OMIS 355 Case Study
Finding the Optimal Distribution Strategies
The San Marcau Company produces and distributes meter used to measure electric
power consumption. The company started with a small production plant in El Paso and
gradually built a customer b
Chapter5
NetworkModeling
1
Agenda
Previous
LP, LP sensitivity analysis, solver
This Chapter: two sessions
Session 1: Transportation Problem (today, EX); Chap 5.1
Session 2:
A quiz on Chap 4
Assignment Problem, Transshipment Problem, and extensions;
Chap 5
0.925
Pass
Test Z
1
Test Y
0
2
5.97
6.15
6.15
2.4
6.15
0.075
Fail
0.875
Pass
3.75
0
3.75
2
0
5.87
0.86
Pass
Test Y
1
Test Z
0
6.15
6.15
2
6.15
Test X
2.4
1.75
5.87
5.355
0.14
Fail
4.15
0
4.15
0.125
Fail
1.75
0
1.75
0.875
Pass
Test Z
1
Test Y
0
1.75
5.85
6
Probabilities
Successful Response
Unsuccessful Response
Total
Joint Probabilities
High Demand
Low Demand
0.500
0.100
0.200
0.200
0.700
0.300
Successful Response
Unsuccessful Response
Conditional Probabilities
For A Given Survey Response
High Demand
Low De
Plan A
0.4
Approved
-137.5
-129.5
-137.5
2
0
-99
Plan B
Seek Approval
-99
-91
-8
-99
-122.1
0.6
Denied
Plan A
1
0
-137.5
-137.5
-129.5
-137.5
Plan A
-129.5
1
-129.5
-129.5
-122.1
Plan A
0.4
Approved
-244
-151
-244
2
-5
-96
Plan B
Plan B
-96
-3
-88
-96
-18
Probabilities
Approve
Reject
Total
Joint Probabilities
Pay
Default
0.720
0.030
0.180
0.070
0.900
0.100
Approve
Reject
Conditional Probabilities
For A Given Customer Type
Pay
Default
0.800
0.300
0.200
0.700
Approve
Reject
Conditional Probabilities
For A Gi
Price
X
Y
Z
Sum
Pairwise Comparisons
X
Y
Z
1.000
0.250
3.000
4.000
1.000
7.000
0.333
0.143
1.000
5.333
1.393
11.000
X
Y
Z
Normalized Comparisons
X
Y
Z
0.188
0.179
0.273
0.750
0.718
0.636
0.063
0.103
0.091
Support Consistency
Score
Measure
0.213
3.023
0.70
Leadership
X
Y
Z
Sum
Pairwise Comparisons
X
Y
Z
1.000
3.000
4.000
0.333
1.000
2.000
0.250
0.500
1.000
1.583
4.500
7.00
X
Y
Z
Normalized Comparisons
X
Y
Z
0.632
0.667
0.571
0.211
0.222
0.286
0.158
0.111
0.143
Price
Scores
0.623
0.239
0.137
Consistency Rati
0.925
Pass
Test Z
1
Test Y
0
2
5.97
6.15
6.15
2.4
6.15
0.075
Fail
0.875
Pass
3.75
0
3.75
2
0
5.87
0.86
Pass
Test Y
1
Test Z
0
6.15
6.15
2
6.15
Test X
2.4
1.75
5.87
5.355
0.14
Fail
4.15
0
4.15
0.125
Fail
1.75
0
1.75
0.875
Pass
Test Z
1
Test Y
0
1.75
5.85
6
Probabilities
Joint Probabilities
Forecasted Demand
Low
Medium
High
Actual Demand
Low
Medium
High
0.1600
0.0300
0.0100
0.0350
0.2800
0.0350
0.0225
0.0450
0.3825
Conditional Probabilities For
Given Forecasted Demands
Forecasted Demand
Low
Medium
High
Actua
Chapter 3
Modeling & Solving LP Problems In A Spreadsheet
1.
In general, it does not matter what is placed in a variable (changing) cell. Ultimately, Solver will determine the
optimal values for these cells. If the model builder places formulas in changin
Maximax
Payoffs (in $1,000s) Amount of Snow: Heavy Normal $10 $7 $8 $8 $4 $4
Size of Order: Large Medium Small
Light $3 $6 $4
Maximum $10 <-maximum $8 $4
Page 1
Maximin
Payoffs (in $1,000s) Amount of Snow: Heavy Normal $10 $7 $8 $8 $4 $4
Size of Order: La
Payoffs
Payoff Matrix (in $1,000s)
Size of Development
Small
Medium
Large
Low
400
200
-400
Market Demand
Medium
400
500
300
High
400
500
800
Page 1
Utilities
Payoff Matrix (in utility)
Size of Development
Small
Medium
Large
Probability:
Risk Tolerance:
Ma
Payoffs
Payoff Matrix (in $1,000s)
Size of Development
Small
Medium
Large
Low
400
200
-400
Market Demand
Medium
400
500
300
High
400
500
800
Page 1
Maximax
Payoff Matrix (in $1,000s)
Size of Development
Small
Medium
Large
Low
400
200
-400
Market Demand
Me