in the basis. 3. 3 surplus variables‚ 3 artificials‚ and 4 variables in the basis. 4. 2 surplus variables‚ 2 artificials‚ and 3 variables in the basis. 5. - 16. For obtaining the solution of dual of the following Linear Programming Problem‚ how many slack and/or surplus‚ and artificial variables are required? Maximize profit = $50X1 + $120X2 subject to 2X1 + 4X2 ≤ 80 3X1 + 1X2 ≤ 60 1. Two slack variables 3 2. Two surplus variables 3. Two
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the amount of tomato products to pack at this season. Tomato Products Whole Tomato Tomato Juice Tomato Paste Information: 1. Amount of Tomato: 3‚000‚000 pounds to be delivered. Tomato quality: 20% (grade A) × 3‚000‚000 = 600‚000 pounds 80% (grade B) × 3‚000‚000 = 2‚400‚000 pounds (provided by production manager) 2. Demand forecasts & selling prices (provided by sale manager): Products Demand Whole canned tomato no limitation Others Refer Exhibit 1 1 lbs. correction (800‚000/18)
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Full data could be found on BlackBoard System‚ in TopSliceDrivers.xls Source: Chapter 9‚ Case Study‚ Introduction to operations and supply chain management‚ 2nd ed.. Cecil C. Bozarth‚ Robert B. Handfield. Prentice Hall. Questions a) Plot the graph to show the sales. b) Select and develop one time series model for Jacob Lee.
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Distributed lag non-linear models in R: the package dlnm Antonio Gasparrini and Ben Armstrong London School of Hygiene and Tropical Medicine‚ UK dlnm version 1.6.4 ‚ 2012-08-22 Contents 1 Preamble 2 Installation and data 2.1 Installing the package dlnm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Distributed lag non-linear models 3.1 The issue . . . . . . . . . . .
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problems cannot have "greater than or equal to" (≥) constraints. Answer Selected Answer: True Correct Answer: False Question 2 0 out of 2 points Fractional relationships between variables are permitted in the standard form of a linear program. Answer Selected Answer: True Correct Answer: False Question 3 2 out of 2 points In a media selection problem‚ instead of having an objective of maximizing profit or minimizing cost‚ generally the objective is to maximize
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used by the company. Coca-Cola uses the forecasting technique of linear regression using a functional relationship between two or more correlated variables. The relationship is usually developed from observable data and plotted in a graph the two variables regress to form a straight line.The linear regression line is of the form Y=a+bX‚ where Y is the value of the dependent variable that we are solving for‚a is the Y-intercept‚ b is the slope‚ and X is the independent variable( In the time series
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that will fit into an 18-wheeler. a) Write an inequality to describe this region. b) Will the truck hold 71 refrigerators and 118 TVs? c) Will the truck hold 51 refrigerators and 176 TVs? Kindly consider the five given vocabulary words: Solid line Dashed line Parallel Linear inequality Test point In order to solve the problem‚ we need to be aware that the formula to solve any linear equation is y=mx+b. with the said‚ we can say that we have our y- intercept which is 330. (The ordered
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| MERTON TRUCK | Memorandum To: President From: Date: ------------------------------------------------- Re: Merton Truck Company Introduction In response to your report and request regarding Merton’s financial performance and product mix‚ I have met with your controller‚ sales manager and production manager‚ and have provided a solution that will improve the company in these two areas. Using a systematic approach‚ I was able to analyze the current machine hours‚ standard costs‚ and
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24 23 28 30 38 32 36 40 44 40 | a) Starting with week 2 and ending with week 11‚ forecast registrations using the naive forecasting method. [2] b) Starting with week 3 and ending with week 11‚ forecast registration using a two-week moving average. [3] c) Starting with week 5 and ending with week 11‚ forecast registrations using a four-week moving average. [3]
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. . . . . 2.8 Determinant . . . . . . . . . . . . . . . . . 2.9 Kronecker Products and the Vec Operator 3 Regression and Projection 3.1 Conditional Mean . . . . . 3.2 Regression Equation . . . 3.3 Conditional Variance . . . 3.4 Linear Regression . . . . . 3.5 Best Linear Predictor . . 3.6 Exercises . . . . . . . . . 4 Least Squares Estimation 4.1 Estimation . . . . . . . . 4.2 Least Squares . . . . . . . 4.3 Normal Regression Model 4.4 Model in Matrix Notation 4.5 Projection Matrices . . . . 4.6
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