BU346
Dr. Kydd
Assignment 4
1.
A company has budgeted $5 million for the coming fiscal year to be allocated among
its plants in Akron, Baltimore and Chicago. The $5 million is to be allocated in $1
million block amounts, with a maximum of $4 million going
BU346
Dr. Kydd
Assignment 1
The executives of the General Products Company (GPC) have to decide which of
three products to introduce, A, B, or C. Product C is essentially a risk-free proposition,
from which the company will obtain a net profit of $1 milli
?
Probability Theory
Random experiment or process
Process that results in 1 of many possible
_ where each outcome is predicted
by a probability
Sample space
Set of all possible outcomes in a _
process
Probability
# favorable outcomes/_ # of all
possible o
BU346
Dr. Kydd
Assignment 3
1.
The Relax-and-Enjoy Lake Development Corporation is developing a lakeside
community at a privately owned lake and is in the business of selling property for
vacation and/or retreat cottages. The primary market for these lake
Chapter 13 Simulation
Learning Objectives
1. 2. 3. 4. Understand what simulation is and how it aids in the analysis of a problem. Learn why simulation is a significant problem-solving tool. Understand the difference between static and dynamic simulation.
FINAL PAPER/PRESENTATION GUIDELINES
BUAD346
I. Statement of the Problem/Decision (20 pts.)
1-2 pages
Give a brief description of the problem or decision that you face. If appropriate,
lay out the options as you see them.
II. Description of the Proposed me
? Quick Review of Discrete and
Continuous Distributions
Random Variable
Discrete
Continuous
Discrete _ Distribution
Binomial
Continuous Probability Distribution
Normal (and _ Approximation
to Binomial)
?
Random Variable
Variable that takes on a set of uni
BU346
Dr. Kydd
Assignment 2
A farmer is attempting to decide which of three crops he should plant on his onehundred-acre farm. The profit from each crop is strongly dependent on the rainfall
during the growing season. He has categorized the amount of rain
Agenda for This Week
Wednesday, April 20
Dynamic
Programming Case 3 Due
Dynamic
Friday, April 22 Monday, April 25 Wednesday, April 27
Programming
AHP AHP
Chapter 18
Dynamic Programming Part 2
Dynamic Programming
1.
Backward Recursion
Determine optimal pol
Bayes Theorem
_ Table
?
2nd letter
1st letter
A
B
Total
C
200
50
250
D
160
190
350
E
240
160
400
Total
600
400
1000
?
Bayes Theorem
Joint Probability table
2nd letter
1st letter
A
B
Total
C
.20
.05
.25
D
.16
.19
.35
E
.24
.16
.40
Total
.60
.40
1.00
Find t
BUAD Exam I
Probability Theory
Random Experiment or process
Process that results in 1 of many possible outcomes where each outcome is predicted by
a probability
Sample Space
Set of all possible outcomes in a random process
Probability
# favorable outcomes
?
Utility Theory
1. Suppose that you live in a floodplain and
stand to lose $100,000 if a flood occurs.
The probability of a flood is .001 in a
given year. Flood insurance costs $300.
What do you do? Explain.
EMV = $100
?
Utility Theory
2. Suppose you win
?
Utility Theory
Utility can be _ many ways:
U(x) = +
x
U(x) = log (x)
x
U(x)
100
4.0
250
6.0
500
7.5
800
8.5
?
Utility Theory
Certainty Equivalent for Losses:
Amount you would pay for certain in
order to avoid having to play the game or
gamble
Los (.5)
$
? Sequential Decision Making
Decision trees are very useful when there are
multiple decisions to be made and they follow a
sequence in time. There are also usually multiple
sets of states.
sion 1 CP
Decision 1
Decision 2
Decision 3
Deci
Decision 2
e1
t
a
?
?
?
Goal Programming
Priority levels
Different levels for goals that give
relative _ to the various
goals (in addition to weights)
P1
P2
Represents highest priority
Represents next highest
priority; _ next
nex
long as there is no reduc
redu
in achieveme
?
?
?
Goal Programming
Mathematical model similar to Linear
Programming, however it allows for
multiple goals to be satisfied at the same
time. Also the multiple goals can be
prioritized and weighted to account for the
DMs utility for meeting the various
Agenda for This Week
Monday, April 18 Wednesday, April 20
Dynamic Dynamic
Programming
Programming Case 3 Due
Dynamic
Friday, April 22 Monday, April 25
Programming
AHP
Chapter 18
Dynamic Programming Part 1
Dynamic Programming
Dynamic
Programming (DP) is a
Agenda for This Week
Monday April 11 Wednesday, April 13 Friday, April 15 Monday, April 18
Case
2 due Markov Processes Markov Processes (HWs) Final Project Topic Due Case 3 Review Dynamic Programming Dynamic Programming
Chapter 17
Markov Processes Part 3
Chapter 13
Simulation Part 2
Brew-Thru Example
Service entrance Car 3 Car 2
Service lane Car 1 Exit
Waiting area
Note: Customers balk if more than 4 cars in line
Monte Carlo Simulation
1. Set up probability distributions for the exogenous variables 2. Bui
Chapter 13
Simulation
Background
Simulation
is one of the most frequently
employed management science
techniques.
It is typically used to model random
processes that are too complex to be
solved by analytical methods.
Advantages
Simulation is relatively
Chapter 1
Introduction to Decision Making
Decision Analysis
Science?
".prescriptive approach designed for normally intelligent people who want to think hard and systematically about some important real problems." - Keeney and Raiffa, 1976 ".when should w
BUAD346 Final Paper/Presentation Each student is required to write and present a paper which applies one of the decision modeling techniques discussed in class to a particular problem or area of interest (in Operations Management or another field of conce
BUAD 346 - Analysis of Operations Problems
Instructor: Susan Murphy Email: [email protected] Office: 012 Purnell Hall Phone: 302.831.4672
BUAD 346 Objectives: In the "real world," typical business problems do not come neatly packaged and labeled. Su
Chapter 18 Dynamic Programming
Learning Objectives
1. 2. 3. Understand the basics of dynamic programming and its approach to problem solving. Learn the general dynamic programming notation. Be able to use the dynamic programming approach to solve problems
Chapter 17
Markov Processes
Learning Objectives
1.
Learn about the types of problems that can be modeled as Markov processes.
2.
Understand the Markov process approach to the market share or brand loyalty problem.
3.
Be able to set up and use the transiti
Chapter 15 Multicriteria Decision Problems
Learning Objectives
1. Understand the concept of multicriteria decision making and how it differs from situations and procedures involving a single criterion. Be able to develop a goal programming model of a mult