1 lecture 6 mgmt 650 simulation – chapter 13. 2 announcements hw #4 solutions and grades posted...
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![Page 1: 1 Lecture 6 MGMT 650 Simulation – Chapter 13. 2 Announcements HW #4 solutions and grades posted in BB HW #4 average = 111.30 Final exam today](https://reader035.vdocuments.us/reader035/viewer/2022081519/56649d585503460f94a37007/html5/thumbnails/1.jpg)
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Lecture6
MGMT 650Simulation – Chapter 13
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AnnouncementsAnnouncements
HW #4 solutions and grades posted in BB HW #4 average = 111.30 Final exam today Open book, open notes…. Proposed class structure for today
Lecture – 6:00 – 7:50 Class evaluations – 7:50 – 8:00 Break – 8:00 – 8:30 Final – 8:30 – 9:45
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Lecture6
SimulationChapter 13
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Simulation Is …Simulation Is … Simulation – very broad term
methods and applications to imitate or mimic real systems, usually via computer
Applies in many fields and industries
Simulation models complex situations
Models are simple to use and understand
Models can play “what if” experiments
Extensive software packages available
ARENA, ProModel Very popular and powerful method
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ApplicationsApplications Manufacturing facility Bank operation Airport operations (passengers, security, planes,
crews, baggage, overbooking) Hospital facilities (emergency room, operating
room, admissions) Traffic flow in a freeway system Waiting lines - fast-food restaurant, supermarkets Emergency-response system Military
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Example – Simulating Machine BreakdownsExample – Simulating Machine Breakdowns
The manager of a machine shop is concerned about machine breakdowns.
Historical data of breakdowns over the last 100 days is as follows
Simulate breakdowns for the manager for a 10-day period
Number of Breakdowns Frequency
0 10
1 30
2 25
3 20
4 10
5 5
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Simulation ProcedureSimulation ProcedureNumber of Breakdowns Frequency Probability Cum Prob Corresponding Random Numbers
0 10 0.10 0.10 01 to 101 30 0.30 0.40 11 to 402 25 0.25 0.65 41 to 653 20 0.20 0.85 61 to 854 10 0.10 0.95 86 to 955 5 0.05 1.00 96 to 00
100
Day Random Number Simulated # of Breakdowns1 90 42 73 33 82 34 16 15 94 46 92 47 68 38 5 09 84 310 91 4
19
Expected number of breakdowns = 1.9 per day
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Statistical AnalysisStatistical AnalysisDay # Replication 1 Replication 2 Replication 3 Replication 4 Replication 5 Replication 6 Replication 7 Replication 8 Replication 9 Replication 10
1 1 4 4 1 0 2 5 1 1 02 3 5 0 2 0 4 3 2 0 33 3 1 1 1 2 1 0 1 1 54 1 2 2 3 2 3 1 1 1 15 2 0 1 1 1 5 2 2 0 26 0 2 3 1 3 1 4 3 2 27 1 1 2 2 1 2 1 1 1 08 3 3 2 2 0 4 2 1 3 29 1 1 1 2 2 1 4 0 1 4
10 5 1 3 2 3 2 1 0 1 1
2.00 2.00 1.90 1.70 1.40 2.50 2.30 1.20 1.10 2.00
95 % confidence interval for mean breakdowns for the 10-day period is given by:
]955.1,665.1[)10
458.0(262.281.1
21;1
n
stx
n
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Monte Carlo SimulationMonte Carlo Simulation
Monte Carlo method: Probabilistic simulation technique used when a process has a random component
Identify a probability distribution
Setup intervals of random numbers to match probability distribution
Obtain the random numbers Interpret the results
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Example 2 – Simulating a Reorder Example 2 – Simulating a Reorder Policy Policy
The manager of a truck dealership wants to acquire some insight into how a proposed policy for reordering trucks might affect order frequency
Under the new policy, 2 trucks will be ordered every time the inventory of trucks is 5 or lower
Due to proximity between the dealership and the local office, orders can be filled overnight
The “historical” probability for daily demand is as follows
Simulate a reorder policy for the dealer for the next 10 days Assume a beginning inventory of 7 trucks
Demand (x) P(x)
0 0.50
1 0.40
2 0.10
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Example 2 SolutionsExample 2 Solutions
x P(x) Cum P(x) RN Day RN Demand Begin Inv End Inv Reorder0 0.5 0.5 01 to 50 1 81 1 7 6 01 0.4 0.9 51 to 90 2 20 0 6 6 02 0.1 1.0 91 to 00 3 82 1 6 5 2
4 34 0 7 7 05 85 1 7 6 06 35 0 6 6 07 10 0 6 6 08 14 0 6 6 09 84 1 6 5 210 92 2 7 5 2
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In-class Example using MS-ExcelIn-class Example using MS-Excel The time between mechanics’ requests for tools in a
AAMCO facility is normally distributed with a mean of 10 minutes and a standard deviation of 1 minute.
The time to fill requests is also normal with a mean of 9 minutes and a standard deviation of 1 minute.
Mechanics’ waiting time represents a cost of $2 per minute.
Servers represent a cost of $1 per minute. Simulate arrivals for the first 9 mechanic requests and
determine Service time for each request Waiting time for each request Total cost in handling all requests
Assume 1 server only
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AAMCO SolutionsAAMCO SolutionsInter-request time Cum Int-req time Service Time Service Begins Service Ends Wait Time
11.70 11.70 8.80 11.70 20.50 0.007.08 18.78 10.28 20.50 30.78 1.725.38 24.16 8.98 24.16 33.14 0.005.92 30.08 8.26 30.08 38.34 0.006.17 36.25 8.49 36.25 44.74 0.007.10 43.35 8.61 43.35 51.96 0.006.58 49.93 8.76 49.93 58.69 0.007.52 57.45 8.09 57.45 65.54 0.005.71 63.16 8.91 63.16 72.08 0.00
Sum of wait 1.72
Server cost/min 1Waiting cos/min 2
Total cost 75.52
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Discrete Event SimulationDiscrete Event SimulationExample 1 - A Simple Processing Example 1 - A Simple Processing
SystemSystem
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Discrete Event Simulation Discrete Event Simulation Example 2 - Electronic Assembly/Test Example 2 - Electronic Assembly/Test
SystemSystem
Produce two different sealed elect. units (A, B) Arriving parts: cast metal cases machined to accept the electronic parts Part A, Part B – separate prep areas Both go to Sealer for assembly, testing – then to Shipping (out) if OK,
or else to Rework Rework – Salvaged (and Shipped), or Scrapped
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Part APart A Interarrivals: expo (5) minutes From arrival point, proceed immediately to Part A Prep
area Process = (machine + deburr + clean) ~ tria (1,4,8)
minutes Go immediately to Sealer
Process = (assemble + test) ~ tria (1,3,4) min. 91% pass, go to Shipped; Else go to Rework
Rework: (re-process + testing) ~ expo (45) 80% pass, go to Salvaged; Else go to Scrapped
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Part BPart B Interarrivals: batches of 4, expo (30) min. Upon arrival, batch separates into 4 individual parts From arrival point, proceed immediately to Part B Prep
area Process = (machine + deburr +clean) ~ tria (3,5,10)
Go to Sealer Process = (assemble + test) ~ weib (2.5, 5.3) min.,
different from Part A, though at same station 91% pass, go to Shipped; Else go to Rework
Rework: (re-process + test) = expo (45) min. 80% pass, go to Salvaged; Else go to Scrapped
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Run Conditions, OutputRun Conditions, Output
Start empty & idle, run for four 8-hour shifts (1,920 minutes)
Collect statistics for each work area on Resource utilization Number in queue Time in queue
For each exit point (Shipped, Salvaged, Scrapped), collect total time in system (a.k.a. cycle time)
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Simulation Models Are BeneficialSimulation Models Are Beneficial
Systematic approach to problem solving Increase understanding of the problem Enable “what if” questions Specific objectives Power of mathematics and statistics Standardized format Require users to organize
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Simulation ProcessSimulation Process
1. Identify the problem
2. Develop the simulation model
3. Test the model
4. Develop the experiments
5. Run the simulation and evaluate results
6. Repeat 4 and 5 until results are satisfactory
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Different Kinds of SimulationDifferent Kinds of Simulation
Static vs. Dynamic Does time have a role in the model?
Continuous-change vs. Discrete-change Can the “state” change continuously or only at
discrete points in time? Deterministic vs. Stochastic
Is everything for sure or is there uncertainty? Most operational models:
Dynamic, Discrete-change, Stochastic
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Advantages of SimulationAdvantages of Simulation
Solves problems that are difficult or impossible to solve mathematically
Flexibility to model things as they are (even if messy and complicated)
Allows experimentation without risk to actual system
Ability to model long-term effects
Serves as training tool for decision makers
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Limitations of SimulationLimitations of Simulation
Does not produce optimum solution
Model development may be difficult
Computer run time may be substantial
Monte Carlo simulation only applicable to random systems
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Fitting Probability Distributions to Existing Fitting Probability Distributions to Existing DataData
Data Summary
Number of Data Points = 187Min Data Value = 3.2
Max Data Value = 12.6Sample Mean = 6.33Sample Std Dev = 1.51
Histogram Summary
Histogram Range = 3 to 13
Number of Intervals = 13
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ARENA – Input AnalyzerARENA – Input AnalyzerDistribution Summary
Distribution: Gamma Expression: 3 + GAMM(0.775, 4.29)
Square Error: 0.003873
Chi Square Test Number of intervals = 7 Degrees of freedom = 4
Test Statistic = 4.68 Corresponding p-value = 0.337
Kolmogorov-Smirnov Test Test Statistic = 0.0727
Corresponding p-value > 0.15
Data Summary
Number of Data Points = 187Min Data Value = 3.2
Max Data Value = 12.6Sample Mean = 6.33Sample Std Dev = 1.51
Histogram Summary
Histogram Range = 3 to 13Number of Intervals = 13
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Simulation in IndustrySimulation in Industry
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Course ConclusionsCourse Conclusions Recognize that not every tool is the best fit for every problem Pay attention to variability
Forecasting Inventory management - Deliveries from suppliers
Build flexibility into models Pay careful attention to technology
Opportunities Improvement in service and response times
Risks Costs involved Difficult to integrate Need for periodic updates Requires training
Garbage in, garbage out Results and recommendations you present are only as reliable as the model and its
inputs Most decisions involve tradeoffs Not a good idea to make decisions to the exclusion of known information