Solution Manual for Quantitative Methods For Business 13th Edition / All Chapters 1 – 17 / Full Complete 2023

Quantitative Methods For Business 13th Edition
Solution Manual
Contents
Preface
Chapter 1: Introduction
Chapter 2: Introduction to Probability
Chapter 3: Probability Distributions
Chapter 4: Decision Analysis
Chapter 5: Utility and Game Theory
Chapter 6: Forecasting
Chapter 7: Introduction to Linear Programming
Chapter 8: Linear Programming: Sensitivity Analysis and Interpretation of Solution
Chapter 9: Linear Programming Applications in Marketing, Finance, and Operations Management
Chapter 10: Distribution and Network Models
Chapter 11: Integer Linear Programming
Chapter 12: Advanced Optimization Applications
Chapter 13: Project Scheduling: PERT/CPM
Chapter 14: Inventory Models
Chapter 15: Waiting Line Models
Chapter 16: Simulation
Chapter 17: Markov Processes
Appendix A: Building Spreadsheet Models

Chapter 1
Introduction
Learning Objectives

1. Develop a general understanding of the management science/operations research approach to decision
   making.
2. Realize that quantitative applications begin with a problem situation.
3. Obtain a brief introduction to quantitative techniques and their frequency of use in practice.
4. Understand that managerial problem situations have both quantitative and qualitative considerations
   that are important in the decision making process.
5. Learn about modelsin terms of what they are and why they are useful (the emphasisis on mathematical
   models).
6. Identify the step-by-step procedure that is used in most quantitative approaches to decision making.
7. Learn about basic models of cost, revenue, and profit and be able to compute the breakeven point.
8. Obtain an introduction to the use of computersoftware packages such as Microsoft Excel in applying
   quantitative methods to decision making.
9. Understand the following terms:
   model infeasible solution
   objective function management science
   constraint operations research
   deterministic model fixed cost
   stochastic model variable cost
   feasible solution breakeven point
   1 –

Chapter 1
Solutions:

1. Management science and operations research, terms used almost interchangeably, are broad
   disciplines that employ scientific methodology in managerial decision making or problem
   solving. Drawing upon a variety of disciplines (behavioral, mathematical, etc.), management
   science and operations research combine quantitative and qualitative considerations in order to
   establish policies and decisions that are in the best interest of the organization.
2. Define the problem
   Identify the alternatives
   Determine the criteria
   Evaluate the alternatives
   Choose an alternative
   For further discussion see section 1.3
3. See section 1.2.
4. A quantitative approach should be considered because the problem is large, complex, important,
   new and repetitive.
5. Models usually have time, cost, and risk advantages over experimenting with actualsituations.
6. Model (a) may be quicker to formulate, easier to solve, and/or more easily understood.
7. Let d = distance
   m = miles per gallon
   c = cost per gallon,
   Therefore Total Cost =
   We must be willing to treat m and c as known and notsubject to variation.
8. a. Maximize 10x + 5y
   s.t.
   5x + 2y  40
   x  0, y  0
   b. Controllable inputs: x and y
   Uncontrollable inputs: profit (10,5), labor hours (5,2) and labor-hour availability (40)

Projected Profit and
check on production
time constraint
Output
Introduction
c.
Profit: $10/unit for x
$ 5/ unit fory
Labor Hours: 5/unit for x
2/ unit fory
40 labor-hour capacity
Uncontrollable Inputs
d. x = 0, y = 20 Profit = $100
(Solution by trial-and-error)
e. Deterministic – all uncontrollable inputs are fixed and known.

9. If a = 3, x = 13 1/3 and profit = 133
   If a = 4, x = 10 and profit = 100
   If a = 5, x = 8 and profit = 80
   If a = 6, x = 6 2/3 and profit = 67
   Since a is unknown, the actual values of x and profit are not known with certainty.
10. a. Total Units Received = x + y
    b. Total Cost = 0.20x +0.25y
    c. x + y = 5000
    d. x  4000 Kansas City Constraint
    y  3000 Minneapolis Constraint
    e. Min 0.20x + 0.25y
    s.t.
    x + y = 5000
    x  4000
    y  3000
    x, y  0
    Mathematical
    Model
     0
    y  0

• 5 y  40
  Max 10x + 5y
  s.t.
  10 x
  x
  Controllable
  Input
  Production Quantities
  x and y