Identifying the case that applies the following LPs:

Mathematical model of given LP is,

Maximize z = x1−x2

Subject to the constraints,

x1+x2≤6x1−x2≥0

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Chapter 3.3, Problem 7P

Chapter 3.3, Problem 9P

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b) Consider the following linear programming problem: Min z = x1 + x2 s.t. 3x1 – 2x2 < 5 X1 + x2 < 3 3x1 + 3x2 2 9 X1, X2 2 0 Using the graphical approach, determine the possible optimal solution(s) and comment on the special case involved, if any.

Variable Cells Final Reduced Objective Allowable Allowable Cell Name Value Cost Coefficient Increase Decrease Туре А 4 -3.5 1E+30 Туре B y Туре С 3. 6. 2 Constraints Final Shadow Constraint Allowable Allowable Cell Name Value Price R.H. Side Increase Decrease Hours 1. 15 1 15 12 8. Plastic 20 30 1E+30 30 Cotton 70 70 40 55 Calculate the optimal value of the objective function if the coefficient of "x" changes to 4 and the coefficient of "y" changes to 3.5. Hint: Since it is proposing a simultaneous change (more than one change at a time), we have to check the 100% rule. **Enter the number only** **Do not use any words or symbols** Answer: 5 M 5

Solve the following problem and find the optimal solution.

Chapter 3 Solutions

Introduction to mathematical programming

Ch. 3.1 - Prob. 1P Ch. 3.1 - Prob. 2P Ch. 3.1 - Prob. 3P Ch. 3.1 - Prob. 4P Ch. 3.1 - Prob. 5P Ch. 3.2 - Prob. 1P Ch. 3.2 - Prob. 2P Ch. 3.2 - Prob. 3P Ch. 3.2 - Prob. 4P Ch. 3.2 - Prob. 5P

Ch. 3.2 - Prob. 6P Ch. 3.3 - Prob. 1P Ch. 3.3 - Prob. 2P Ch. 3.3 - Prob. 3P Ch. 3.3 - Prob. 4P Ch. 3.3 - Prob. 5P Ch. 3.3 - Prob. 6P Ch. 3.3 - Prob. 7P Ch. 3.3 - Prob. 8P Ch. 3.3 - Prob. 9P Ch. 3.3 - Prob. 10P Ch. 3.4 - Prob. 1P Ch. 3.4 - Prob. 2P Ch. 3.4 - Prob. 3P Ch. 3.4 - Prob. 4P Ch. 3.5 - Prob. 1P Ch. 3.5 - Prob. 2P Ch. 3.5 - Prob. 3P Ch. 3.5 - Prob. 4P Ch. 3.5 - Prob. 5P Ch. 3.5 - Prob. 6P Ch. 3.5 - Prob. 7P Ch. 3.6 - Prob. 1P Ch. 3.6 - Prob. 2P Ch. 3.6 - Prob. 3P Ch. 3.6 - Prob. 4P Ch. 3.6 - Prob. 5P Ch. 3.7 - Prob. 1P Ch. 3.8 - Prob. 1P Ch. 3.8 - Prob. 2P Ch. 3.8 - Prob. 3P Ch. 3.8 - Prob. 4P Ch. 3.8 - Prob. 5P Ch. 3.8 - Prob. 6P Ch. 3.8 - Prob. 7P Ch. 3.8 - Prob. 8P Ch. 3.8 - Prob. 9P Ch. 3.8 - Prob. 10P Ch. 3.8 - Prob. 11P Ch. 3.8 - Prob. 12P Ch. 3.8 - Prob. 13P Ch. 3.8 - Prob. 14P Ch. 3.9 - Prob. 1P Ch. 3.9 - Prob. 2P Ch. 3.9 - Prob. 3P Ch. 3.9 - Prob. 4P Ch. 3.9 - Prob. 5P Ch. 3.9 - Prob. 6P Ch. 3.9 - Prob. 7P Ch. 3.9 - Prob. 8P Ch. 3.9 - Prob. 9P Ch. 3.9 - Prob. 10P Ch. 3.9 - Prob. 11P Ch. 3.9 - Prob. 12P Ch. 3.9 - Prob. 13P Ch. 3.9 - Prob. 14P Ch. 3.10 - Prob. 1P Ch. 3.10 - Prob. 2P Ch. 3.10 - Prob. 3P Ch. 3.10 - Prob. 4P Ch. 3.10 - Prob. 5P Ch. 3.10 - Prob. 6P Ch. 3.10 - Prob. 7P Ch. 3.10 - Prob. 8P Ch. 3.10 - Prob. 9P Ch. 3.11 - Prob. 1P Ch. 3.11 - Show that Fincos objective function may also be...Ch. 3.11 - Prob. 3P Ch. 3.11 - Prob. 4P Ch. 3.11 - Prob. 7P Ch. 3.11 - Prob. 8P Ch. 3.11 - Prob. 9P Ch. 3.12 - Prob. 2P Ch. 3.12 - Prob. 3P Ch. 3.12 - Prob. 4P Ch. 3 - Prob. 1RP Ch. 3 - Prob. 2RP Ch. 3 - Prob. 3RP Ch. 3 - Prob. 4RP Ch. 3 - Prob. 5RP Ch. 3 - Prob. 6RP Ch. 3 - Prob. 7RP Ch. 3 - Prob. 8RP Ch. 3 - Prob. 9RP Ch. 3 - Prob. 10RP Ch. 3 - Prob. 11RP Ch. 3 - Prob. 12RP Ch. 3 - Prob. 13RP Ch. 3 - Prob. 14RP Ch. 3 - Prob. 15RP Ch. 3 - Prob. 16RP Ch. 3 - Prob. 17RP Ch. 3 - Prob. 18RP Ch. 3 - Prob. 19RP Ch. 3 - Prob. 20RP Ch. 3 - Prob. 21RP Ch. 3 - Prob. 22RP Ch. 3 - Prob. 23RP Ch. 3 - Prob. 24RP Ch. 3 - Prob. 25RP Ch. 3 - Prob. 26RP Ch. 3 - Prob. 27RP Ch. 3 - Prob. 28RP Ch. 3 - Prob. 29RP Ch. 3 - Prob. 30RP Ch. 3 - Prob. 31RP Ch. 3 - Prob. 32RP Ch. 3 - Prob. 33RP Ch. 3 - Prob. 34RP Ch. 3 - Prob. 35RP Ch. 3 - Prob. 36RP Ch. 3 - Prob. 37RP Ch. 3 - Prob. 38RP Ch. 3 - Prob. 39RP Ch. 3 - Prob. 40RP Ch. 3 - Prob. 41RP Ch. 3 - Prob. 42RP Ch. 3 - Prob. 43RP Ch. 3 - Prob. 44RP Ch. 3 - Prob. 45RP Ch. 3 - Prob. 46RP Ch. 3 - Prob. 47RP Ch. 3 - Prob. 48RP Ch. 3 - Prob. 49RP Ch. 3 - Prob. 50RP Ch. 3 - Prob. 51RP Ch. 3 - Prob. 52RP Ch. 3 - Prob. 53RP Ch. 3 - Prob. 54RP Ch. 3 - Prob. 56RP Ch. 3 - Prob. 57RP Ch. 3 - Prob. 58RP Ch. 3 - Prob. 59RP Ch. 3 - Prob. 60RP Ch. 3 - Prob. 61RP Ch. 3 - Prob. 62RP Ch. 3 - Prob. 63RP

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Similar questions

QUESTION 9 What is one advantage of AABB over Bounding Spheres? Computing the optimal AABB for a set of points is easy to program and can be run in linear time. Computing the optimal bounding sphere is a much more difficult problem. The volume of AABB can be an integer, while the volume of a Bounding Sphere is always irrational. An AABB can surround a Bounding Sphere, while a Bounding Sphere cannot surround an AABB. To draw a Bounding Ball you need calculus knowledge.
If 142 and 155 are two feasible solutions for a primal minimization problem, whereas 122 and 130 are two feasible solutions for the associated dual maximization problem. Which of the following statements is the most correct? 122 <= Optimal solution <= 155 O b. 122 <= Optimal solution <= 142 D DC 130 <= Optimal solution <= 142 Od 130 <= Optimal solution <= 155
If it is possible to create an optimal solution for a problem by constructing optimal solutions for its subproblems, then the problem possesses the corresponding property. a) Subproblems which overlap b) Optimal substructure c) Memorization d) Greedy

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Identifying the case that applies the following LPs: Mathematical model of given LP is, Maximize z = x 1 − x 2 Subject to the constraints, x 1 + x 2 ≤ 6 x 1 − x 2 ≥ 0

Solution Summary: The author explains that the given linear program has no feasible region and thus is an infeasible program.

Explanation of Solution

Identifying the case that applies the following LPs:

Mathematical model of given LP is,

Maximize z = x1−x2

Subject to the constraints,

x1+x2≤6x1−x2≥0

Want to see the full answer?

Chapter 3 Solutions

Identifying the case that applies the following LPs: Mathematical model of given LP is, Maximize z = x 1 − x 2 Subject to the constraints, x 1 + x 2 ≤ 6 x 1 − x 2 ≥ 0

Solution Summary: The author explains that the given linear program has no feasible region and thus is an infeasible program.

Explanation of Solution

Identifying the case that applies the following LPs: Mathematical model of given LP is, Maximize z = x1−x2 Subject to the constraints, x1+x2≤6x1−x2≥0

Want to see the full answer?

Chapter 3 Solutions

Identifying the case that applies the following LPs:

Mathematical model of given LP is,

Maximize z = x1−x2

Subject to the constraints,

x1+x2≤6x1−x2≥0