: ◦ ◦ ◦ ◦ ◦ ◦ Different
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This case presents some of the basic concepts of aggregate plan-ning by the transportation method. The case involves solving arather complex set of transportation problems. Four different con-figurations of operating plants have to be tested. The solutions‚ al-though requiring relatively few iterations to optimality‚ involvedegeneracy if solved manually. The costs are The lowest weekly total cost‚ operating plants 1 and 3 with 2closed‚ is $217‚430. This is $3‚300 per week ($171‚600 per year)or
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