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![Page 1: MISSISSIPPI RIVER BARGE ARRIVALS AND UNLOADINGS A Queuing Simulation Applied Management Science for Decision Making, 1e © 2012 Pearson Prentice-Hall, Inc.](https://reader036.fdocuments.in/reader036/viewer/2022062806/56649d205503460f949f56e6/html5/thumbnails/1.jpg)
MISSISSIPPI RIVER BARGE ARRIVALS AND UNLOADINGS
A Queuing SimulationA Queuing Simulation
Applied Management Science for Decision Making, 1e Applied Management Science for Decision Making, 1e © 2012 Pearson Prentice-Hall, Inc. Philip A. Vaccaro , PhD© 2012 Pearson Prentice-Hall, Inc. Philip A. Vaccaro , PhD
Port of New OrleansPort of New Orleans
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Two Courses of Action ConsideredTwo Courses of Action Considered
COA Number 1 COA Number 2COA Number 1 COA Number 2
DOCK CREW OF 6 DOCK CREW OF 12DOCK CREW OF 6 DOCK CREW OF 12
COURSE OF ACTIONCOURSE OF ACTIONNUMBER ONENUMBER ONE
COURSE OF ACTIONCOURSE OF ACTIONNUMBER TWONUMBER TWO
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Two Relevant VariablesTwo Relevant Variables
Daily Evening BargeDaily Evening Barge
ArrivalsArrivalsDaily Evening BargeDaily Evening Barge
UnloadingsUnloadings
VARIABLEVARIABLENUMBERNUMBER
ONEONE
VARIABLEVARIABLENUMBERNUMBER
TWOTWO
Port of New OrleansPort of New Orleans
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Evaluation CriteriaEvaluation Criteria
1. Average Number of Barges Unloaded Each evening.
2. Average Number of Barges Delayed Each evening.
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Simulation ExecutionSimulation Execution
Simulate a daily barge arrival.Simulate a daily barge arrival.
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Simulation ExecutionSimulation Execution
Simulate a daily barge arrival.Simulate a daily barge arrival.
Simulate a daily barge unloading.Simulate a daily barge unloading.
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Simulation ExecutionSimulation Execution
Simulate a daily barge arrival.Simulate a daily barge arrival.
Simulate a daily barge unloading.Simulate a daily barge unloading.
Determine how many, if any, barges Determine how many, if any, barges remain unloaded at the end of the evening.remain unloaded at the end of the evening.
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Simulation ExecutionSimulation Execution
Simulate a daily barge arrival.Simulate a daily barge arrival.
Simulate a daily barge unloading.Simulate a daily barge unloading.
Determine how many, if any, barges Determine how many, if any, barges remain unloaded at the end of the evening.remain unloaded at the end of the evening.
Unloaded barges become the beginningUnloaded barges become the beginning balance for the following evening.balance for the following evening.
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Overnight Barge ArrivalsOvernight Barge Arrivals
SPREADSHEET
NUMBER OF ARRIVALS
PROBABILITY CUMULATIVE
PROBABILITY
RANDOM NUMBER
INTERVAL
00 .13.13 .13.13 01 - 1301 - 13
11 .17.17 .30.30 14 - 3014 - 30
22 .15.15 .45.45 31 - 4531 - 45
33 .25.25 .70.70 46 - 7046 - 70
44 .20.20 .90.90 71 - 9071 - 90
55 .10.10 1.001.00 91 - 0091 - 00
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Crew of 6 Unloading RatesCrew of 6 Unloading Rates
SPREADSHEET
DAILY UNLOADING
RATEPROBABILITY
CUMULATIVE
PROBABILITY
RANDOM NUMBER
INTERVAL
11 .05.05 .05.05 01 - 0501 - 05
22 .15.15 .20.20 06 - 2006 - 20
33 .50.50 .70.70 21 - 7021 - 70
44 .20.20 .90.90 71 - 9071 - 90
55 .10.10 1.001.00 91 - 0091 - 00
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Random Number StringsRandom Number Strings
TO GENERATE DAILY ARRIVALS
52 06 50 88 53 30 10 47 99 37 66 91 35 32 00
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Random Number StringsRandom Number Strings
TO GENERATE DAILY ARRIVALS
52 06 50 88 53 30 10 47 99 37 66 91 35 32 00
TO GENERATE DAILY UNLOADINGS
37 63 28 02 74 35 24 03 29 60 74 85 90 73 59
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Random Number StringsRandom Number Strings
Day1st 2nd 3rd
Random Number
for
Daily Arrival52 06 50
Random Number
for Daily
Unloading37 63 28
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Random Number StringsRandom Number Strings
Day4th 5th 6th
Random Number
for
Daily Arrival88 53 30
Random Number
for Daily
Unloading02 74 35
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Random Number StringsRandom Number Strings
Day4th 5th 6th
Random Number
for
Daily Arrival88 53 30
Random Number
for Daily
Unloading02 74 35
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Random Number StringsRandom Number Strings
Day4th 5th 6th
Random Number
for
Daily Arrival88 53 30
Random Number
for Daily
Unloading02 74 35
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Random Number StringsRandom Number Strings
Day7th 8th 9th
Random Number
for
Daily Arrival10 47 99
Random Number
for Daily
Unloading24 03 29
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Random Number StringsRandom Number Strings
Day7th 8th 9th
Random Number
for
Daily Arrival10 47 99
Random Number
for Daily
Unloading24 03 29
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Random Number StringsRandom Number Strings
Day7th 8th 9th
Random Number
for
Daily Arrival10 47 99
Random Number
for Daily
Unloading24 03 29
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Random Number StringsRandom Number Strings
Day10th 11th 12th
Random Number
for
Daily Arrival37 66 91
Random Number
for Daily
Unloading60 74 85
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Random Number StringsRandom Number Strings
Day10th 11th 12th
Random Number
for
Daily Arrival37 66 91
Random Number
for Daily
Unloading60 74 85
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Random Number StringsRandom Number Strings
Day10th 11th 12th
Random Number
for
Daily Arrival37 66 91
Random Number
for Daily
Unloading60 74 85
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Random Number StringsRandom Number Strings
Day13th 14th 15th
Random Number
for
Daily Arrival35 32 00
Random Number
for Daily
Unloading90 73 59
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Random Number StringsRandom Number Strings
Day13th 14th 15th
Random Number
for
Daily Arrival35 32 00
Random Number
for Daily
Unloading90 73 59
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Random Number StringsRandom Number Strings
Day13th 14th 15th
Random Number
for
Daily Arrival35 32 00
Random Number
for Daily
Unloading90 73 59
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Simulation ExecutionSimulation Execution
EVENINGEVENING
NUMBERNUMBERDELAYEDDELAYEDPREVIOUSPREVIOUSEVENINGEVENING
ARRIVALARRIVALRANDOMRANDOMNUMBERNUMBER
BARGEBARGEARRIVALARRIVALNUMBERNUMBER
TOTAL TOTAL TO BETO BE
UNLOADEDUNLOADED
UNLOADINGUNLOADINGRANDOMRANDOMNUMBERNUMBER
NUMBERNUMBERUNLOADEDUNLOADED
11stst - - aa 5252 33 33 3737 33
22ndnd 00 0606 00 00 6363 00bb
33rdrd 00 5050 33 33 2828 33
a – a – WE CAN BEGIN WITH NO DELAYS OR SOME DELAYS FROM THE PREVIOUS EVENING. OVER THE LENGTH OF THE SIMULATION, THE INITIAL BALANCE AVERAGES OUT.
b - b - THREE BARGES COULD HAVE BEEN UNLOADED BUT SINCE THERE WERE NO ARRIVALS AND NO BACKLOG, ZERO UNLOADINGS RESULTED.
c- THE PROGRAM WOULD HAVE UNLOADED ANY NUMBER OF BARGES UP TO, AND INCLUDING THREE (3) , HAD THERE BEEN A POSITIVE BALANCE FOR TOTAL TO BE UNLOADED !
,c,c
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Simulation ExecutionSimulation Execution
EVENINGEVENING
NUMBERNUMBERDELAYEDDELAYEDPREVIOUSPREVIOUSEVENINGEVENING
ARRIVALARRIVALRANDOMRANDOMNUMBERNUMBER
BARGEBARGEARRIVALARRIVALNUMBERNUMBER
TOTAL TOTAL TO BETO BE
UNLOADEDUNLOADED
UNLOADINGUNLOADINGRANDOMRANDOMNUMBERNUMBER
NUMBERNUMBERUNLOADEDUNLOADED
44thth 00 8888 44 44 0202 11
55thth 33 5353 33 66 7474 44
66thth 22 3030 11 33 3535 33
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Simulation ExecutionSimulation Execution
EVENINGEVENING
NUMBERNUMBERDELAYEDDELAYEDPREVIOUSPREVIOUSEVENINGEVENING
ARRIVALARRIVALRANDOMRANDOMNUMBERNUMBER
BARGEBARGEARRIVALARRIVALNUMBERNUMBER
TOTAL TOTAL TO BETO BE
UNLOADEDUNLOADED
UNLOADINGUNLOADINGRANDOMRANDOMNUMBERNUMBER
NUMBERNUMBERUNLOADEDUNLOADED
77thth 00 1010 00 00 2424 00
88thth 00 4747 33 33 0303 11
99thth 22 9999 55 77 2929 33
c,dc,d
C – THREE BARGES COULD HAVE BEEN UNLOADED BUT SINCE THERE WERE NO ARRIVALS AND NO BACKLOGS, ZERO UNLOADINGS RESULTED.
D - THE PROGRAM WOULD HAVE UNLOADED UP TO, AND INCLUDING THREE (3) BARGES, HAD THERE BEEN A POSITIVE BALANCE FOR UNLOADINGS !
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Simulation ExecutionSimulation Execution
EVENINGEVENING
NUMBERNUMBERDELAYEDDELAYEDPREVIOUSPREVIOUSEVENINGEVENING
ARRIVALARRIVALRANDOMRANDOMNUMBERNUMBER
BARGEBARGEARRIVALARRIVALNUMBERNUMBER
TOTAL TOTAL TO BETO BE
UNLOADEDUNLOADED
UNLOADINGUNLOADINGRANDOMRANDOMNUMBERNUMBER
NUMBERNUMBERUNLOADEDUNLOADED
1313thth 33 3535 22 55 9090 44
1414thth 11 3232 22 33 7373 33dd
1515thth 00 0000 55 55 5959 33
d – FOUR BARGES COULD HAVE BEEN UNLOADED BUT SINCE ONLY THREE WERE IN THE QUEUE, THE NUMBER UNLOADED IS RECORDED AS “3”.
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Simulation SummarySimulation Summary
EVENINGEVENING
NUMBERNUMBERDELAYEDDELAYEDPREVIOUSPREVIOUSEVENINGEVENING
ARRIVALARRIVALRANDOMRANDOMNUMBERNUMBER
BARGEBARGEARRIVALARRIVALNUMBERNUMBER
TOTAL TOTAL TO BETO BE
UNLOADEDUNLOADED
UNLOADINGUNLOADINGRANDOMRANDOMNUMBERNUMBER
NUMBERNUMBERUNLOADEDUNLOADED
1313thth 33 3535 22 55 9090 44
1414thth 11 3232 22 33 7373 3 3
1515thth 00 0000 55 55 5959 33
TOTAL DELAYS = 20AVERAGE = 1.33
TOTAL ARRIVALS = 41AVERAGE = 2.73
TOTAL UNLOADINGS = 39AVERAGE = 2.60
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Overnight Barge ArrivalsOvernight Barge Arrivals
SPREADSHEET FOR CREW OF 12
NUMBER OF ARRIVALS
PROBABILITY CUMULATIVE
PROBABILITY
RANDOM NUMBER INTERVAL
00 .13.13 .13.13 01 - 1301 - 13
11 .17.17 .30.30 14 - 3014 - 30
22 .15.15 .45.45 31 - 4531 - 45
33 .25.25 .70.70 46 - 7046 - 70
44 .20.20 .90.90 71 - 9071 - 90
55 .10.10 1.001.00 91 - 0091 - 00
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Crew of 12 Unloading RatesCrew of 12 Unloading RatesSPREADSHEET
DAILY UNLOADING RATE PROBABILITY
CUMULATIVE
PROBABILITY
RANDOM NUMBER INTERVAL
11 .03.03 .03.03 01 - 0301 - 03
22 .12.12 .15.15 04 - 1504 - 15
33 .40.40 .55.55 16 - 5516 - 55
44 .28.28 .83.83 56 - 8356 - 83
55 .12.12 .95.95 84 - 9584 - 95
66 .05.05 1.001.00 96 - 0096 - 00
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Random Number StringsRandom Number StringsCREW OF 12 SIMULATIONCREW OF 12 SIMULATION
TO GENERATE DAILY ARRIVALS
37 77 13 10 02 18 31 19 32 85 31 94 81 43 31
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Random Number StringsRandom Number StringsCREW OF 12 SIMULATIONCREW OF 12 SIMULATION
TO GENERATE DAILY ARRIVALS
TO GENERATE DAILY UNLOADINGS
37 77 13 10 02 18 31 19 32 85 31 94 81 43 31
69 84 12 94 51 36 17 02 15 29 16 52 56 43 26
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Simulation ExecutionSimulation ExecutionCREW OF TWELVECREW OF TWELVE
11stst -- 3737 22 22 6969 22
22ndnd 00 7777 44 44 8484 44
33rdrd 00 1313 00 00 1212 00
44thth 00 1010 00 00 9494 00
EVENINGEVENINGDELAYEDDELAYEDPREVIOUSPREVIOUSEVENINGEVENING
ARRIVALARRIVALRANDOMRANDOMNUMBERNUMBER
BARGEBARGEARRIVALARRIVALNUMBERNUMBER
TOTALTOTALTO BETO BE
UNLOADEDUNLOADED
UNLOADINGUNLOADINGRANDOMRANDOMNUMBERNUMBER
NUMBERNUMBERUNLOADEDUNLOADED
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Simulation ExecutionSimulation ExecutionCREW OF TWELVECREW OF TWELVE
55thth 00 0202 00 00 5151 00
66thth 00 1818 11 11 3636 11
77thth 00 3131 22 22 1717 22
88thth 00 1919 11 11 0202 11
EVENINGEVENINGDELAYEDDELAYEDPREVIOUSPREVIOUSEVENINGEVENING
ARRIVALARRIVALRANDOMRANDOMNUMBERNUMBER
BARGEBARGEARRIVALARRIVALNUMBERNUMBER
TOTALTOTALTO BETO BE
UNLOADEDUNLOADED
UNLOADINGUNLOADINGRANDOMRANDOMNUMBERNUMBER
NUMBERNUMBERUNLOADEDUNLOADED
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Simulation ExecutionSimulation ExecutionCREW OF TWELVECREW OF TWELVE
1212thth 00 9494 55 55 5252 33
1313thth 22 8181 44 66 5656 44
1414thth 22 4343 22 44 4343 33
1515thth 11 3131 22 33 2626 33
EVENINGEVENINGDELAYEDDELAYEDPREVIOUSPREVIOUSEVENINGEVENING
ARRIVALARRIVALRANDOMRANDOMNUMBERNUMBER
BARGEBARGEARRIVALARRIVALNUMBERNUMBER
TOTALTOTALTO BETO BE
UNLOADEDUNLOADED
UNLOADINGUNLOADINGRANDOMRANDOMNUMBERNUMBER
NUMBERNUMBERUNLOADEDUNLOADED
TOTAL DELAYS = 6AVERAGE = 0.4
TOTAL ARRIVALS = 31AVERAGE = 2.07
TOTAL UNLOADINGS = 31AVERAGE = 2.07
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ScoreboardScoreboard
BargesBarges Crew of 6Crew of 6 Crew of 12Crew of 12AVERAGE DAILY AVERAGE DAILY
DELAYSDELAYS 1.331.33 .40.40AVERAGE DAILYAVERAGE DAILY
UNLOADINGSUNLOADINGS2.602.60 2.072.07
AVERAGE DAILYAVERAGE DAILY
ARRIVALSARRIVALS2.732.73 2.072.07
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Possible Relevant VariablesPossible Relevant Variables
WindsWinds CurrentsCurrents FogFog TemperatureTemperature River IceRiver Ice Seasonal Barge TrafficSeasonal Barge Traffic Competing DocksCompeting Docks PrecipitationPrecipitation
Absentee RatesAbsentee Rates Barge SizesBarge Sizes Additional Crew Staffing Additional Crew Staffing
OptionsOptions Local Economy Effect Local Economy Effect
on Barge Trafficon Barge Traffic Crew TrainingCrew Training
BARGE SIMULATIONBARGE SIMULATION
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Repeating Random Number StringsRepeating Random Number Strings
Used for generating arrival and unloading ratesUsed for generating arrival and unloading ratesfor for bothboth crew staffing options if you want to crew staffing options if you want to
isolate and observe the impact of each staffingisolate and observe the impact of each staffingoption on the dock-river system.option on the dock-river system.
ANY DIFFERENCES FOUND IN THE UNLOADINGRATES WOULD BE DIRECTLY ATTRIBUTABLE TO
THE CREW SIZE ITSELF, SINCE ALL OTHER ELEMENTS OF THE SIMULATION HAD BEEN
HELD CONSTANT!
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Non-Repeating Random Number StringsNon-Repeating Random Number Strings
Used for generating arrival and unloading ratesUsed for generating arrival and unloading ratesfor for bothboth crew staffing options if you want to crew staffing options if you want to
test for consistent results of the impact of each test for consistent results of the impact of each staffing option on the dock-river system.staffing option on the dock-river system.
TO YIELD VALID CONCLUSIONS HOWEVER, YOU MUSTINSURE THAT THE SIMULATION HAS RUN OVER A
SUFFICIENTLY LONG PERIOD OF TIME IN ORDER TOALLOW THE NUMBERS TO “SETTLE DOWN” TO
THEIR LONG-TERM AVERAGES.
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Barge Simulation PostscriptBarge Simulation Postscript
If the data were also analyzed in terms of barge delay opportunity costs, extra crew hiring costs, idle time costs, insurance, and barge traffic po- tential, a better quality staffing decision might have been attained.
The simulation should also have been executed under other crew size options.
THIS DATE IS AVAILABLE FROM HUMAN RESOURCES,THIS DATE IS AVAILABLE FROM HUMAN RESOURCES,MARKETING, ACCOUNTING, AND FINANCE.MARKETING, ACCOUNTING, AND FINANCE.
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QM for WINDOWSQM for WINDOWSCOMMENTSCOMMENTS
This program cannot simultaneously accommodate two or more relevant variables.
Every simulation is custom-built , and therefore presents too many design options for assimilation into a general-purpose software program. An alternative would be to run each relevant varia- ble separately, insert the simulated outcomes on a spreadsheet, and then manually calculate the out- comes of the variables’ interactions.
THIS APPROACH IS FEASIBLE FOR ONLY THE MOST ELEMENTAL SIMULATIONSTHIS APPROACH IS FEASIBLE FOR ONLY THE MOST ELEMENTAL SIMULATIONS
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ExampleExample
EVENINGEVENING
NUMBERNUMBERDELAYEDDELAYEDPREVIOUSPREVIOUSEVENINGEVENING
ARRIVALARRIVALRANDOMRANDOMNUMBERNUMBER
BARGEBARGEARRIVALARRIVALNUMBERNUMBER
TOTAL TOTAL TO BETO BE
UNLOADEDUNLOADED
UNLOADINGUNLOADINGRANDOMRANDOMNUMBERNUMBER
NUMBERNUMBERUNLOADEDUNLOADED
11stst - - 5252 3 33 3737 3
22ndnd 00 0606 0 00 6363 0
33rdrd 00 5050 3 33 2828 3
SIMULATEDVIA
QM for WINDOWSor QM EXCEL
SIMULATEDVIA
QM for WINDOWSor QM EXCEL
NOTREQUIRED
NOT REQUIRED
MANUALLYMANUALLYENTEREDENTERED
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These simulatedbarge arrivals
would be insertedon our manualspreadsheet
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These simulated bargeunloadings wouldbe inserted on our
manual spreadsheet
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Average Daily Delays ( 20/15 days ) = 1.33 Barges
Average Daily Arrivals ( 41/15 days ) = 2.73 Barges
Average Daily Unloadings ( 39 / 15 days ) = 2.60 Barges
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Templateand
Sample Data
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TemplateAnd
Sample Data
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Sample Data
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Sample Data
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Inventory Policy SimulationInventory Policy Simulation
Establishing an inventory control doctrinefor an item having variable daily demand
and variable reorder lead time.
The goal is to minimize the ordering, holding, and stockout costs
involved.
a more realisticbusiness application
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Electric Drill DemandElectric Drill Demand
Daily Demand
Frequency
( days )
Probability Cumulative
Probability
Random
No. Interval
0 15 .05 .05 01 - 05
1 30 .10 .15 06 - 15
2 60 .20 .35 16 - 35
3 120 .40 .75 36 - 75
4 45 .15 .90 76 - 90
5 30 .10 1.00 91 - 00
∑∑= = 300 300 ∑= ∑= 1.001.00
11stst relevant variable relevant variable
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Electric Drill Electric Drill Reorder Lead TimeReorder Lead Time
LEAD TIME
(DAYS)
Frequency
(ORDERS) Probability
Cumulative
Probability
RN
Interval
1 1010 .20 .20 01 - 20
2 2525 .50 .70 21 - 70
3 1515 .30 1.00 71 - 00
∑∑ = = 50 50 ∑ = ∑ = 1.001.00
22ndnd relevant variable relevant variable
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The SimulationThe Simulation
The 1The 1stst inventory policy to be simulated: inventory policy to be simulated:
Q = 10 units
R = 5 units
Regardless of the simulated lead time period,an order will not arrive the next morning but at the beginning of the following working day
Order 10 drills at a time when the shelf stock falls to five drills or less at the end of the business dayOrder 10 drills at a time when the shelf stock falls to five drills or less at the end of the business day
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11 -- 1010 0606 11 99 00 NONO -- --
22 00 99 6363 33 66 00 NONO -- --
33 00 66 5757 33 33 00 YESYES 0202 11
44 00 33 9494 55 00 22 NONO -- --
55 1010 1010 5252 33 77 00 NONO -- --
DAYDAYUNITS
RECEIVEDBEGINNINGINVENTORY
RANDOMRANDOMNUMBERNUMBER DEMAND
ENDINGENDINGINVENTORYINVENTORY
LOSTLOSTSALESSALES
ORDER? RANDOMNUMBER
LEAD TIME
a – 1st order is placedb – generates 1st lead timec – next random number in seriesd – no order placed because of outstanding order from previous day
aa bbcc
dd
The SimulationThe Simulation
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66 00 77 6969 33 44 00 YESYES 3333 22
77 00 44 3232 22 22 00 NONO -- --
88 00 22 3030 22 00 00 NONO -- --
99 1010 1010 4848 33 77 00 NONO -- --
1010 00 77 8888 44 33 00 YESYES 1414 11
DAYDAYUNITSUNITS
RECEIVEDRECEIVEDBEGINNINGBEGINNINGINVENTORYINVENTORY
RANDOMRANDOMNUMBERNUMBER DEMAND
ENDINGENDINGINVENTORYINVENTORY
LOSTLOSTSALESSALES
ORDER ?
RANDOMNUMBER
LEAD TIME
f – order placed at end of 6th day arrives
ff
The SimulationThe Simulation
∑ = 41 2 3 units
endinginventory
numberof lostsales
numberof orders
placed
SUMMARYSTATISTICS
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SimulationSimulation Results Results
AVERAGE ENDING INVENTORY 41 units / 10 days = 4.1 units per day
AVERAGE LOST SALES2 sales lost / 10 days = .2 unit per day
AVERAGE NUMBER OF ORDERS PLACED3 orders / 10 days = .3 order per day
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Simulation CostsSimulation Costs
Daily Order Cost $10.00 per order x .3 daily orders = $3.00
Daily Holding Cost$.03 per unit per day x 4.1 units per day = $.12
Daily Stockout Cost$8.00 per lost sale x .2 daily lost sales = $1.60
Total Daily Cost = $4.72
( TOTAL ANNUAL COSTS = $944.00 )
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Simulation PostscriptSimulation Postscript
We must now compare this potential inventory control doctrine to others.
Perhaps we might evaluate every pair of values for Q ( 6 to 20 units ) and R ( 3 to 10 units ) :
After simulating all reasonable combinations of Q and R, we select the pair yielding
the lowest total inventory cost
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Fast Food Fast Food Drive-Through Drive-Through
SimulationSimulation
ARRIVALARRIVAL
RN for RN for TIME TIME
betweenbetweenARRIVALSARRIVALS
TIME TIME BETWEEN BETWEEN ARRIVALSARRIVALS
TIMETIME
RN for RN for SERVICESERVICE
TIMETIMESERVICESERVICE
TIMETIME
WaitingWaiting
TimeTimeCUSTOMERCUSTOMER
LEAVESLEAVES
11stst 1414 1 min.1 min. 11:0111:01 8888 3 min.3 min. 00 11:0411:04
22ndnd 7474 3 min.3 min. 11:0411:04 3232 2 min.2 min. 00 11:0611:06
33rdrd 2727 2 min.2 min. 11:0611:06 3636 2 min.2 min. 00 11:0811:08
44thth 0303 1 min.1 min. 11:0711:07 2424 1 min.1 min. 11 11:0911:09
( ASSUME THE DRIVE-THROUGH OPENS AT 11:00 AM )( ASSUME THE DRIVE-THROUGH OPENS AT 11:00 AM )
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Generator BreakdownGenerator Breakdown
SimulationSimulation
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Generator Breakdown SimulationGenerator Breakdown Simulation
TIME BETWEENTIME BETWEEN
RECORDED RECORDED MACHINE MACHINE
FAILURES (hours)FAILURES (hours)PROBABILITYPROBABILITY CUMULATIVECUMULATIVE
PROBABILITYPROBABILITY
RANDOMRANDOM
NUMBERNUMBERINTERVALINTERVAL
½ ½ .05 .05 01 - 05
11 .06 .11 06 - 11
1 ½ 1 ½ .16 .27 12 - 27
22 .33 .60 28 - 60
2 ½ 2 ½ .21 .81 61 - 81
33 .19 1.00 82 - 00
∑ 1.00
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Generator Breakdown SimulationGenerator Breakdown Simulation
REPAIR TIMEREPAIR TIME
REQUIREDREQUIRED
( HOURS )( HOURS )
PROBABILITYPROBABILITY
CUMULATIVECUMULATIVE
PROBABILITYPROBABILITY
RANDOMRANDOM
NUMBERNUMBER
INTERVALINTERVAL
11 .28.28 .28.28 01 - 2801 - 28
22 .52.52 .80.80 29 - 8029 - 80
33 .20.20 1.001.00 81 - 0081 - 00
TotalTotal 1.001.00
a – MAINTENANCE TIME IS ROUNDED TO HOURLY TIME BLOCKSa – MAINTENANCE TIME IS ROUNDED TO HOURLY TIME BLOCKS
aa
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1 57 2 02:00 02:00 07 1 03:00 1
2 17 1.5 03:30 03:30 60 2 05:30 2
3 36 2 05:30 05:30 77 2 07:30 2
4 72 2.5 08:00 08:00 49 2 10:00 2
5 85 3 11:00 11:00 76 2 13:00 2
6 31 2 13:00 13:00 95 3 16:00 3
BREAKDOWNBREAKDOWNNUMBERNUMBER
TIME BETWEENTIME BETWEENBREAKDOWNSBREAKDOWNSRANDOM NO.RANDOM NO.
TIMETIMEBETWEENBETWEEN
BREAKDOWNSBREAKDOWNS
TIME OFTIME OFBREAKDOWNBREAKDOWN
TIMETIMEMECHANICMECHANICFREE TO FREE TO
BEGIN THISBEGIN THISREPAIRREPAIR
REPAIR TIMEREPAIR TIMERANDOM NO.RANDOM NO.
REPAIR TIMEREPAIR TIMEREQUIREDREQUIRED
TIME TIME REPAIRREPAIRENDSENDS
NO. HRS.NO. HRS.MACHINEMACHINE
DOWNDOWN
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13 33 2 01:00 04:00 40 2 06:00 5
14 89 3 04:00 06:00 42 2 08:00 4
15 13 1.5 05:30 08:00 52 2 10:00 4.5
BREAKDOWNBREAKDOWNNUMBERNUMBER
TIME BETWEENTIME BETWEENBREAKDOWNSBREAKDOWNSRANDOM NO.RANDOM NO.
TIMETIMEBETWEENBETWEEN
BREAKDOWNSBREAKDOWNS
TIME OFTIME OFBREAKDOWNBREAKDOWN
TIMETIMEMECHANICMECHANICFREE TO FREE TO
BEGIN THISBEGIN THISREPAIRREPAIR
REPAIR TIMEREPAIR TIMERANDOM NO.RANDOM NO.
REPAIR TIMEREPAIR TIMEREQUIREDREQUIRED
TIME TIME REPAIRREPAIRENDSENDS
TOTALTOTALNO. HRS.NO. HRS.
MACHINESMACHINESDOWNDOWN
44
Generator Breakdown SimulationGenerator Breakdown Simulation
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Simulation ResultsSimulation Results
Simulation of fifteen (15) generator breakdowns spanned 34 hours of operation. The clock began at 00:00 hours of day 1 and ran until the final repair at 10:00 hours of day 2.
THE TOTALNUMBER OF HOURSTHAT GENERATORS
WERE OUT OFSERVICE IS
COMPUTED TO BE44 HOURS
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Simulation CostsSimulation Costs
Service Maintenance CostService Maintenance Cost34 hours x $30.00 per hour = $1,020.0034 hours x $30.00 per hour = $1,020.00
Simulated Machine Breakdown CostSimulated Machine Breakdown Cost44 hours x $75.00 lost per down hour = $3,300.0044 hours x $75.00 lost per down hour = $3,300.00
Total Simulated Maintenance Cost$4,320.00
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Simulation ApplicationsSimulation Applications
Applied Management Science for Decision Making, 1e Applied Management Science for Decision Making, 1e © 2011 Pearson Prentice-Hall, Inc. Philip A. Vaccaro , PhD© 2011 Pearson Prentice-Hall, Inc. Philip A. Vaccaro , PhD
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Solved ProblemsSolved Problems
Simulation ModelingSimulation ModelingComputer-BasedComputer-Based
ManualManual
Applied Management Science for Decision Making, 1e Applied Management Science for Decision Making, 1e © 2012 Pearson Prentice-Hall, Inc. Philip A. Vaccaro , PhD© 2012 Pearson Prentice-Hall, Inc. Philip A. Vaccaro , PhD
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Simulation ModelingSimulation ModelingLundberg’s Car WashLundberg’s Car Wash
The number of cars arriving per hour at The number of cars arriving per hour at Lundberg’s Car Wash during the past 200 Lundberg’s Car Wash during the past 200
hours of operation is observed to be as follows:hours of operation is observed to be as follows:
CarsCars
ArrivingArriving
FrequencyFrequency CarsCars
ArrivingArriving
FrequencyFrequency
=<3=<3 00 77 6060
44 2020 88 4040
55 3030 =>9=>9 00
66 5050 ∑∑ 200200
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Simulation ModelingSimulation ModelingLundberg’s Car WashLundberg’s Car Wash
REQUIREMENT:
1. Set up a probability and cumulative probability distribution for the variable of car arrivals.2. Establish random number intervals for the above variable.3. Simulate fifteen (15) hours of car arrivals and compute the average number of arrivals per hour.4. Compute the expected number of cars arriving using the expected value formula. Compare this with the results ob- tained in the simulation.
Note: Select the random numbers needed from the 1st column of Table 15.5 , beginning with the digits “52”.
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Simulation ModelingSimulation ModelingLundberg’s Car WashLundberg’s Car Wash
Number of CarsNumber of Cars ProbabilityProbability CumulativeCumulative
ProbabilityProbability
Random Number Random Number IntervalInterval
3 or less3 or less 0.000.00 0.000.00 ------
44 0.100.10 0.100.10 01-1001-10
55 0.150.15 0.250.25 11-2511-25
66 0.250.25 0.500.50 26-5026-50
77 0.300.30 0.800.80 51-8051-80
88 0.200.20 1.001.00 81-0081-00
9 or more9 or more 0.000.00 1.001.00 ------
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Simulation ModelingSimulation ModelingLundberg’s Car WashLundberg’s Car Wash
HourHour RNRN Simulated Simulated ArrivalsArrivals
HourHour RNRN SimulatedSimulated
ArrivalsArrivals
11 5252 77 99 8888 88
22 3737 66 1010 9090 88
33 8282 88 1111 5050 66
44 6969 77 1212 2727 66
55 9898 88 1313 4545 66
66 9696 88 1414 8181 88
77 3333 66 1515 6666 77
88 5050 66 ∑=105105/15 = 7.00 carsAverage hourly
arrivals
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Simulation ModelingSimulation ModelingLundberg’s Car WashLundberg’s Car Wash
((.10.10 x 4) + x 4) + (.15(.15 x 5) + ( x 5) + (.25.25 x 6) + ( x 6) + (.30.30 x 7) + ( x 7) + (.20.20 x 8) = 6.35 x 8) = 6.35
ExpectedValue
Arrival EventsArrival Events
ProbabilitiesProbabilities
The average number of arrivals in the simulation was “ 7.00 “.If enough simulations were performed, the average number
computed would approach the expected value.
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Simulation ModelingSimulation Modeling
Time Time BetweenBetween
Arrivals Arrivals
(minutes(minutes))
ProbabilityProbability
11 0.200.20
22 0.250.25
33 0.300.30
44 0.150.15
55 0.100.10
Local BankLocal Bank
A local bank A local bank collected one collected one
month’s arrival month’s arrival and service ratesand service rates
at its single-teller at its single-teller drive-through drive-through station. These station. These
data are shown data are shown here:here:
ServiceService
TimeTime
(minutes)(minutes)
ProbabilityProbability
11 0.100.10
22 0.150.15
33 0.350.35
44 0.150.15
55 0.150.15
66 0.100.10
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Simulation ModelingSimulation ModelingLocal BankLocal Bank
REQUIREMENT :
1. Simulate a one-hour time period from 1:00 P.M. to 2:00 P.M. for the single-teller drive-through station.
FOR THE TIME BETWEEN CUSTOMER ARRIVALS, USE THE RN STRING:
52,37,82,69,98,96,33,50,88,90,50,27,45,81,66,74,30,59,67
FOR THE CUSTOMER SERVICE TIME, USE THE RN STRING:
60,60,80,53,69,37,06,63,57,02,94,52,69,33,32,30,48,88
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Simulation ModelingSimulation ModelingLocal BankLocal Bank
Time Between Time Between ArrivalsArrivals
ProbabilityProbability Random NumberRandom Number
IntervalInterval
11 0.200.20 01-2001-20
22 0.250.25 21-4521-45
33 0.300.30 46-7546-75
44 0.150.15 76-9076-90
55 0.100.10 91-0091-00
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Simulation ModelingSimulation ModelingLocal BankLocal Bank
Service TimeService Time ProbabilityProbability Random NumberRandom Number
IntervalInterval
11 0.100.10 01-1001-10
22 0.150.15 11-2511-25
33 0.350.35 26-6026-60
44 0.150.15 61-7561-75
55 0.150.15 76-9076-90
66 0.100.10 91-0091-00
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Simulation ModelingSimulation ModelingLocal BankLocal Bank
RANDOM RANDOM NUMBERNUMBER
TIME TIME BETWEEN BETWEEN ARRIVALSARRIVALS
ACTUAL ACTUAL TIMETIME
TIME TIME SERVICE SERVICE BEGINSBEGINS
RANDOM RANDOM NUMBERNUMBER
SERVICE SERVICE TIMETIME
SERVICESERVICE
COMPLETECOMPLETEWAIT TIME WAIT TIME (MINUTES)(MINUTES)
5252 33 1:031:03 1:031:03 6060 33 1:061:06 00
3737 22 1:051:05 1:061:06 6060 33 1:091:09 11
8282 44 1:091:09 1:091:09 8080 55 1:141:14 00
6969 33 1:121:12 1:141:14 5353 33 1:171:17 22
9898 55 1:171:17 1:171:17 6969 44 1:211:21 00
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Simulation ModelingSimulation ModelingLocal BankLocal Bank
RANDOM RANDOM NUMBERNUMBER
TIME TIME BETWEEN BETWEEN ARRIVALSARRIVALS
ACTUAL ACTUAL TIMETIME
TIME TIME SERVICE SERVICE BEGINSBEGINS
RANDOM RANDOM NUMBERNUMBER
SERVICE SERVICE TIMETIME
SERVICESERVICE
COMPLETECOMPLETEWAIT TIME WAIT TIME (MINUTES)(MINUTES)
9696 55 1:221:22 1:221:22 3737 33 1:251:25 00
3333 22 1:241:24 1:251:25 0606 11 1:261:26 11
5050 33 1:271:27 1:271:27 6363 44 1:311:31 00
8888 44 1:311:31 1:311:31 5757 33 1:341:34 00
9090 44 1:351:35 1:351:35 0202 11 1:361:36 00
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Simulation ModelingSimulation ModelingLocal BankLocal Bank
RANDOM RANDOM NUMBERNUMBER
TIME TIME BETWEEN BETWEEN ARRIVALSARRIVALS
ACTUAL ACTUAL TIMETIME
TIME TIME SERVICE SERVICE BEGINSBEGINS
RANDOM RANDOM NUMBERNUMBER
SERVICE SERVICE TIMETIME
SERVICESERVICE
COMPLETECOMPLETEWAIT TIME WAIT TIME (MINUTES)(MINUTES)
5050 33 1:381:38 1:381:38 9494 66 1:441:44 00
2727 22 1:401:40 1:441:44 5252 33 1:471:47 44
4545 22 1:421:42 1:471:47 6969 44 1:511:51 55
8181 44 1:461:46 1:511:51 3333 33 1:541:54 55
6666 33 1:491:49 1:541:54 3232 33 1:571:57 55
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Simulation ModelingSimulation ModelingLocal BankLocal Bank
RANDOM RANDOM NUMBERNUMBER
TIME TIME BETWEEN BETWEEN ARRIVALSARRIVALS
ACTUAL ACTUAL TIMETIME
TIME TIME SERVICE SERVICE BEGINSBEGINS
RANDOM RANDOM NUMBERNUMBER
SERVICE SERVICE TIMETIME
SERVICESERVICE
COMPLETECOMPLETEWAIT TIME WAIT TIME (MINUTES)(MINUTES)
7474 33 1:521:52 1:571:57 3030 33 2:002:00 55
3030 22 1:541:54 2:002:00 4848 33 2:032:03 66
5959 33 1:571:57 2:032:03 8888 55 2:082:08 66
6767 33 2:002:00 ------ ------ ------ TOTALTOTAL 4040
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Simulation ModelingSimulation ModelingLocal BankLocal Bank
Cost of Customer Waiting
40 minutes per hour X
7 hours per dayX
200 days per yearX
$1.00 per minute=
$56,000.00
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Simulation ModelingSimulation ModelingLocal BankLocal Bank
Total Costs
Drive-Through Depreciation per year - $12,000.00+
Salary and Benefits for one teller per year - $16,000.00+
Customer Waiting Cost per year - $56,000.00=
$84,000.000
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Simulation ModelingSimulation ModelingLocal BankLocal Bank
Total Costs for Two Drive-Throughs
Drive-Through Depreciation per year - $20,000.00+
Salary and Benefits for two tellers per year - $32,000.00+
Customer Waiting Cost per year - $1,400.00=
$53,400.000
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Simulation ModelingSimulation ModelingLocal BankLocal Bank
Cost Savings With Two Tellers
$84,000.00 ( 1 drive-through )- $53,400.00 ( 2 drive-throughs )
$30,600.00
The conclusion is to place two teller booths in use.It is critical to replicate this simulation for a much
longer time period before drawing any firm conclusions, however.
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Solved ProblemsSolved Problems
Simulation ModelingSimulation ModelingComputer-BasedComputer-Based
ManualManual
Applied Management Science for Decision Making, 1e Applied Management Science for Decision Making, 1e © 2012 Pearson Prentice-Hall, Inc. Philip A. Vaccaro , PhD© 2012 Pearson Prentice-Hall, Inc. Philip A. Vaccaro , PhD