Operations Research: Applications and Algorithms
Operations Research: Applications and Algorithms
4th Edition
ISBN: 9780357907818
Author: WINSTON
Publisher: Cengage Learning US
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Chapter 5.3, Problem 11P

Explanation of Solution

Contradicting the reason:

  • Let xi be the number of cars produced at plant “i” then, to express the objective function for thousands of dollars, the suitable Linear Programming is,
  • min z= 15x1+10x2+9x3+7x4
  • Such that
    • x1+x2+x3+x41000
    • x3400
    • 2x1+3x2+4x3+5x43300
    • 3x1+4x2+5x3+6x44000
    • x1,x2,x3,x40
  • The following is the LINDO output for this Linear Programming:
  • Min z= 15x1+10x2+9x3+7x4
  • Subject to constraint
    • x1+x2+x3+x41000
    • x3400
    • 2x1+3x2+4x3+5x43300
    • 3x1+4x2+5x3+6x44000
  • End
  • LP optimum is found at step 2
  • Objective function value: 11600

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Question 1 Generate a random sample of standard lognormal data (rlnorm()) for sample size n = 100. Construct histogram estimates of density for this sample using Sturges’ Rule, Scott’s Normal Reference Rule, and the FD Rule. Question 2 Construct a frequency polygon density estimate for the sample in Question 1, using bin width determined by Sturges’ Rule.
Generate a random sample of standard lognormal data (rlnorm()) for sample size n = 100. Construct histogram estimates of density for this sample using Sturges’ Rule, Scott’s Normal Reference Rule, and the FD Rule.
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