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Program Name Source Content1.3 Pritchett Clock Repair Shop Excel QM Breakeven Analysis1.4 Pritchett Clock Repair Shop Excel QM Goal Seek2.1 Expected Value and Variance Excel Expected Value and Variance2.2 Binomial Probabilities Excel Binomial Probabilities3.1 Thompson Lumber Excel QM Decision Table3.5 Bayes Theorem for Thompson Lumber Example Excel Bayes Theorem4.1 Triple A Construction Company Sales Excel QM Regression4.2 Jenny Wilson Realty Excel QM Multiple Regression4.3 Jenny Wilson Realty Excel QM Dummy Variables - Regression4.4 MPG Data Excel QM Linear Regression4.5 MPG Data Excel QM Nonlinear Regression4.6 Solved Problem 4-2 Excel Regression5.1 Wallace Garden Supply Shed Sales Excel QM Weighted Moving Average5.2 Port of Baltimore Excel QM Exponential Smoothing5.3 Midwestern Manufacturing's Demand Excel Trend Analysis5.4 Midwestern Manufacturing's Demand Excel QM Trend Analysis5.6 Turner Industries Excel Regression6.1 Sumco Pump Company Excel QM EOQ Model6.2 Brown Manufacturing Excel QM Production Run Model6.3 Brass Department Store Excel QM Quantity Discount Model7.2 Flair Furniture Excel Linear Programming7.4 Holiday Meal Turkey Ranch Excel Linear Programming7.6 High note sound company Excel Linear Programming8.1 Win Big Gambling Club Excel Linear Programming8.3 Fifth Avenue Industries Excel Linear Programming8.5 Top Speed Bicycle Company Excel Linear Programming8.6 Goodman Shipping Excel Linear Programming9.1 High note sound company Excel Linear Programming9.2 Manufacturing Example Excel Linear Programming
10.1 Executive Furniture Company Excel QM Transportation10.2 Birmingham Plant Excel QM Transportation10.3 Fix-It Shop Assignment Excel QM Assignment11.2 Harrison Electric IP Analysis Excel Integer programming11.4 Bagwell Chemical Company Excel Integer programming11.5 Simkin, Simkin and Steinberg Excel Integer programming11.7 Great Western Appliance Excel Nonlinear programming11.8 Hospicare Corp Excel Nonlinear programming11.9 Thermlock Gaskets Excel Nonlinear programming
11.10 Solved Problem 11-1 Excel 0-1 programming13.1 Crashing General Foundry Problem Excel Crashing14.1 Arnold's Muffler Shop Excel QM Single Server (M/M/1) system14.2 Arnold's Muffler Shop Excel QM Multi-Server (M/M/m) system14.3 Golding Recycling, Inc. Excel QM Constant Service Rate (M/D/1)14.4 Department of Commerce Excel QM Finite population queue15.2 Harry's Tire Shop Excel Simulation (inventory)15.3 Generating Normal Random Numbers Excel Random #s and Frequency15.4 Port of New Orleans Barge Unloadings Excel Simulation (waiting line)15.5 Three Hills Power Company Excel Maintenance Simulation16.4 Three Grocery Example Excel Markov Analysis16.5 Accounts Receivable Example Excel Fundamental Matrix & Absorbing States17.1 ARCO Excel p-Chart Analysis
ModuleM1.1 AHP Excel
M5.1 Matrix Multiplication Excel
Dummy Variables - Regression
Constant Service Rate (M/D/1)
Fundamental Matrix & Absorbing States
Pritchett Clock Repair Shop
Breakeven Analysis
DataRebuilt Springs
Fixed cost 1000Variable cost 5Revenue 10
ResultsBreakeven points
Units 200Dollars $ 2,000.00
GraphUnits Costs Revenue
0 1000 0400 3000 4000
0 2 4 6 8 10 120
2
4
6
8
10
12Cost-volume analysis
Units
$
Enter the fixed and variable costs and the selling price in the data area.Enter the fixed and variable costs and the selling price in the data area.
Pritchett Clock Repair Shop
Breakeven Analysis
DataRebuilt Springs
Fixed cost 1000Variable cost 5Revenue 10.71Volume (optional) 250
ResultsBreakeven points
Units 175Dollars $ 1,875.00
Volume Analysis@ 250 Costs $ 2,250.00 Revenue $ 2,678.57 Profit $ 428.57
GraphUnits Costs Revenue
0 1000 0350 2750 3750
Enter the fixed and variable costs and the selling price in the data area.Enter the fixed and variable costs and the selling price in the data area.
x P(x) xP(x) (x-mean)squared*P(x)10 0.2 2 54.4520 0.25 5 10.562530 0.25 7.5 3.062540 0.3 12 54.675
26.5 122.75Mean Variance
The Binomial Distributionn= 5p= 0.5r= 4
Cumulative probability 0.9688P(r) 0.1563
P(r<_)
Thompson Lumber
Decision Tables
Data Results
Profit EMV Minimum Maximum HurwiczProbability 0.5 0.5 coefficient 0.8Large Plant 200000 -180000 10000 -180000 200000 124000Small plant 100000 -20000 40000 -20000 100000 76000Do nothing 0 0 0 0 0
Maximum 40000 0 200000 124000
Expected Value of Perfect InformationColumn best 200000 0 100000 <-Expected value under certainty
40000 <-Best expected value60000 <-Expected value of perfect information
RegretFavorable MUnfavorable Market Expected Maximum
Probability 0.5 0.5Large Plant 0 180000 90000 180000Small plant 100000 20000 60000 100000Do nothing 200000 0 100000 200000
Minimum 60000 100000
Favorable Market
Unfavorable Market
Enter the profits or costs in the main body of the data table. Enter probabilities in the first row if you want to compute the expected value.Enter the profits or costs in the main body of the data table. Enter probabilities in the first row if you want to compute the expected value.
Bayes Theorem for Thompson Lumber Example
Fill in cells B7, B8, and C7
Probability Revisions Given a Positive Survey
State of Nature P(Sur.Pos.|state of nature) Prior Prob. Joint Prob.FM 0.7 0.5 0.35 0.78UM 0.2 0.5 0.1 0.22
P(Sur.pos.)= 0.45
Probability Revisions Given a Negative Survey
State of Nature P(Sur.Pos.|state of nature) Prior Prob. Joint Prob.FM 0.3 0.5 0.15 0.27UM 0.8 0.5 0.4 0.73
P(Sur.neg.)= 0.55
Posterior Probability
Posterior Probability
Triple A Construction Co SUMMARY OUTPUT
Sales (Y)Payroll (X) Regression Statistics
6 3 Multiple R 0.833333
8 4 R Square 0.694444
9 6 Adjusted R 0.618056
5 4 Standard E 1.311011
4.5 2 Observatio 6
9.5 5ANOVA
df SS MS F Significance F
Regression 1 15.625 15.625 9.090909 0.039352
Residual 4 6.875 1.71875
Total 5 22.5
CoefficientsStandard Error t Stat P-value Lower 95%
Intercept 2 1.742544 1.147747 0.31505 -2.838077
Payroll (X) 1.25 0.414578 3.015113 0.039352 0.098947
Significance F
Upper 95%Lower 95.0%Upper 95.0%
6.838077 -2.838077 6.838077
2.401053 0.098947 2.401053
SELL PRICE SF AGE35000 1926 3047000 2069 4049900 1720 3055000 1396 1558900 1706 3260000 1847 3867000 1950 2770000 2323 3078500 2285 2679000 3752 3587500 2300 1893000 2525 1795000 3800 4097000 1740 12
SUMMARY OUTPUT
Regression StatisticsMultiple R 0.81968R Square 0.67188Adjusted R Sq 0.61222Standard Erro 12156.3Observations 14
ANOVAdf SS MS F Significance F
Regression 2 3328484242 1.66E+09 11.26195 0.002179Residual 11 1625532901 1.48E+08Total 13 4954017143
CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%Intercept 60815.4 12741.04143 4.773193 0.000578 32772.6 88858.29 32772.6 88858.29SF 21.9097 5.140482535 4.262184 0.001338 10.59556 33.22381 10.59556 33.22381AGE -1449.34 398.282471 -3.638983 0.003895 -2325.957 -572.7293 -2325.957 -572.7293
Upper 95.0%
SELL PRI SF AGE X3(Exc) X4(Mint) Condition35000 1926 30 0 0 Good47000 2069 40 1 0 Excellent49900 1720 30 1 0 Excellent55000 1396 15 0 0 Good58900 1706 32 0 1 Mint60000 1847 38 0 1 Mint67000 1950 27 0 1 Mint70000 2323 30 1 0 Excellent78500 2285 26 0 1 Mint79000 3752 35 0 0 Good87500 2300 18 0 0 Good93000 2525 17 0 0 Good95000 3800 40 1 0 Excellent97000 1740 12 0 1 Mint
SUMMARY OUTPUT
Regression StatisticsMultiple R 0.947618R Square 0.89798Adjusted R 0.852637Standard E 7493.777Observatio 14
ANOVAdf SS MS F Significance F
Regression 4 4.45E+09 1.11E+09 19.80444 0.000174Residual 9 5.05E+08 56156698Total 13 4.95E+09
CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%Intercept 48329.23 8713.307 5.5466 0.000358 28618.36 68040.1 28618.36 68040.1SF 28.2138 3.473758 8.121981 1.96E-05 20.35561 36.07199 20.35561 36.07199AGE -1981.41 298.0139 -6.648716 9.39E-05 -2655.564 -1307.256 -2655.564 -1307.256X3(Exc) 16581.32 6089.81 2.722798 0.0235 2805.216 30357.43 2805.216 30357.43X4(Mint) 23684.62 5324.635 4.448122 0.001605 11639.46 35729.78 11639.46 35729.78
Upper 95.0%
Automobile Weight vs. MPG SUMMARY OUTPUT
MPG (Y) Weight (X1) Regression Statistics12 4.58 Multiple R 0.8628813 4.66 R Square 0.7445615 4.02 Adjusted R 0.7190218 2.53 Standard E 5.0075719 3.09 Observatio 1219 3.1120 3.18 ANOVA23 2.68 df SS MS F Significance F24 2.65 Regression 1 730.909 730.909 29.14802 0.00030233 1.70 Residual 10 250.7577 25.0757736 1.95 Total 11 981.666742 1.92
CoefficientsStandard Error t Stat P-value Lower 95%Intercept 47.6193 4.813151 9.89359 1.75E-06 36.89498Weight (X1 -8.24597 1.527345 -5.398891 0.000302 -11.64911
Significance F
Upper 95%Lower 95.0%Upper 95.0%58.34371 36.89498 58.34371
-4.842833 -11.64911 -4.842833
Automobile Weight vs. MPG SUMMARY OUTPUT
MPG (Y) Weight (X1) WeightSq.(X2) Regression Statistics12 4.58 20.98 Multiple R 0.920813 4.66 21.72 R Square 0.847815 4.02 16.16 Adjusted R 0.814018 2.53 6.40 Standard E 4.074519 3.09 9.55 Observatio 1219 3.11 9.6720 3.18 10.11 ANOVA23 2.68 7.18 df SS MS F Significance F24 2.65 7.02 Regression 2 832.2557 416.1278 25.0661 0.00020933 1.70 2.89 Residual 9 149.411 16.6012236 1.95 3.80 Total 11 981.666742 1.92 3.69
CoefficientsStandard Error t Stat P-value Lower 95%Intercept 79.7888 13.5962 5.8685 0.0002 49.0321Weight (X1 -30.2224 8.9809 -3.3652 0.0083 -50.5386WeightSq.( 3.4124 1.3811 2.4708 0.0355 0.2881
Significance F
Upper 95%Lower 95.0%Upper 95.0%110.5454 49.0321 110.5454
-9.9062 -50.5386 -9.90626.5367 0.2881 6.5367
Solved Problem 4-2
Advertising ($100) Y Sales X11 5
6 310 7
6 212 8
SUMMARY OUTPUT
Regression StatisticsMultiple R 0.9014R Square 0.8125Adjusted R Square 0.7500Standard Error 1.4142Observations 5
ANOVAdf SS MS F Significance F
Regression 1 26 26 13 0.036618Residual 3 6 2Total 4 32
CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Intercept 4 1.5242 2.6244 0.0787 -0.8506 8.8506 -0.8506Sales X 1 0.2774 3.6056 0.0366 0.1173 1.8827 0.1173
Upper 95.0%8.85061.8827
Wallace Garden Supply Shed Sales
Forecasting Weighted moving averages 3 period moving average
Data Error analysisPeriod Demand Weights Forecast Error Absolute SquaredJanuary 10 1February 12 2March 13 3April 16 12.16667 3.833333 3.833333 14.69444May 19 14.33333 4.666667 4.666667 21.77778June 23 17 6 6 36July 26 20.5 5.5 5.5 30.25August 30 23.83333 6.166667 6.166667 38.02778September 28 27.5 0.5 0.5 0.25October 18 28.33333 -10.33333 10.33333 106.7778November 16 23.33333 -7.333333 7.333333 53.77778December 14 18.66667 -4.666667 4.666667 21.77778
Total 4.333333 49 323.3333Average 0.481481 5.444444 35.92593
Bias MAD MSESE 6.796358
Next period 15.3333333
Enter the data in the shaded area. Enter weights in INCREASING order from top to bottom.
Enter the data in the shaded area. Enter weights in INCREASING order from top to bottom.
Port of Baltimore
Forecasting Exponential smoothing
Alpha 0.1Data Error AnalysisPeriod Demand Forecast Error Absolute SquaredQuarter 1 180 175 5 5 25Quarter 2 168 175.5 -7.5 7.5 56.25Quarter 3 159 174.75 -15.75 15.75 248.0625Quarter 4 175 173.175 1.825 1.825 3.330625Quarter 5 190 173.3575 16.6425 16.6425 276.97281Quarter 6 205 175.0218 29.97825 29.97825 898.69547Quarter 7 180 178.0196 1.980425 1.980425 3.9220832Quarter 8 182 178.2176 3.782382 3.782382 14.306417
Total 35.95856 82.45856 1526.5399Average 4.49482 10.30732 190.81749
Bias MAD MSESE 15.950653
Next period 178.595856
Enter alpha (between 0 and 1) then enter the past demands in the shaded area.Enter alpha (between 0 and 1) then enter the past demands in the shaded area.
Midwestern Manufacturing
Time (X) Demand (Y)1 742 793 804 905 1056 1427 122
SUMMARY OUTPUT
Regression StatisticsMultiple R 0.8949096R Square 0.8008632Adjusted R 0.7610359Standard E 12.432389Observatio 7
ANOVAdf SS MS F Significance F
Regression 1 3108.0357 3108.036 20.10837 0.0064933Residual 5 772.82143 154.5643Total 6 3880.8571
CoefficientsStandard Error t Stat P-value Lower 95% Upper 95%Lower 95.0%Upper 95.0%Intercept 56.71429 10.50729 5.39762 0.00295 29.70445 83.72412 29.70445 83.72412Time (X) 10.53571 2.34950 4.48424 0.00649 4.49613 16.57530 4.49613 16.57530
Upper 95.0%
Midwestern Manufacturing's Demand
Forecasting Regression/Trend analysis
Data Error analysisPeriod Demand (y) Period(x) Forecast Error Absolute Squared1993 74 1 67.25 6.75 6.75 45.56251994 79 2 77.78571 1.214286 1.2142857 1.474491995 80 3 88.32143 -8.321429 8.3214286 69.246171996 90 4 98.85714 -8.857143 8.8571429 78.448981997 105 5 109.3929 -4.392857 4.3928571 19.297191998 142 6 119.9286 22.07143 22.071429 487.1481999 122 7 130.4643 -8.464286 8.4642857 71.64413
Total 0.00 60.071429 772.8214Intercept 56.7142857 Average 0.00 8.5816327 110.4031Slope 10.5357143 Bias MAD MSE
SE 12.43239Next period 141 8
Correlation 0.89491
If this is trend analysis then simply enter the past demands in the demand column. If this is causal regression then enter the y,x pairs with y first and enter a new value of x at the bottom in order to forecast y.
If this is trend analysis then simply enter the past demands in the demand column. If this is causal regression then enter the y,x pairs with y first and enter a new value of x at the bottom in order to forecast y.
Year Quarter SalesX1 Time PeriodX2 Qtr 2 X3 Qtr 3 X4 Qtr 41 1 108 1 0 0 0
2 125 2 1 0 03 150 3 0 1 04 141 4 0 0 1
2 1 116 5 0 0 02 134 6 1 0 03 159 7 0 1 04 152 8 0 0 1
3 1 123 9 0 0 02 142 10 1 0 03 168 11 0 1 04 165 12 0 0 1
SUMMARY OUTPUT
Regression StatisticsMultiple R 0.99718R Square 0.99436Adjusted R 0.99114Standard E 1.83225Observatio 12
ANOVAdf SS MS F Significance F
Regression 4 4144.75 1036.188 308.6516 6.03E-08Residual 7 23.5 3.357143Total 11 4168.25
CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%Intercept 104.104 1.332194 78.14493 1.48E-11 100.954 107.2543 100.954 107.2543X1 Time Pe 2.3125 0.16195 14.27913 1.96E-06 1.92955 2.69545 1.92955 2.69545X2 Qtr 2 15.6875 1.504767 10.4252 1.62E-05 12.12929 19.24571 12.12929 19.24571X3 Qtr 3 38.7083 1.530688 25.28819 3.86E-08 35.08883 42.32784 35.08883 42.32784X4 Qtr 4 30.0625 1.572941 19.11228 2.67E-07 26.34308 33.78192 26.34308 33.78192
Sumco Pump Company
Inventory Economic Order Quantity Model
DataDemand rate, D 1000Setup cost, S 10Holding cost, H 0.5 (fixed amount)Unit Price, P 0
ResultsOptimal Order Quantity, Q* 200Maximum Inventory 200Average Inventory 100Number of Setups 5
Holding cost $50.00 Setup cost $50.00
Unit costs $0.00
$100.00
COST TABLE Start at 25 Increment 15
Q Setup cost Holding cosTotal cost25 400 6.25 406.2540 250 10 26055 181.8182 13.75 195.568270 142.8571 17.5 160.357185 117.6471 21.25 138.8971
100 100 25 125115 86.95652 28.75 115.7065130 76.92308 32.5 109.4231145 68.96552 36.25 105.2155160 62.5 40 102.5175 57.14286 43.75 100.8929190 52.63158 47.5 100.1316205 48.78049 51.25 100.0305220 45.45455 55 100.4545235 42.55319 58.75 101.3032250 40 62.5 102.5265 37.73585 66.25 103.9858280 35.71429 70 105.7143295 33.89831 73.75 107.6483310 32.25806 77.5 109.7581325 30.76923 81.25 112.0192340 29.41176 85 114.4118355 28.16901 88.75 116.919370 27.02703 92.5 119.527
Total cost, Tc 0
2
4
6
8
10
12
Inventory: Cost vs Quantity
Order Quantity (Q)
Co
st (
$)
Enter the data in the shaded areaEnter the data in the shaded area
Brown Manufacturing
Inventory Production Order Quantity Model
DataDemand rate, D 10000Setup cost, S 100Holding cost, H 0.5 (fixed amount)Daily production rate, p 80Daily demand rate, d 60Unit price, P 0
ResultsOptimal production quantity, Q* 4000Maximum Inventory 1000Average Inventory 500Number of Setups 2.5
Holding cost 250Setup cost 250
Unit costs 0
500
COST TABLE Start at 1000 Increment 333.3333
Q Setup cost Holding cosTotal cost1000 1000 62.5 1062.5
1333.333 750 83.33333 833.33331666.667 600 104.1667 704.1667
2000 500 125 6252333.333 428.5714 145.8333 574.40482666.667 375 166.6667 541.6667
3000 333.3333 187.5 520.83333333.333 300 208.3333 508.33333666.667 272.7273 229.1667 501.8939
4000 250 250 5004333.333 230.7692 270.8333 501.60264666.667 214.2857 291.6667 505.9524
5000 200 312.5 512.55333.333 187.5 333.3333 520.83335666.667 176.4706 354.1667 530.6373
6000 166.6667 375 541.66676333.333 157.8947 395.8333 553.72816666.667 150 416.6667 566.6667
7000 142.8571 437.5 580.35717333.333 136.3636 458.3333 594.6977666.667 130.4348 479.1667 609.6014
8000 125 500 6258333.333 120 520.8333 640.83338666.667 115.3846 541.6667 657.0513
Total cost, Tc
0
2
4
6
8
10
12
Inventory: Cost vs Quantity
Order Quantity (Q)C
ost
($)
Enter the data in the shaded area. You may have to do some work to enter the daily production rate.Enter the data in the shaded area. You may have to do some work to enter the daily production rate.
0
2
4
6
8
10
12
Inventory: Cost vs Quantity
Order Quantity (Q)
Co
st (
$)
Enter the data in the shaded area. You may have to do some work to enter the daily production rate.Enter the data in the shaded area. You may have to do some work to enter the daily production rate.
Brass Department Store
Inventory Quantity Discount Model
DataDemand rate, D 5000Setup cost, S 49Holding cost %, I 20%
Range 1 Range 2 Range 3Minimum quantity 0 1000 2000Unit Price, P 5 4.8 4.75
ResultsRange 1 Range 2 Range 3
Q* (Square root formula) 700 714.4345083118 718.18484646Order Quantity 700 1000 2000
Holding cost $350.00 $480.00 $950.00 Setup cost $350.00 $245.00 $122.50
Unit costs $25,000.00 $24,000.00 $23,750.00
$25,700.00 $24,725.00 $24,822.50 minimumOptimal Order Quantity 1000Total cost, Tc
=
$24,725.00
Flair Furniture
Tables Chairs SlackObjective function 70 50 4100Carpentry 4 3 240 <= 240 0Painting 2 1 100 <= 100 0
Solution Values 30 40
Left Hand Side
Right Hand Side
Holiday Meal Turkey Ranch
Brand 1 Brand 2 SurplusObjective function 2 3 31.2Ingredient A 5 10 90 >= 90Ingredient B 4 3 48 >= 48 0Ingredient C 0.5 0 4.2 >= 1.5 2.7
Solution Values 8.4 4.8
Left Hand Side
Right Hand Side
High note sound company
CD PlayersReceiversValue 0 20
TotalProfit 50 120 2400
Used Sign AvailableElectrician hours 2 4 80 <= 80Audio technician hours 3 1 20 <= 60
Win Big Gambling Club
Solution 1.96875 5 6.20689655 0Variables X1 X2 X3 X4Audience reached per ad 5000 8500 2400 2800Maximum TV 1Maximum Newspaper 1Maximum 30-second radio 1Maximum 1 min. radio 1Cost per ad 800 925 290 380Radio dollars 290 380Radio spots 1 1
1 minute TV spots
newspaper ads
30 second radio spots
1 minute radio spots
RHS67240.302
1.96875 <= 125 <= 5
6.2068966 <= 250 <= 20
8000 <= 80001800 <= 1800
6.2068966 >= 5
Fifth Avenue Industries
Variety silk polyester cottonAll silk 6400 6.7 6000 7000 0.125 100%
All polyester 14000 3.55 10000 14000 0.08 100%
16000 4.31 13000 16000 0.1 50% 50%
8500 4.81 6000 8500 0.1 30% 70%
Total revenue 202425 800 2175 1395
Material Cost Available UsedSilk 21 800 800Polyester 6 3000 2175Cotton 9 1600 1395
Total Cost 42405
Total Profit 160020
Number (X)
Selling price
Monthly minimum
Monthly demand
Material (yards)
Poly-cotton blend 1
Poly-cotton blend 2
Top Speed Bicycle Company
Transportation
DataCOSTS New York Chicago Los Angel SupplyNew Orleans 2 3 5 20000 Omaha 3 1 4 15000 Demand 10000 8000 15000 33000 \ 35000
ShipmentsShipments New York Chicago Los Angel Row Total New Orleans 10000 0 8000 18000Omaha 0 8000 7000 15000Column Total 10000 8000 15000 33000 \ 33000 Total Cost 96000
Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.
Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.
Goodman Shipping
Item Value ($) weight (lbs)1 0.333333 1 22500 75002 1 1 24000 75003 0 1 8000 30004 0 1 9500 35005 0 1 11500 40006 0 1 9750 3500
Total $ 31,500 10000
Weight Capacity 10000
Percent loaded
Max percent loaded
High note sound company
CD PlayersReceiversValue 0 20
TotalProfit 50 120 2400
Used Sign AvailableElectrician hours 2 4 80 <= 80Audio technician hours 3 1 20 <= 60
Manufacturing Example
mower blowervariable-> 100 200
Total profitprofit 30 80 19000
used availablelabor hours 2 4 1000 < 1000steel (lbs) 6 2 1000 < 1200snowblower engines 1 200 < 200
Executive Furniture Company
Transportation
DataCOSTS Albuquerq Boston Cleveland SupplyDes Moines 5 4 3 100 Evansville 8 4 3 300 Fort Lauderdale 9 7 5 300 Demand 300 200 200 700 \ 700
ShipmentsShipments Albuquerq Boston Cleveland Row Total Des Moines 100 0 0 100Evansville 0 200 100 300Fort Lauderdale 200 0 100 300Column Total 300 200 200 700 \ 700 Total Cost 3900
Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.
Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.
Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.
Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.
Birmingham Plant
Transportation
DataCOSTS Detroit Dallas New York Los Angel SupplyCincinnati 73 103 88 108 15000 Salt Lake 85 80 100 90 6000 Pittsburgh 88 97 78 118 14000 Birmingha 84 79 90 99 11000 Demand 10000 12000 15000 9000 46000 \ 46000
ShipmentsShipments Detroit Dallas New York Los Angel Column Tot Cincinnati 10000 0 1000 4000 15000Salt Lake 0 1000 0 5000 6000Pittsburgh 0 0 14000 0 14000Birmingha 0 11000 0 0 11000Column Tot 10000 12000 15000 9000 46000 \ 46000 Total Cost 3741000
Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.
Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.
Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.
Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.
Fix-It Shop Assignment
Fix-It Shop Assignment
Assignment
DataCOSTS Project 1 Project 2 Project 3Adams 11 14 6 Brown 8 10 11 Cooper 9 12 7
AssignmentsShipments Project 1 Project 2 Project 3 Row Total Adams 0 0 1 1 Brown 0 1 0 1 Cooper 1 0 0 1 Column Total 1 1 1 3 Total Cost 25
Enter the assignment costs in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS. If SOLVER is not an addin option then reinstall Excel.
Enter the assignment costs in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS. If SOLVER is not an addin option then reinstall Excel.
Harrison Electric IP Analysis
Chandeliers FansSolution 5 0
TotalProfit 7 6 35
Used Sign Limitwiring hours 2 3 10 < 12assembly hours 6 5 30 < 30
Bagwell Chemical Company
xyline (bags) hexall (lbs)value 44 20
profit 85 1.5 3770used sign available
ingredient a 30 0.5 1330 <= 2000ingredient b 18 0.4 800 <= 800ingredient c 2 0.1 90 <= 200
Simkin, Simkin and Steinberg
Stock Company Name Invest Return Cost1 Trans-Texas Oil 0 50 4802 British Petroleum 0 80 5403 Dutch Shell 1 90 6804 Houston Drilling 1 120 10005 Texas Petroleum 1 110 7006 San Diego Oil 1 40 5107 California Petro 0 75 900
Total 360 2890Limit 3000
BoundTexas Constraint 2 >= 2Foreign oil constraint 1 <= 1California Constraint 1 = 1
Great Western Appliance
Microtoast Self-clean TotalNumber 0 1000 1000 < 1000Profit 0 271000 $ 271,000.00
used Sign capacityHours 0.5 0.4 400 < 500
Hospicare Corpx1 x2
value 6.066259 4.100253
terms x1 x1^2 x1*x2 x2 x2^3 1/x2values 6.066259 36.79949 24.87319 4.100253 68.93374 0.243887
totalrevenue 13 6 5 1 248.846
constraint 1 2 4 90 < 90constraint 2 1 1 75 < 75constraint 3 8 -2 40.3296 < 61
Thermlock Gaskets
x1 x2value 3.325326 14.67227
totalcost 5 7 119.3325
constraintsx1 x1^2 x1^3 x2 x2^2
value 3.325326 11.05779 36.77076 14.67227 215.2756 TotalConstraint 1 3 0.25 4 0.3 136.0122 > 125Constraint 2 13 1 80 > 80Constraint 3 0.7 1 17 > 17
0-1 integer Program
x1 x2 x3values 1 1 0
totalmaximize 50 45 48 95
Limitconstraint 19 27 34 46 < 80
22 13 12 35 < 401 1 1 2 < 2
Crashing General Foundry ProblemYA YB YC YD YE YF YG YH XST XA XB XC XD XE XF XG XH XFIN
Values 0 0 1 0 0 0 2 0 0 2 3 3 7 7 6 10 12 12Minimize cost 1000 2000 1000 1000 1000 500 2000 3000A crash max. 1B crash max. 1C crash max. 1D crash max. 1E crash max. 1F crash max. 1G crash max. 1H crash max. 1Due date 1Start 1A constraint 1 -1 1B constraint 1 -1 1C constraint 1 -1 1D constraint 1 -1 1E constraint 1 -1 1F constraint 1 -1 1G constraint 1 1 -1 1G constraint 2 1 -1 1H constraint 1 1 -1 1H constraint 2 1 -1 1Finish constraint -1 1
Totals5000
0 < 10 < 21 < 10 < 10 < 20 < 12 < 30 < 1
12 < 120 = 02 > 23 > 32 > 24 > 44 > 43 > 35 > 55 > 56 > 22 > 20 > 0
Arnold's Muffler Shop
Waiting Lines M/M/1 (Single Server Model)
Data Results2 0.666667
3 1.333333Average number of customers in the system(L) 2
0.666667Average time in the system(W) 1
0.333333
ProbabilitiesNumber Probability Cumulative Probability
0 0.333333 0.3333331 0.222222 0.5555562 0.148148 0.7037043 0.098765 0.8024694 0.065844 0.8683135 0.043896 0.9122096 0.029264 0.9414727 0.019509 0.9609828 0.013006 0.9739889 0.008671 0.982658
10 0.005781 0.98843911 0.003854 0.99229312 0.002569 0.99486213 0.001713 0.99657514 0.001142 0.99771615 0.000761 0.99847816 0.000507 0.99898517 0.000338 0.99932318 0.000226 0.99954919 0.000150 0.99969920 0.000100 0.999800
Arrival rate (l) Average server utilization(r)
Service rate (m) Average number of customers in the queue(Lq)
Average waiting time in the queue(Wq)
Probability (% of time) system is empty (P0)
The arrival RATE and service RATE both must be rates and use the same time unit. Given a time such as 10 minutes, convert it to a rate such as 6 per hour.The arrival RATE and service RATE both must be rates and use the same time unit. Given a time such as 10 minutes, convert it to a rate such as 6 per hour.
The arrival RATE and service RATE both must be rates and use the same time unit. Given a time such as 10 minutes, convert it to a rate such as 6 per hour.The arrival RATE and service RATE both must be rates and use the same time unit. Given a time such as 10 minutes, convert it to a rate such as 6 per hour.
Arnold's Muffler Shop
Waiting Lines M/M/s
Data Results2 0.33333
3 0.08333Number of servers(s) 2 Average number of customers in the system(L) 0.75
0.04167Average time in the system(W) 0.375
0.5ProbabilitiesNumber Probability Cumulative Probability
0 0.500000 0.5000001 0.333333 0.8333332 0.111111 0.9444443 0.037037 0.9814814 0.012346 0.9938275 0.004115 0.9979426 0.001372 0.9993147 0.000457 0.9997718 0.000152 0.9999249 0.000051 0.999975
10 0.000017 0.99999211 0.000006 0.99999712 0.000002 0.99999913 0.000001 1.00000014 0.000000 1.00000015 0.000000 1.00000016 0.000000 1.00000017 0.000000 1.00000018 0.000000 1.00000019 0.000000 1.00000020 0.000000 1.000000
Computationsn or s (lam/mu)^nCumsum(n-term2 P0(s)
0 11 0.666667 1 2 0.333332 0.222222 1.666667 0.333333333333333 0.53 0.049383 1.888889 0.0634920634920635 0.51224 0.00823 1.938272 0.00987654320987654 0.513315 0.001097 1.946502 0.00126622348844571 0.513416 0.000122 1.947599 0.000137174211248285 0.513427 1.16E-05 1.947721 1.28350139179682E-05 0.513428 9.68E-07 1.947733 1.05569378546059E-06 0.513429 7.17E-08 1.947734 7.74175442671098E-08 0.51342
10 4.78E-09 1.947734 5.12020795417393E-09 0.5134211 2.9E-10 1.947734 3.08313597240581E-10 0.5134212 1.61E-11 1.947734 1.70369367459144E-11 0.5134213 8.25E-13 1.947734 8.69753527569206E-13 0.5134214 3.93E-14 1.947734 4.12575391282828E-14 0.51342
Arrival rate (l) Average server utilization(r)
Service rate (m) Average number of customers in the queue(Lq)
Average waiting time in the queue(Wq)
Probability (% of time) system is empty (P0)
The arrival RATE and service RATE both must be rates and use the same time unit. Given a time such as 10 minutes, convert it to a rate such as 6 per hour.The arrival RATE and service RATE both must be rates and use the same time unit. Given a time such as 10 minutes, convert it to a rate such as 6 per hour.
15 1.75E-15 1.947734 1.82757648408783E-15 0.5134216 7.28E-17 1.947734 7.59282983727312E-17 0.5134217 2.85E-18 1.947734 2.96998446015785E-18 0.5134218 1.06E-19 1.947734 1.09750556974159E-19 0.5134219 3.71E-21 1.947734 3.84311714656988E-21 0.5134220 1.24E-22 1.947734 1.27871411410371E-22 0.5134221 3.92E-24 1.947734 4.05275511573854E-24 0.5134222 1.19E-25 1.947734 1.22628006974231E-25 0.513422324252627282930
Rho(s) Lq(s) L(s) Wq(s) W(S)
0.666667 1.333333 2 0.666667 10.333333 0.083333 0.75 0.041667 0.3750.222222 0.009292 0.675958 0.004646 0.3379790.166667 0.001014 0.667681 0.000507 0.333840.133333 0.0001 0.666767 5E-05 0.3333830.111111 8.8E-06 0.666675 4.4E-06 0.3333380.095238 6.94E-07 0.666667 3.47E-07 0.3333340.083333 4.93E-08 0.666667 2.46E-08 0.3333330.074074 3.18E-09 0.666667 1.59E-09 0.3333330.066667 1.88E-10 0.666667 9.39E-11 0.3333330.060606 1.02E-11 0.666667 5.11E-12 0.3333330.055556 5.15E-13 0.666667 2.57E-13 0.3333330.051282 2.41E-14 0.666667 1.21E-14 0.3333330.047619 1.06E-15 0.666667 5.3E-16 0.333333
0.044444 4.36E-17 0.666667 2.18E-17 0.3333330.041667 1.69E-18 0.666667 8.47E-19 0.3333330.039216 6.22E-20 0.666667 3.11E-20 0.3333330.037037 2.17E-21 0.666667 1.08E-21 0.3333330.035088 7.17E-23 0.666667 3.59E-23 0.3333330.033333 2.26E-24 0.666667 1.13E-24 0.3333330.031746 6.82E-26 0.666667 3.41E-26 0.3333330.030303 1.97E-27 0.666667 9.84E-28 0.333333
Garcia-Golding Recycling
Waiting Lines M/D/1 (Constant Service Times)
Data Results8 0.666667
12 0.666667Average number of customers in the system(L) 1.333333
0.083333Average time in the system(W) 0.166667
0.333333
Waiting cost/hour $ 60.00 Waiting cost/trip $ 5.00
Arrival rate (l) Average server utilization(r)
Service rate (m) Average number of customers in the queue(Lq)
Average waiting time in the queue(Wq)
Probability (% of time) system is empty (P0)
The arrival RATE and service RATE both must be rates and use the same time unit. Given a time such as 10 minutes, convert it to a rate such as 6 per hour.The arrival RATE and service RATE both must be rates and use the same time unit. Given a time such as 10 minutes, convert it to a rate such as 6 per hour.
Department of Commerce
Waiting Lines M/M/s with a finite population
Data Results
0.05 0.436048
0.5 0.203474Number of servers 1 Average number of customers in the system(L) 0.639522
Population size (N) 5 0.933264Average time in the system(W) 2.933264
0.563952Effective arrival rate 0.218024
Probabilities
Number, n Number waiting0 0.5639522 0.56395218 0 0.251 0.2819761 0.84592827 0 0.22 0.1127904 0.9587187 1 0.153 0.0338371 0.99255583 2 0.14 0.0067674 0.99932326 3 0.055 0.0006767 1 4 06789
10111213141516171819202122232425262728293031
Arrival rate (l) per customer Average server utilization(r)
Service rate (m) Average number of customers in the queue(Lq)
Average waiting time in the queue(Wq)
Probability (% of time) system is empty (P0)
Probability, P(n)
Cumulative Probability
Arrival rate(n)
The arrival rate is for each member of the population. If they go for service every 20 minutes then enter 3 (per hour).The arrival rate is for each member of the population. If they go for service every 20 minutes then enter 3 (per hour).
1.7732
Term 1 Term 2 P0(s)1 1 1 1 0.7732
0.5 1.5 0.5 1.5 0.2732 0.5639520.2 1.7 0.0732
0.06 1.76 0.01320.012 1.772 0.0012
0.0012 1.7732 0
Sum term 1
Sum term 2
Decum term 2
Harry's Tire Shop NOTE: The random numbers appearing here may not be the same as the ones in the book, but the formulas are the same.
Probability Day0.05 0 0.05 0 1 0.10838 1
0.1 0.05 0.15 1 2 0.772863 40.2 0.15 0.35 2 3 0.789351 40.3 0.35 0.65 3 4 0.511857 30.2 0.65 0.85 4 5 0.801233 4
0.15 0.85 1 5 6 0.440355 37 0.550327 38 0.482025 39 0.234368 2
10 0.553767 3Average 3
Results (Frequency table)
Frequency Percentage Cum %0 0 0% 0%1 1 10% 10%2 1 10% 20%3 5 50% 70%4 3 30% 100%5 0 0% 100%
10
Probability Range (Lower)
Cumulative Probability
Tires Demand
Random Number
Simulated Demand
Tires Demanded
NOTE: The random numbers appearing here may not be the same as the ones in the book, but the formulas are the same.
Generating Normal Random Numbers NOTE: The random numbers appearing here may not be the same as the ones in the book, but the formulas are the same.
Random number Value Frenquency Percentage42.004161470523 26 1 0.5%
43.6166685678545 28 1 0.5%38.7555889874489 30 2 1.0%38.3596562306756 32 9 4.5%43.8513434558299 34 8 4.0%40.0546082617959 36 17 8.5%43.9940540943639 38 30 15.0%44.1155535603764 40 23 11.5%
47.990037026023 42 33 16.5%41.8364019067895 44 33 16.5%42.8410869545674 46 20 10.0%
34.006271096758 48 14 7.0%34.3852830371233 50 6 3.0%41.6224675703723 52 1 0.5%31.8622824985997 54 1 0.5%40.3382927536189 56 1 0.5%30.2193672153786 20026.465334415001641.808734578983530.265442570838240.804846701123137.999055593424341.358267286592437.116091534605435.434134714665148.283060269080440.203903445265538.664155025632844.397685216767635.308728879807229.070631798822936.286111602969737.243544593034637.562927958252934.9165441655781
41.18619304269928.310910771256738.910010916005545.940723682098741.110397716888736.176588592564745.934401427165943.717576760895535.432230367246544.974181635920543.199697609328845.5275067052154
41.408491477600244.3662743531233
36.02322624758747.212550462624342.464352182405446.264620415068130.044147818988545.369973575021940.669920342684841.383884279480340.972574522119936.837370074111544.410871602102943.979717681767641.990833842569941.5215961681065
42.55615727086746.8431116901892
41.06275717981748.471575117394749.598847364713440.054042335559944.992147701226238.646925255444943.6729957222102
44.58074494521239.633870192587941.855104681661547.311234640207747.112170187109349.835897462610244.244732582880334.642690943546941.230213549822455.246210248142843.960754055194833.732463491333847.556683111060146.936067446602137.281123799481736.994771248093442.428498786547540.954137965934536.765403785476438.497741303655537.6730719930521
42.40418182633337.494997189711
45.013359586506933.845314662985934.0487957542751
33.213823528338543.565413493533242.326370309882145.849340708986939.966900791565740.571589441231444.868864986866439.368050890671239.635947194542341.700086946960835.070755208686140.732971157215836.991944533487538.203800723510946.423799996618140.406013605907336.438189544677342.868368488436240.349122796370441.469027540275736.793416338441833.253636913723743.759752975002331.881570918679539.4090447709118
41.14328684694230.605980546009737.139591393213133.111851030910652.337172630013138.606912994573943.314396685398444.182038857990342.308578213857334.610313958986238.0769363791143
38.269446815439.008940083526731.2720895571357
38.26177426039950.393974070303241.956735775536535.468603073700434.876958429944242.710879995732436.728963147377142.721128849429940.386098085442330.5984117407848
34.72716638625343.1345921665582
44.14343304305640.836376608100434.610027661771848.546791822209636.6970326447897
36.64528462453237.507396669988641.213141963621736.608608480028247.047557312707337.604385393228133.493180445356139.066632787827546.733284587672532.820197857299536.833511063167348.297832092896739.169004552686625.991569557983844.591393690508742.669414788753241.029582943202442.487242089524143.334836598563942.025427156394246.118354844953438.564453759901742.450473868508643.095077207643243.8227898943934
34.92564362795238.3392035237526
43.89607786061137.7865260886732
32.53242669826536.859892562065244.756362717974436.899199000133430.623374703545943.157334301782737.272764237847939.218548061574635.369171445027934.944602565573547.421284561057637.565292498364938.716992207726845.9013806306768
46.15168878607143.257696396960437.8650315760613
NOTE: The random numbers appearing here may not be the same as the ones in the book, but the formulas are the same.
Port of New Orleans Barge Unloadings NOTE: The random numbers appearing here may not be the same as the ones in the book, but the formulas are the same.
Day Arrivals Unloaded1 0 0.06407 0 0 0.196947 2 02 0 0.33732 2 2 0.120067 2 23 0 0.797373 4 4 0.390929 3 34 1 0.469619 3 4 0.231749 3 35 1 0.553819 3 4 0.525827 3 36 1 0.025666 0 1 0.575644 3 17 0 0.048624 0 0 0.144905 2 08 0 0.542187 3 3 0.976314 5 39 0 0.989611 5 5 0.838528 4 4
10 1 0.302016 2 3 0.609019 3 3
Barge Arrivals Unloading ratesDemand Probability Lower CumulativeDemand Number Probability Lower
0 0.13 0 0.13 0 1 0.05 01 0.17 0.13 0.3 1 2 0.15 0.052 0.15 0.3 0.45 2 3 0.5 0.23 0.25 0.45 0.7 3 4 0.2 0.74 0.2 0.7 0.9 4 5 0.1 0.95 0.1 0.9 1 5
Previously delayed
Random number
Total to be unoaded
Random Number
Possibly unloaded
NOTE: The random numbers appearing here may not be the same as the ones in the book, but the formulas are the same.
CumulativeUnloading0.05 1
0.2 20.7 30.9 4
1 5
Three Hills Power NOTE: The random numbers appearing here may not be the same as the ones in the book, but the formulas are the same.
Repair time1 0.4974439 2 2 2 0.04409677 1 32 0.03654599 0.5 2.5 3 0.85222897 3 63 0.70350733 2.5 5 6 0.50631825 2 84 0.35945809 2 7 8 0.48779373 2 105 0.4712546 2 9 10 0.09273529 1 116 0.93263795 3 12 12 0.88321115 3 157 0.76994122 2.5 14.5 15 0.031501 1 168 0.34418468 2 16.5 16.5 0.88179192 3 19.59 0.07834147 1 17.5 19.5 0.19933693 1 20.5
10 0.55444484 2 19.5 20.5 0.98681188 3 23.5
Demand Table Repair timesTime betweeProbability Lower Cumulative Demand Time Probability
0.5 0.05 0 0.05 0.5 1 0.281 0.06 0.05 0.11 1 2 0.52
1.5 0.16 0.11 0.27 1.5 3 0.22 0.33 0.27 0.6 2
2.5 0.21 0.6 0.81 2.53 0.19 0.81 1 3
Breakdown number
Random number
Time between breakdowns
Time of breakdowns
Time repairperson is free
Random Number
Repair ends
NOTE: The random numbers appearing here may not be the same as the ones in the book, but the formulas are the same.
Lower CumulativeLead time0 0.28 1
0.28 0.8 20.8 1 3
Three Grocery Example
State ProbabilitiesAmerican Food SFood Mart Atlas Foods
Time #1 #2 #3 Matrix of Transition Probabilities0 0.4 0.3 0.3 0.8 0.1 0.11 0.41 0.31 0.28 0.1 0.7 0.22 0.415 0.314 0.271 0.2 0.2 0.63 0.4176 0.3155 0.26694 0.41901 0.31599 0.2655 0.419807 0.316094 0.2640996 0.4202748 0.3160663 0.2636589
Accounts Receivable Example
1 0 0 0P= I : 0 = 0 1 0 0
A : B 0.6 0 0.2 0.20.4 0.1 0.3 0.2
I - B = 0.8 -0.2-0.3 0.8
F = (I - B) inverse 1.37931 0.3448280.517241 1.37931
FA = 0.965517 0.0344830.862069 0.137931
ARCO Quality Control
Number of samples 20Sample size 100
Data Results# Defects % Defects Total Sample Size 2000
Sample 1 6 0.06 Total Defects 80Sample 2 5 0.05 Percentage defects 0.04Sample 3 0 0 Std dev of p-bar 0.019596Sample 4 1 0.01Sample 5 4 0.04 Upper Control Limit 0.098788Sample 6 2 0.02 Center Line 0.04Sample 7 5 0.05 Lower Control Limit 0Sample 8 3 0.03Sample 9 3 0.03Sample 10 2 0.02Sample 11 6 0.06Sample 12 1 0.01Sample 13 8 0.08Sample 14 7 0.07Sample 15 5 0.05Sample 16 4 0.04Sample 17 11 0.11 Above UCLSample 18 3 0.03Sample 19 0 0Sample 20 4 0.04
Graph informationSample 1 0.06 0 0Sample 2 0.05 0 0Sample 3 0 0 0Sample 4 0.01 0 0Sample 5 0.04 0 0Sample 6 0.02 0 0Sample 7 0.05 0 0Sample 8 0.03 0 0Sample 9 0.03 0 0Sample 10 0.02 0 0Sample 11 0.06 0 0Sample 12 0.01 0 0Sample 13 0.08 0 0Sample 14 0.07 0 0Sample 15 0.05 0 0Sample 16 0.04 0 0Sample 17 0.11 0 0Sample 18 0.03 0 0Sample 19 0 0 0Sample 20 0.04 0 0
Enter the sample size then enter the number of defects in each sample.Enter the sample size then enter the number of defects in each sample.
AHP n= 3
Hardware Sys.1 Sys.2 Sys.3 Sys.1 Sys.2 Sys.3 Priority Wt. sum vector Consistency vector
Sys.1 1 3 9 Sys.1 0.6923 0.7200 0.5625 0.6583 2.0423 3.1025
Sys.2 0.3333 1 6 Sys.2 0.2308 0.2400 0.3750 0.2819 0.8602 3.0512
Sys.3 0.1111 0.1667 1 Sys.3 0.0769 0.0400 0.0625 0.0598 0.1799 3.0086
Column Total 1.4444 4.1667 16
Software Sys.1 Sys.2 Sys.3 Sys.1 Sys.2 Sys.3 Priority Wt. sum vector
Sys.1 1 0.5 0.125 Sys.1 0.0909 0.0769 0.0943 0.0874 0.2623 3.0014
Sys.2 2 1 0.2 Sys.2 0.1818 0.1538 0.1509 0.1622 0.4871 3.0028
Sys.3 8 5 1 Sys.3 0.7273 0.7692 0.7547 0.7504 2.2605 3.0124
Column Total 11 6.5 1.325
Vendor Sys.1 Sys.2 Sys.3 Sys.1 Sys.2 Sys.3 Priority Wt. sum vector
Sys.1 1 1 6 Sys.1 0.4615 0.4286 0.6000 0.4967 1.5330 3.0863
Sys.2 1 1 3 Sys.2 0.4615 0.4286 0.3000 0.3967 1.2132 3.0582
Sys.3 0.1667 0.3333 1 Sys.3 0.0769 0.1429 0.1000 0.1066 0.3216 3.0172
Column Total 2.1667 2.3333 10
Factor Hard. Soft. Vendor Hardware Software Vendor Priority Wt. sum vector
Hardware 1 0.125 0.3333 Hardware 0.0833 0.0857 0.0769 0.0820 0.2460 3.0004
Software 8 1 3 Software 0.6667 0.6857 0.6923 0.6816 2.0468 3.0031
Vendor 3 0.3333 1 Vendor 0.2500 0.2286 0.2308 0.2364 0.7096 3.0011
Column Total 12 1.4583 4.3333
n RI Hardware Software Vendor Priority
2 0.00 Sys.1 0.658 0.087 0.497 0.231
3 0.58 Sys.2 0.282 0.162 0.397 0.227
4 0.90 Sys.3 0.060 0.750 0.107 0.542
5 1.12
6 1.24
7 1.32
8 1.41
Consistency vector
Lambd 3.0541
CI 0.0270
CR 0.0466
Lambd 3.0055430750418
CI 0.0028
CR 0.0048
Lambd 3.0539
CI 0.0269
CR 0.0464
Lambd 3.0015
CI 0.0008
CR 0.0013
Matrix Multiplication
A= 1 2 3 B= 2 11 2 0 1 1
3 2
AxB = 13 94 3
Matrix Inverse
A= 2 1 A-inverse= 1.5 -0.54 3 -2 1
Matrix Determinant
A= 3 4 det(A)= -104 2