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Determining the optimal solution to a real-world engineering problem

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Presentation on theme: "Determining the optimal solution to a real-world engineering problem"— Presentation transcript:

1 Determining the optimal solution to a real-world engineering problem
IEEE TEP Activity 3: It’s all about Optimization 2010 Spring Technical English Program Санкт-Петербург, Рассия May 15, 2010 Determining the optimal solution to a real-world engineering problem

2 IEEE TEP: All About Optimization
OPTIMIZE a quantity of interest MINIMIZE MAXIMIZE Time Risk Cost Effort Energy Efficiency Performance Return Revenue Profit

3 IEEE TEP: All About Optimization
Example problem You need to buy some filing cabinets. Two choices Cabinet X and Cabinet Y

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Example problem Cabinet X Cabinet Y Costs $10 per unit Costs $20 per unit Requires six (6) square feet of floor space Requires eight (8) square feet of floor space Holds eight (8) cubic feet of files Holds twelve (12) cubic feet of files

5 IEEE TEP: All About Optimization
Your role -- is to optimize!!! You have been given $140 for this purchase, though you don't have to spend that much. The office has room for no more than 72 square feet of cabinets. How many of which model should you buy, in order to maximize storage volume?

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Your approach?

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Variables of interest?

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Mathematical Model – Define Variables What are my variables of interest? x: number of model X cabinets purchased y: number of model Y cabinets purchased Also called DECISION VARIABLES.

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Mathematical Model – Define Variables What are my variables of interest? x: number of model X cabinets purchased y: number of model Y cabinets purchased Also called DECISION VARIABLES. Other quantities of interest based on the information provided?

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Mathematical Model – Define Variables What are my variables of interest? x: number of model X cabinets purchased y: number of model Y cabinets purchased Also called DECISION VARIABLES. Other quantities of interest based on the information provided: Cost: C = 10x+20y

11 IEEE TEP: All About Optimization
Mathematical Model – Define Variables What are my variables of interest? x: number of model X cabinets purchased y: number of model Y cabinets purchased Also called DECISION VARIABLES. Other quantities of interest based on the information provided: Cost: C = 10x+20y Space: S = 6x + 8y

12 IEEE TEP: All About Optimization
Mathematical Model – Define Variables What are my variables of interest? x: number of model X cabinets purchased y: number of model Y cabinets purchased Also called DECISION VARIABLES. Other quantities of interest based on the information provided: Cost: C = 10x+20y Space: S = 6x + 8y Volume: V = 8x + 12y

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Any Constraints?

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Mathematical Model – Any constraints? Are there any constraints on cost (money)?

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Mathematical Model – Any constraints? Are there any constraints on cost (money)? Yes – I only have $140 to purchase

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Mathematical Model – Any constraints? Are there any constraints on cost (money)? Yes – I only have $140 to purchase i.e. Cost: C < 140 i.e. 10x + 20y < 140 or y < – ( 1/x) + 7

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Mathematical Model – Any constraints? Are there any constraints on space?

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Mathematical Model – Any constraints? Are there any constraints on space? Yes - no more than 72 square feet of cabinets

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Mathematical Model – Any constraints? Are there any constraints on space? Yes - no more than 72 square feet of cabinets i.e. Space: S < 72 i.e. 6x + 8y < 72 or y < – ( 3/ 4)x + 9

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All Constraints together

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All Constraints together Are there any constraints on cost (money)? Yes – I only have $140 to purchase i.e. Cost: C < 140 i.e. 10x + 20y < 140 or y < – ( 1/x) + 7 Are there any constraints on space? Yes - no more than 72 square feet of cabinets i.e. Space: S < 72 i.e. 6x + 8y < 72 or y < – ( 3/ 4)x + 9

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What about volume?

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Mathematical Model – Any constraints? Are there any constraints on volume? Not really We are trying to MAXIMIZE VOLUME i.e. MAXIMIZE: Volume: V = 8x + 12y

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Mathematical Model – FINAL MODEL MAXIMIZE: VOLUME: V = 8x + 12y subject to: COST: y < – ( 1/x) + 7 and SPACE: y < – ( 3/ 4)x + 9

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Let’s look at the Feasible Region y 10 8 6 x = 0 4 2 x 2 4 8 10 12 6 14 y = 0

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Let’s look at the Feasible Region y 10 8 SPACE: y < – ( 3/ 4)x + 9 6 x = 0 4 2 x 2 4 8 10 12 6 14 y = 0

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Let’s look at the Feasible Region y 10 8 6 x = 0 4 COST: y < – ( 1/x) + 7 2 x 2 4 8 10 12 6 14 y = 0

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Let’s look at the Feasible Region y 10 8 y < – ( 3/ 4)x + 9 6 x = 0 4 2 y < – ( 1/x) + 7 x 2 4 8 10 12 6 14 y = 0

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Let’s Look at the Feasible Region y 10 8 y < – ( 3/ 4)x + 9 6 x = 0 4 2 y < – ( 1/x) + 7 x 2 4 8 10 12 6 14 y = 0

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Let’s Look at the Feasible Region y 10 Corner Points 8 y < – ( 3/ 4)x + 9 6 x = 0 4 2 y < – ( 1/x) + 7 x 2 4 8 10 12 6 14 y = 0

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Finally the solution Solutions are available on the corner of the feasible region

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Let’s look at the Feasible Region y 10 Corner Points 8 y < – ( 3/ 4)x + 9 6 x = 0 4 2 y < – ( 1/x) + 7 x 2 4 8 10 12 6 14 y = 0

33 IEEE TEP: All About Optimization
Evaluate the volume at corner points V = 8x + 12y At (0, 0) --> V = 0 At (8, 3) --> V = 100 At (0, 7) --> V = 84 At (12, 0) --> V = 84 Optimal solution is (8,3)

34 IEEE TEP: All About Optimization
Your approach Mathematical Model – Write the equations that represent the system/scenario Decision Variables Objective function Constraints 2) Feasible Region – Draw the feasible region on an x-y plane that will show the constraints 3) Solution evaluation – pick points to evaluate the objective function

35 IEEE TEP: All About Optimization
Today’s tasks Form pairs Read the problem provided to you Optimize!


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