Julia Booth Case Study Angela Walker June‚ 06‚ 2013 Dr. Carl Tucker field In this case study‚ I must create a linear programming model for Julia that will help her to decide whether or not she need to lease a booth. The three products or variable we must consider for this booth are pizza‚ hotdogs‚ and barbecue sandwiches. In this model‚ x 1 equal the number of pizza slices Julia should purchase‚ x2 equals the number of hotdogs Julia should purchase‚ and x3 equal the number of barbecue sandwiches
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Julia Food Booth Introduction Julia is planning to lease a food booth outside the Tech Stadium at Home Football games to finance her last year education with all the games go sold out. The rent for the booth per game is $ 1000. Julia will sell slices of Cheese Pizza‚ Hot Dogs and Barbecue Sandwiches which are acclaimed to be the most popular so these are the three products she has chosen to sell at the home games football stadium. The rent for oven is $ 600 for six home games‚ which
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occurs when P=1250 h-1250 s=0 Profit =$2250 Since the booth costs $1000 to lease per game‚ and the oven is $100 per game‚ then Julia’s overall profit is‚ P = 2250 - 1100 = $1150 Hence it is worth leasing the booth. b) The shadow price for the budget is 1.50 and allowable increase is 138.4 Therefore‚ each dollar added to the budget will increase profit by $1.50 with a maximum increase of $138.40. Therefore the maximum amount Julia can borrow is $138.40 which will produce an additional profit
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Julia’s Food Booth (A) Formulate and solve an LP model for this case The objective here is to maximize the profit. Profit is calculated for each variable by subtracting cost from the selling price. The decision variables used are X1 for pizza slices‚ X2 for hotdog‚ and X3 for BBQ sandwich. X1 (pizza) X2 (hotdog) X3 (sandwich) Sales Price 1.50 1.50 2.25 Cost 0.75 0.45 0.90 Profit 0.75 1.05 1.35 *For Pizza Slice: Cost/Slice = $6/8 = $0.75 cost per slice Maximize Z =
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Duality in Linear Programming 4 In the preceding chapter on sensitivity analysis‚ we saw that the shadow-price interpretation of the optimal simplex multipliers is a very useful concept. First‚ these shadow prices give us directly the marginal worth of an additional unit of any of the resources. Second‚ when an activity is ‘‘priced out’’ using these shadow prices‚ the opportunity cost of allocating resources to that activity relative to other activities is determined. Duality in linear programming
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problems for Module 4 are: 2-24‚ 34‚ 36‚ 38 (you only have to do part A for these problems. You do not have to do the part B graphical solutions) and 3-10‚ 12 (parts B and C for Problem 12)‚ 28‚ 30. Please use Excel solver function. I posted an annotated solution to Problem 2-5. This problem is an examplar for both the chapter 2 and 3 problems. In this example I show how I typically set up a problem. First I set up the linear programming model and then develop a parallel set-up to use as input
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17th International Mining Congress and Exhibition of Turkey- IMCET 2001‚ © 2001‚ ISBN 975-395-417-4 Optimum Blending of Coal by Linear Programming for the Power Plant at Seyitömer Coal Mine K.Erarslan‚ H.Aykul‚ H.Akçakoca & N.Çetin Dumlupınar University‚ Department of Mining Engineering‚ 43100‚ Kütahya‚ Turkey ABSTRACT: In this study‚ a linear programming model is developed to determine the optimum coal blend in terms of quality and quantity. Coal with various features is mined from different
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Linear Programming History of linear programming goes back as far as 1940s. Main motivation for the need of linear programming goes back to the war time when they needed ways to solve many complex planning problems. The simplex method which is used to solve linear programming was developed by George B. Dantzig‚ in 1947. Dantzig‚ was one in who did a lot of work on linear programming‚ he was reconzied by several honours. Dantzig’s discovery was through his personal contribution‚ during WWII when Dantzig
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RESEARCH PAPER ON LINEAR PROGRAMMING Vikas Vasam ID: 100-11-5919 Faculty: Prof. Dr Goran Trajkovski CMP 561: Algorithm Analysis VIRGINIA INTERNATIONAL UNIVERSITY Introduction: One of the section of mathematical programming is linear programming. Methods and linear programming models are widely used in the optimization of processes in all sectors of the economy: the development of the production program of the company
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LINEAR PROGRAMMING DATE; 5 JUNE‚ 14 UNIVERSITY OF CENTRAL PUNJAB INTRODUCTION TO LINEAR PROGRAMMING Linear programming (LP; also called linear optimization) is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships. Linear programming is a special case of mathematical programming. It is a mathematical
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