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CVEN 5393 Spring 2013 Homework 8 Solution. Revelle , Whitlatch and Wright Civil and Environmental Systems Engineering – 2 nd Edition Problem 5.3. a) Plot feasible region in decision space . 4x 1 - 12x 2 <= -6. -4x 1 + 6x 2 <= 12. 4x 1 + 2x 2 >= 8. x 1 + x 2 <= 9. 4x 2 <= 16.

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cven 5393 spring 2013 homework 8 solution

CVEN 5393 Spring 2013 Homework 8 Solution

Revelle, Whitlatch and Wright

Civil and Environmental Systems Engineering – 2nd Edition

Problem 5.3

a plot feasible region in decision space
a) Plot feasible region in decision space

4x1 - 12x2 <= -6

-4x1 + 6x2 <= 12

4x1 + 2x2 >= 8

x1 + x2 <= 9

4x2 <= 16

slide3

b) Plot the corresponding feasible region in objective space. For each extreme point indicate if it is a noninferior or dominated solution

Max Z2

Min Z1

Z2

Z1

slide4

C. Use the constraint method (graphically) to generate an approximation of the non-inferior set having 6 noninferior solutions evenly spaced along the Z1 axis.

Construct a payoff table. Each row represents the solution of one individual objective function, and shows the values for the other objects at that point. If alternate optima are detected, compare the values of other objectives to select the noninferior point. Every point in the payoff table is a noninferior solution. The table gives the entire range of values each objective can have on the noninferior set.

Need 6 noninferior solutions evenly spaced along the Z1 axis

Max is 14; Min is 4.0 Other 4 points are 6, 8, 10, 12

Create constrained problem by selecting one of the objectives to optimize and moving the other(s) into the constraint set with the addition of a right hand side coefficient for each. 4 additional constraints to add to the problem, one at a time and find optimal solution for Z2:

2X1 + X2 <= 6

2X1 + X2 <= 8

2X1 + X2 <= 10

2X1 + X2 <= 12

slide5

Add each additional constraint to the set and find the optimal solution for the remaining objective. Each solution is a noninferior solution of the multiobjective problem.

2x1 + x2 <= 12

2x1 + x2 <= 10

2x1 + x2 <= 14

4x1 - 12x2 <= -6

2x1 + x2 <= 8

New constraint set:

2X1 + X2 <= 6

2X1 + X2 <= 8

2X1 + X2 <= 10

2X1 + X2 <= 12

-4x1 + 6x2 <= 12

4x1 + 2x2 >= 8

2x1 + x2 <= 6

x1 + x2 <= 9

----

----

----

4x2 <= 16

-----

Noninferior Solutions

--------------------------------------------------------------------------------------------------------------------0

2x1 + x2 <= 6

Max 3X1 + 7X2

slide6

c) The selection of the Z1 points gave the same solution for the pareto front as we got in a) and b). If we had not used as many Z1 points we may have gotten some approximation errors.

Max Z2

Min Z1

Z2

Z1

slide7

d.) Use the weighting method (graphically) to generate an approximiation of the non- inferior set having 6 noninferior solutions evenly spaced along the Z2 axis.What is range of Z2 point? We know this from the payoff table. Z2 ranges from 19.75 to 43.0.Six evenly space values are: Z2 = 19.75; 24.40; 29.05; 33.70; 38.35; 43.0To solve: use the weighting method to identify noninferior points in objective space. Then interpolate between these to find the value of noninferior points at the designated Z2 values. Note that since Z1 is a minimization objective and Z2 is a max objective, we must make Z1 also a max objective by taking the negative of it.We arbitrarily select a set of weights and compute the Grand Objective:

d solve single grand objectives
d) Solve single grand objectives

4x1 - 12x2 <= -6

-4x1 + 6x2 <= 12

x1 x2 Z1 Z2

0.75 2.5 4.0 19.75

0.75 2.5 4.0 19.75

3.0 4.0 10.0 37.0

5.0 4.0 14.0 43.0

5.0 4.0 14.0 43.0

5.0 4.0 14.0 43.0

5.0 4.0 14.0 43.0

GrObjective

-2x1– x2

2 -x1+ 0.6x2

2.2x2

4 x1+ 3.8x2

5 2.0x1+ 5.4x2

6 3x1+ 7x2

Solution

A,B

B

C,D

D

D

D

4x1 + 2x2 >= 8

x1 + x2 <= 9

4x2 <= 16

b use linear interpolation to find the noninferior points at z 2 19 75 24 40 29 05 33 70 38 35 43 0
b) Use linear interpolation to find the noninferior points at Z2 = 19.75; 24.40; 29.05; 33.70; 38.35; 43.0

10.9, 38.35

Z2

Z1

things to keep in mind
Things to keep in mind

The “classical” methods of solving multi-objective optimization problems are based on creating a set of single objective optimization problems, each of which identifies a non-inferior solution. The set forms a pareto front or surface (if more than 2-d). Other solutions can be inferred by interpolation if the variables are continuous.

If you have a plot of extreme points in objective space, you can identify the noninferior extreme points by the relative values of the objectives or by the “corner” rule. The NE corner rule depends on sign of objectives

The payoff table identifies the range of values that each objective can have.

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