Operations Research : Applications and Algorithms
Operations Research : Applications and Algorithms
4th Edition
ISBN: 9780534380588
Author: Wayne L. Winston
Publisher: Brooks Cole
bartleby

Concept explainers

Expert Solution & Answer
Book Icon
Chapter 4.16, Problem 13P

Explanation of Solution

Formulating preemptive goal programming model:

Consider the linear programming problem of new president deciding the tax rate to achieve the following goals:

Goal 1: Balance the budget (this means revenues are at least as large as costs).

Goal 2: Cut spending by at most $150 billion.

Goal 3: Raise at most $550 billion in taxes from rich.

Goal 4: Raise at most $350 billion in taxes from the poor.

Let,

x= Number of low income tax prayers.

y= Number of high income tax prayers.

Now, determine the below stated values

G= Per gallon tax rate

LTR= percentage tax rate charged on first $30,000 of income

HTR= Percentage tax rate charged on any income earned more than $30,000

C= Cut in spending

 Low IncomeHigh Income
Gas taxG0.5G
Tax on income up to $3000020LTR5LTR
Tax on income above $30000015HTR

From the given information, the following constraints are formed.

Goal 1: Balance the budget. Amount spend (1000 billion = amount collected as tax). The constraint formed is given below,

Gx+0.5y+20LTRx+5LTRy+15HTRy=1000

Goal 2: Cut spending by at most $150 billion. The constraint formed is given below,

C150

Goal 3: Raise at most $550 billion in taxes from rich. The constraint formed is given below,

0.5Gy+5LTRy+15HTRy550

Goal 4: Raise at most $350 billion in taxes from the poor.

Gx+20LTRx350

Here, the user can observe that the above set of constraints there is no feasible region. That is all constraints cannot be met. Hence the user should assign a cost value incurred if any of the goal is not met. So, introduce the deviational variables as follows

si-= Amount by which numerically under the ith goal.

si+= Amount by which numerically exceed the ith goal.

Thus the constraints become,

Gx+0.5y+20LTRx+5LTRy+15HTRy+s1+-s1-=1000C+s2+-s2-=1500

Blurred answer
Students have asked these similar questions
Exercise 1 Santa is worried about his employee relations, since christmas preparations have led to a lot of overtime. To make sure all the elves are happy, he wants to recruit some of them as complaint officers, with weekly meetings to report any complaints or worries to him. His worker elves W are pretty busy already, so Santa wants to task no more than k elves with this additional workload. Still, Santa wants to make sure that for as many elves e e W as possible, at least one of his friends (whose identities he knows) is a complaint officer. 1. Give an intuitive greedy algorithm that outputs k elves that will serve as compliant officers. 2. Prove that for large numbers of k the algorithm approximates a solution with ratio no more than (1 – !).
Consider the following task of building a zoo for Drexel! You've gathered m donors to fund the creation ofyour new park, and you've picked the location, so now you just need to choose the inhabitants. Ideally you'dget every animal imaginable, except you just don't have space. As it is, you have room to comfortably  tk animals. Since you'd like to ensure that your funding doesn't dry up, you  gure your donors should getto make requests about which animals will be kept. After sending out a few emails, you collect from eachdonor i a list of animals Ai which donor i would like to have at the zoo. As you suspected, the total numberof di erent animals appearing on the m lists exceeds k, so you won't be able to satisfy all of their requests.You decide the fairest thing to do is to ensure that at least 1 animal from each Ai is chosen.Given all these lists, we want to know whether we can select a set H of at most k animals to put in thezoo such that each donor i will be able to see at least one…
Consider the following task of building a zoo for Drexel! You’ve gathered m donors to fund the creation ofyour new park, and you’ve picked the location, so now you just need to choose the inhabitants. Ideally you’dget every animal imaginable, except you just don’t have space. As it is, you have room to comfortably fitk animals. Since you’d like to ensure that your funding doesn’t dry up, you figure your donors should getto make requests about which animals will be kept. After sending out a few emails, you collect from eachdonor i a list of animals Ai which donor i would like to have at the zoo. As you suspected, the total numberof different animals appearing on the m lists exceeds k, so you won’t be able to satisfy all of their requests.You decide the fairest thing to do is to ensure that at least 1 animal from each Aiis chosen.Given all these lists, we want to know whether we can select a set H of at most k animals to put in thezoo such that each donor i will be able to see at least…

Chapter 4 Solutions

Operations Research : Applications and Algorithms

Ch. 4.5 - Prob. 1PCh. 4.5 - Prob. 2PCh. 4.5 - Prob. 3PCh. 4.5 - Prob. 4PCh. 4.5 - Prob. 5PCh. 4.5 - Prob. 6PCh. 4.5 - Prob. 7PCh. 4.6 - Prob. 1PCh. 4.6 - Prob. 2PCh. 4.6 - Prob. 3PCh. 4.6 - Prob. 4PCh. 4.7 - Prob. 1PCh. 4.7 - Prob. 2PCh. 4.7 - Prob. 3PCh. 4.7 - Prob. 4PCh. 4.7 - Prob. 5PCh. 4.7 - Prob. 6PCh. 4.7 - Prob. 7PCh. 4.7 - Prob. 8PCh. 4.7 - Prob. 9PCh. 4.8 - Prob. 1PCh. 4.8 - Prob. 2PCh. 4.8 - Prob. 3PCh. 4.8 - Prob. 4PCh. 4.8 - Prob. 5PCh. 4.8 - Prob. 6PCh. 4.10 - Prob. 1PCh. 4.10 - Prob. 2PCh. 4.10 - Prob. 3PCh. 4.10 - Prob. 4PCh. 4.10 - Prob. 5PCh. 4.11 - Prob. 1PCh. 4.11 - Prob. 2PCh. 4.11 - Prob. 3PCh. 4.11 - Prob. 4PCh. 4.11 - Prob. 5PCh. 4.11 - Prob. 6PCh. 4.12 - Prob. 1PCh. 4.12 - Prob. 2PCh. 4.12 - Prob. 3PCh. 4.12 - Prob. 4PCh. 4.12 - Prob. 5PCh. 4.12 - Prob. 6PCh. 4.13 - Prob. 2PCh. 4.14 - Prob. 1PCh. 4.14 - Prob. 2PCh. 4.14 - Prob. 3PCh. 4.14 - Prob. 4PCh. 4.14 - Prob. 5PCh. 4.14 - Prob. 6PCh. 4.14 - Prob. 7PCh. 4.16 - Prob. 1PCh. 4.16 - Prob. 2PCh. 4.16 - Prob. 3PCh. 4.16 - Prob. 5PCh. 4.16 - Prob. 7PCh. 4.16 - Prob. 8PCh. 4.16 - Prob. 9PCh. 4.16 - Prob. 10PCh. 4.16 - Prob. 11PCh. 4.16 - Prob. 12PCh. 4.16 - Prob. 13PCh. 4.16 - Prob. 14PCh. 4.17 - Prob. 1PCh. 4.17 - Prob. 2PCh. 4.17 - Prob. 3PCh. 4.17 - Prob. 4PCh. 4.17 - Prob. 5PCh. 4.17 - Prob. 7PCh. 4.17 - Prob. 8PCh. 4 - Prob. 1RPCh. 4 - Prob. 2RPCh. 4 - Prob. 3RPCh. 4 - Prob. 4RPCh. 4 - Prob. 5RPCh. 4 - Prob. 6RPCh. 4 - Prob. 7RPCh. 4 - Prob. 8RPCh. 4 - Prob. 9RPCh. 4 - Prob. 10RPCh. 4 - Prob. 12RPCh. 4 - Prob. 13RPCh. 4 - Prob. 14RPCh. 4 - Prob. 16RPCh. 4 - Prob. 17RPCh. 4 - Prob. 18RPCh. 4 - Prob. 19RPCh. 4 - Prob. 20RPCh. 4 - Prob. 21RPCh. 4 - Prob. 22RPCh. 4 - Prob. 23RPCh. 4 - Prob. 24RPCh. 4 - Prob. 26RPCh. 4 - Prob. 27RPCh. 4 - Prob. 28RP
Knowledge Booster
Background pattern image
Computer Science
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.
Similar questions
SEE MORE QUESTIONS
Recommended textbooks for you
Text book image
Operations Research : Applications and Algorithms
Computer Science
ISBN:9780534380588
Author:Wayne L. Winston
Publisher:Brooks Cole