1-2 Introduction to MS.pdf

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© 2008 Thomson South-Western. All Rights Reserved
Slides by
JOHN
LOUCKS
St. Edward’s
University

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Management Science Revolution
 According to Irv Lustig of ILOG Inc., solution methods
developed today are 10, 000 times faster than the ones
used 15 years ago.
 Quantitative methods are especially helpful with large,
complex problems. For example, in the coordination of
thousands of tasks associated with landing Apollo 11
safely on the moon, quantitative techniques helped to
ensure that more than 300, 000 pieces of work performed
by more than 400,000 people were integrated smoothly.
 Herbert A. Simon, a Nobel Prize winner in economics
and an expert in decision making, said that a
mathematical model does not have to be exact; it just has
to be close enough to provide better results than can be
obtained by common sense.

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Chapter 1
Introduction
 Body of Knowledge
 Problem Solving and Decision Making
 Quantitative Analysis and Decision Making
 Quantitative Analysis
 Models of Cost, Revenue, and Profit
 Management Science Techniques

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Body of Knowledge
 The body of knowledge involving quantitative
approaches to decision making is referred to as
•Management Science
•Operations Research
•Decision Science
 It had its early roots in World War II and is flourishing
in business and industry due, in part, to:
•numerous methodological developments (e.g.
simplex method for solving linear programming
problems)
•a virtual explosion in computing power

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 7 Steps of Problem Solving
(First 5 steps are the process of decision making)
1. Identify and define the problem.
2. Determine the set of alternative solutions.
3. Determine the criteria for evaluating alternatives.
4. Evaluate the alternatives.
5. Choose an alternative (make a decision).
---------------------------------------------------------------------
6. Implement the selected alternative.
7. Evaluate the results.
Problem Solving and Decision Making

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Quantitative Analysis and Decision Making
Define
the
Problem
Identify
the
Alternatives
Determine
the
Criteria
Evaluate
the
Alternatives
Choose
an
Alternative
Structuring the Problem Analyzing the Problem
 Decision-Making Process

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Analysis Phase of Decision-Making Process
 Qualitative Analysis
• based largely on the manager’s judgment and
experience
• includes the manager’s intuitive “feel” for the
problem
• is more of an art than a science

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Analysis Phase of Decision-Making Process
 Quantitative Analysis
• analyst will concentrate on the quantitative facts
or data associated with the problem
• analyst will develop mathematical expressions
that describe the objectives, constraints, and
other relationships that exist in the problem
• analyst will use one or more quantitative
methods to make a recommendation

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 Potential Reasons for a Quantitative Analysis
Approach to Decision Making
•The problem is complex.
•The problem is very important.
•The problem is new.
•The problem is repetitive.

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Quantitative Analysis
 Quantitative Analysis Process
•Model Development
•Data Preparation
•Model Solution
•Report Generation

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Model Development
 Models are representations of real objects or situations
 Three forms of models are:
•Iconic models - physical replicas (scalar
representations) of real objects
•Analog models - physical in form, but do not
physically resemble the object being modeled
•Mathematical models - represent real world
problems through a system of mathematical
formulas and expressions based on key
assumptions, estimates, or statistical analyses

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Advantages of Models
 Generally, experimenting with models (compared to
experimenting with the real situation):
•requires less time
•is less expensive
•involves less risk
 The more closely the model represents the real
situation, the accurate the conclusions and predictions
will be.

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Mathematical Models
 Objective Function – a mathematical expression that
describes the problem’s objective, such as maximizing
profit or minimizing cost
 Constraints – a set of restrictions or limitations, such as
production capacities
 Uncontrollable Inputs – environmental factors that are
not under the control of the decision maker
 Decision Variables – controllable inputs; decision
alternatives specified by the decision maker, such as the
number of units of Product X to produce

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Mathematical Models
 Deterministic Model – if all uncontrollable inputs to the
model are known and cannot vary
 Stochastic (or Probabilistic) Model – if any uncontrollable
are uncertain and subject to variation
 Stochastic models are often more difficult to analyze.

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Mathematical Models
 Cost/benefit considerations must be made in selecting
an appropriate mathematical model.
 Frequently a less complicated (and perhaps less
precise) model is more appropriate than a more
complex and accurate one due to cost and ease of
solution considerations.

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O.R.Models
 OR models address three main questions:
 What are the decision alternatives?
 Under what restrictions is the decision made?
 What is an appropriate objective criterion for
evaluating the alternatives?

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River Crossing Problem
 Amy, Jim, John, and Kelly are standing on the east bank
of a river and wish to cross to the west side using a canoe.
The canoe can hold at most two individuals at a time.
Amy, being the most athletic, can cross the river in one
minute. Jim, John, and Kelly would take 2, 5, and 10
minutes respectively. If two people are in the canoe, the
slower person dictates the crossing time. The objective is
for all four individuals to be on the other side of the river
in the least time possible.
• Identify at least two feasible plans for crossing the
river (remember, the canoe is the only mode of
transportation and it cannot be shuttled empty).
• Define the criterion for evaluating the alternatives.
• What is the smallest time for moving all four
individuals to the other side of the river?

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River Crossing Problem (contd.)

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Another example
 Consider forming a maximum area rectangle out of a piece of
wire of length L cms.
 Here the number of alternatives is not finite, since the length
and width of the rectangle can assume infinite possibilities.
 Hence the alternatives of the problem are identified by
defining the length and width as continuous (algebraic)
variables.
 Let
 l = length of the rectangle in cms
 w = width of the rectangle in cms.
 Based on the above definitions the restrictions can be
expressed verbally as
 Length of rectangle + width of rectangle = half of the length of
the wire.
 Length and width cannot be negative.

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Another example
 We may write these conditions algebraically as
 2(l+w) = L
 l>= 0, w>=0.
 We now define the remaining component of objective of the
problem:
 Maximize z=lw, where z is defined as the area of the rectangle.
 The complete model becomes
 Maximize z=lw,
 Subject to
 2(l+w) = L
 l, w >=0.
 The optimal solution of this model is l=w = L/4, which means
we have to construct a square.

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OR Model format
 An OR model is usually presented in the following
format:
 Maximize or minimize objective functionSubject
toconstraints
 A solution of the model is feasible if it satisfies all the
constraints.
 It is optimal if, in addition to being feasible, it yields the
best (maximum or Minimum) value of the objective
function.
 In the tickets example, there are three feasible
alternatives and the third one yields the optimal
solution.
 In the rectangle example, a feasible alternative must
satisfy the condition l+w = L/2 with l and w assuming
non – negative values. This leads to infinity of feasible
solutions and the optimal solution is determined by an
appropriate mathematical tool (in this case, differential
calculus).

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Quality and sub- optimality
 Quality and sub- optimality
 OR models are designed to optimize a specific
objective criterion subject to a set of constraints.
 However the quality of the resulting solution
depends on the accuracy of the model in
representing the real system.
 For example, in the tickets problem, if one is not
able to identify all the dominant alternatives, then
the resulting solution is only optimal relative to the
choices represented in the model. To be specific, if
alt 3 is left out of the model, and then the resulting
optimal solution would call for spending Rs.44000
to purchase the tickets, which provides only a sub
optimal solution to the problem.
 Hence it can be concluded that the optimal solution
is best only for that model. If the model represents
the real system reasonably well, then its solution is
optimal also for the real situation.

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Transforming Model Inputs into Output
Uncontrollable Inputs
(Environmental Factors)
Controllable
Inputs
(Decision
Variables)
Output
(Projected
Results)
Mathematical
Model

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Data Preparation
 Data preparation is not a trivial step, due to the time
required and the possibility of data collection errors.
 A model with 50 decision variables and 25 constraints
could have over 1300 data elements!
 Often, a fairly large data base is needed.
 Information systems specialists might be needed.

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Model Solution
 The analyst attempts to identify the alternative (the
set of decision variable values) that provides the
“best” output for the model.
 The “best” output is the optimal solution.
 If the alternative does not satisfy all of the model
constraints, it is rejected as being infeasible, regardless
of the objective function value.
 If the alternative satisfies all of the model constraints,
it is feasible and a candidate for the “best” solution.

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Model Solution
 One solution approach is trial-and-error.
•Might not provide the best solution
•Inefficient (numerous calculations required)
 Special solution procedures have been developed for
specific mathematical models.
•Some small models/problems can be solved by
hand calculations
•Most practical applications require using a
computer

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Model Solution
 A variety of software packages are available for
solving mathematical models.
•Microsoft Excel
•The Management Scientist
•LINGO

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Model Testing and Validation
 Often, goodness/accuracy of a model cannot be
assessed until solutions are generated.
 Small test problems having known, or at least
expected, solutions can be used for model testing and
validation.
 If the model generates expected solutions, use the
model on the full-scale problem.
 If inaccuracies or potential shortcomings inherent in
the model are identified, take corrective action such
as:
•Collection of more-accurate input data
•Modification of the model

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Report Generation
 A managerial report, based on the results of the
model, should be prepared.
 The report should be easily understood by the
decision maker.
 The report should include:
•the recommended decision
•other pertinent information about the results (for
example, how sensitive the model solution is to the
assumptions and data used in the model)

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Implementation and Follow-Up
 Successful implementation of model results is of
critical importance.
 Secure as much user involvement as possible
throughout the modeling process.
 Continue to monitor the contribution of the model.
 It might be necessary to refine or expand the model.

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Example: Austin Auto Auction
An auctioneer has developed a simple
mathematical model for deciding the starting bid he
will require when auctioning a used automobile.
Essentially, he sets the starting bid at seventy
percent of what he predicts the final winning bid will
(or should) be. He predicts the winning bid by
starting with the car's original selling price and
making two deductions, one based on the car's age
and the other based on the car's mileage.
The age deduction is $800 per year and the
mileage deduction is $.025 per mile.

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 Question:
Develop the mathematical model that will give
the starting bid (B ) for a car in terms of the car's
original price (P ), current age (A) and mileage (M ).

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 Answer:
The expected winning bid can be expressed as:
P - 800(A) - .025(M )
The entire model is:
B = .7(expected winning bid)
B = .7(P - 800(A) - .025(M ))
B = .7(P )- 560(A) - .0175(M )

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 Question:
Suppose a four-year old car with 60,000 miles on
the odometer is being auctioned. If its original price
was $12,500, what starting bid should the auctioneer
require?
 Answer:
B = .7(12,500) - 560(4) - .0175(60,000) = $5,460

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 Question:
The model is based on what assumptions?
 Answer:
The model assumes that the only factors
influencing the value of a used car are the original
price, age, and mileage (not condition, rarity, or other
factors).
Also, it is assumed that age and mileage devalue a
car in a linear manner and without limit. (Note, the
starting bid for a very old car might be negative!)

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Example: Iron Works, Inc.
Iron Works, Inc. manufactures two
products made from steel and just received
this month's allocation of b pounds of steel.
It takes a1 pounds of steel to make a unit of product 1
and a2 pounds of steel to make a unit of product 2.
Let x1 and x2 denote this month's production level of
product 1 and product 2, respectively. Denote by p1 and
p2 the unit profits for products 1 and 2, respectively.
Iron Works has a contract calling for at least m units of
product 1 this month. The firm's facilities are such that at
most u units of product 2 may be produced monthly.

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 Mathematical Model
•The total monthly profit =
(profit per unit of product 1)
x (monthly production of product 1)
+ (profit per unit of product 2)
= p1x1 + p2x2
We want to maximize total monthly profit:
Max p1x1 + p2x2

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 Mathematical Model (continued)
•The total amount of steel used during monthly
production equals:
(steel required per unit of product 1)
+ (steel required per unit of product 2)
= a1x1 + a2x2
This quantity must be less than or equal to the
allocated b pounds of steel:
a1x1 + a2x2 < b

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 Mathematical Model (continued)
•The monthly production level of product 1 must
be greater than or equal to m :
x1 > m
•The monthly production level of product 2 must
be less than or equal to u :
x2 < u
•However, the production level for product 2
cannot be negative:
x2 > 0

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 Mathematical Model Summary
Max p1x1 + p2x2
s.t. a1x1 + a2x2 < b
x1 > m
x2 < u
x2 > 0
Objectiv
e
Function
“Subject to”
Constraint
s

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 Question:
Suppose b = 2000, a1 = 2, a2 = 3, m = 60, u = 720,
p1 = 100, p2 = 200. Rewrite the model with these
specific values for the uncontrollable inputs.

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 Answer:
Substituting, the model is:
Max 100x1 + 200x2
s.t. 2x1 + 3x2 < 2000
x1 > 60
x2 < 720
x2 > 0

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 Question:
The optimal solution to the current model is x1 =
60 and x2 = 626 2/3. If the product were engines,
explain why this is not a true optimal solution for the
"real-life" problem.
 Answer:
One cannot produce and sell 2/3 of an engine.
Thus the problem is further restricted by the fact that
both x1 and x2 must be integers. (They could remain
fractions if it is assumed these fractions are work in
progress to be completed the next month.)

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Uncontrollable Inputs
$100 profit per unit Prod. 1
$200 profit per unit Prod. 2
2 lbs. steel per unit Prod. 1
3 lbs. Steel per unit Prod. 2
2000 lbs. steel allocated
60 units minimum Prod. 1
720 units maximum Prod. 2
0 units minimum Prod. 2
60 units Prod. 1
626.67 units Prod. 2
Controllable Inputs
Profit = $131,333.33
Steel Used = 2000
Output
Mathematical Model
Max 100(60) + 200(626.67)
s.t. 2(60) + 3(626.67) < 2000
60 > 60
626.67 < 720
626.67 > 0

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Example: Ponderosa Development Corp.
Ponderosa Development Corporation
(PDC) is a small real estate developer that builds
only one style house. The selling price of the house is
$115,000.
Land for each house costs $55,000 and lumber,
supplies, and other materials run another $28,000 per
house. Total labor costs are approximately $20,000 per
house.

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Ponderosa leases office space for $2,000
per month. The cost of supplies, utilities, and
leased equipment runs another $3,000 per month.
The one salesperson of PDC is paid a commission
of $2,000 on the sale of each house. PDC has seven
permanent office employees whose monthly salaries
are given on the next slide.

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Employee Monthly Salary
President $10,000
VP, Development 6,000
VP, Marketing 4,500
Project Manager 5,500
Controller 4,000
Office Manager 3,000
Receptionist 2,000

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 Question:
Identify all costs and denote the marginal cost
and marginal revenue for each house.
 Answer:
The monthly salaries total $35,000 and monthly
office lease and supply costs total another $5,000.
This $40,000 is a monthly fixed cost.
The total cost of land, material, labor, and sales
commission per house, $105,000, is the marginal cost
for a house.
The selling price of $115,000 is the marginal
revenue per house.

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 Question:
Write the monthly cost function c (x), revenue
function r (x), and profit function p (x).
 Answer:
c (x) = variable cost + fixed cost = 105,000x + 40,000
r (x) = 115,000x
p (x) = r (x) - c (x) = 10,000x - 40,000

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 Question:
What is the breakeven point for monthly sales
of the houses?
 Answer:
r (x ) = c (x )
115,000x = 105,000x + 40,000
Solving, x = 4.

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 Question:
What is the monthly profit if 12 houses per
month are built and sold?
 Answer:
p (12) = 10,000(12) - 40,000 = $80,000 monthly profit

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0
200
400
600
800
1000
1200
0 1 2 3 4 5 6 7 8 9 10
Number of Houses Sold (x)
Thousands
of
Dollars
Break-Even Point = 4 Houses
Total Cost =
40,000 + 105,000x
Total Revenue =
115,000x

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Management Science Techniques
 Linear Programming
 Integer Linear
Programming
 PERT/CPM
 Inventory Models
 Waiting Line Models
 Simulation
 Decision Analysis
 Goal Programming
 Analytic Hierarchy
Process
 Forecasting
 Markov-Process Models
 Dynamic Programming

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The Management Scientist Software
 12 Modules

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Using Excel for Breakeven Analysis
 A spreadsheet software package such as Microsoft
Excel can be used to perform a quantitative analysis of
Ponderosa Development Corporation.
 We will enter the problem data in the top portion of
the spreadsheet.
 The bottom of the spreadsheet will be used for model
development.

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 Formula Spreadsheet
A B
1 PROBLEM DATA
2 Fixed Cost $40,000
3 Variable Cost Per Unit $105,000
4 Selling Price Per Unit $115,000
5 MODEL
6 Sales Volume
7 Total Revenue =B4*B6
8 Total Cost =B2+B3*B6
9 Total Profit (Loss) =B7-B8

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 Question
What is the monthly profit if 12 houses are built
and sold per month?

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 Spreadsheet Solution
A B
1 PROBLEM DATA
5 MODEL
6 Sales Volume 12
7 Total Revenue $1,380,000
8 Total Cost $1,300,000
9 Total Profit (Loss) $80,000

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 Question:
What is the breakeven point for monthly sales
of the houses?
 Spreadsheet Solution:
•One way to determine the break-even point using a
spreadsheet is to use the Goal Seek tool.
•Microsoft Excel ‘s Goal Seek tool allows the user to
determine the value for an input cell that will cause
the output cell to equal some specified value.
•In our case, the goal is to set Total Profit to zero by
seeking an appropriate value for Sales Volume.

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 Spreadsheet Solution: Goal Seek Approach
Using Excel ’s Goal Seek Tool
Step 1: Select the Tools menu
Step 2: Choose the Goal Seek option
Step 3: When the Goal Seek dialog box appears:
Enter B9 in the Set cell box
Enter 0 in the To value box
Enter B6 in the By changing cell box
Click OK

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Completed Goal Seek Dialog Box

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A B
1 PROBLEM DATA
5 MODEL
6 Sales Volume 4
7 Total Revenue $460,000
8 Total Cost $460,000
9 Total Profit (Loss) $0

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Management Science Revolution
 According to Irv Lustig of ILOG Inc., solution methods
developed today are 10, 000 times faster than the ones
used 15 years ago.
 Quantitative methods are especially helpful with large,
complex problems. For example, in the coordination of
thousands of tasks associated with landing Apollo 11
safely on the moon, quantitative techniques helped to
ensure that more than 300, 000 pieces of work performed
by more than 400,000 people were integrated smoothly.
 Herbert A. Simon, a Nobel Prize winner in economics
and an expert in decision making, said that a
mathematical model does not have to be exact; it just has
to be close enough to provide better results than can be
obtained by common sense.

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End of Chapter 1

1-2 Introduction to MS.pdf

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