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Discrete/Stochastic Simulation. Using PROMODEL. Usages. Business Process Re-engineering Manufacturing Process Design Service Process Design Operations Supply chains As a planning tool As an innovation and improvement tool. Applications of Discrete Stochastic Simulation.

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Usages l.jpg
Usages

  • Business Process Re-engineering

  • Manufacturing Process Design

  • Service Process Design

  • Operations

  • Supply chains

  • As a planning tool

  • As an innovation and improvement tool


Applications of discrete stochastic simulation l.jpg
Applications of Discrete Stochastic Simulation

  • Resource management systems

  • Pollution management systems

  • Urban and regional planning

  • Transportation systems

  • Health systems

  • Criminal justice systems

  • Industrial systems

  • Education systems

  • eCommerce systems


What discrete stochastic simulation isn t l.jpg
What Discrete Stochastic Simulation isn’t

  • Stocks, states, rates, flows, information

  • Continuously changing variables

  • Causal loop diagramming

  • stock-and-flow diagramming


What discrete stochastic simulation is l.jpg
What Discrete Stochastic Simulation is

  • Probabilistic occurrences

  • Activity completions

  • Processes

  • Precedence relationships

  • Probabilistic routing

  • Events, Entities and Attributes


An example southwest airlines airline turn activities l.jpg
An example—Southwest Airlines airline turn--ACTIVITIES

  • Disembark passengers

  • Cabin cleanup

  • Embark passengers

  • Unload baggage

  • Load Baggage

  • Refuel

  • Remove waste

  • Refurbish snacks and drinks


Events for the airline gate turn l.jpg
EVENTS for the airline gate turn

  • Arrival at gate

  • Beginning of unloading

  • Completion of passenger unloading

  • Beginning of cleanup

  • Ending of cleanup

  • Beginning of passenger loading

  • Ending of passenger loading

  • Beginning of baggage unloading

  • Ending of baggage unloading


Activities events l.jpg
ACTIVITIES & EVENTS

  • Activities always have time duration.

    • That time duration is in general random

  • Events are instants in time

  • Activities are sometimes engaged in by entities

  • Activities, events and entities have attributes


Events entities and attributes l.jpg
Events, Entities and Attributes

  • Entities may be permanent or temporary

    • Customers, students, piece parts, messages, boxes, items,--TEMPORARY

    • Universities, cities, companies, facilities, servers, professors, service areas --PERMANENT

  • Both entities and events possess ATTRIBUTES

    • Attributes of a server entity—mean service time, std. dev. of service time, distribution type

    • attributes of an event--event type, no of entities assoc. with it


A typical service system scenario l.jpg
A typical service system scenario

  • In the early morning hours between 7 and 9 a.m., arrivals to convenience stores are larger than normal. If there are more than 6 people waiting in line, new arrivals will balk and go somewhere else. People arrive at the rate of 1 every second, but the time is exponentially distributed. Patrons shop for a time period that is uniformly distributed between 3 and 5 minutes.


Convenience store scenario continued l.jpg
Convenience Store Scenario, continued

  • It takes the checkout clerk an average of 43 sec to collect money from a customer and provide them with a receipt, but this time is normally distributed with a std dev. of 30 sec. . People will automatically enqueue themselves in front of the checkout stand.

  • The manager can hire a second clerk, who is less well paid but also much slower


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Convenience Store Scenario, continued

  • The store manager is interested in

    • the average waiting time of his patrons in the queue

    • the average number of customers that balked


With one store clerk l.jpg
With one store clerk:

  • average waiting time is 128 seconds

  • number of balked customers is 76 out of 1000 customers

  • Check out clerk is busy 86% of the time checking out customers


With two store clerks l.jpg
With two store clerks:

  • average waiting time is 42 secs for the first server

    • 69 secs for the second

  • There are no balked customers

  • Servers are busy 70% and 40% of the time, respectively


How does randomness come into play l.jpg
How does randomness come into play?

  • Probabilistic activity durations

  • Probabilistic routing “decisions”

  • Probabilistic arrivals


How is randomness created within a digital computer l.jpg
How is randomness created within a digital computer?

  • Monte Carlo--the computer generation of random numbers

    • Sample the clock?--no--not replicable

    • maintain a huge file of random numbers--no

      • Takes up too much space in primary memory

      • On secondary storage, its too slow

      • (when deciding to fetch from disk as opposed to primary memory, the time required. is 500,000 times longer

    • Use an ALGORITHM? --YES, YES


Why use an algorithm l.jpg
Why use an algorithm?

  • The sequence it generates will be deterministic

  • Doesn’t take up much space in primary storage

  • Takes up no space on secondary storage


An algorithm for generating random numbers l.jpg
An Algorithm for Generating Random Numbers

  • Must be fast (short and sweet)

  • Must be capable of generating numbers that have all of the characteristics of randomness, but in fact are deterministic

  • Multiplicative Congruence is one method


About random numbers l.jpg
About Random Numbers

  • Uniform on the interval zero to one

  • They are completely independent and therefore un-correlated

  • We represent them this way: U(0,1)


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Multiplicative Congruence Algorithm

  • CI+1 = K*Ci

    function random(float u, int I)

    I = I * 1220703125;

    if I<0 then

    I = I + 2147483647 + 1;

    else

    U = I * 0.4656613E-9;

    return “and” end;


Notes l.jpg
Notes

  • Generates a sequence on the entire interval of 32-bit integers--0 to 2147483647

  • Maps these onto the real interval of 0 to 1

  • If the first multiplication causes integer overflow, the resultant number I will be negative--it is made positive by adding the largest 32-bit integer representable +1

    The last multiplication is like dividing the number by the largest integer possible

    1/2147483647 = .4656613x10 to the minus 9


You can easily generate random numbers in an excel spreadsheet using the function rand l.jpg
You can easily generate random numbers in an EXCEL spreadsheet using the function RAND()


What about non uniform random numbers l.jpg
What about non-uniform random numbers? spreadsheet using the function RAND()

Exponential

Normal

Gamma

Poisson

Lognormal

Rectangular

Triangular


One answer use the inverse transformation method l.jpg
ONE ANSWER: Use the inverse transformation method spreadsheet using the function RAND()

Every non-uniform random variate has an associated cumulative distribution function F(x) whose values are contained within the interval 0 to 1 and whose values are uniformly distributed over this interval

If x is a non uniform random variate, y = F(x) is uniformly distributed over the interval 0 to 1.


Strategy l.jpg
Strategy spreadsheet using the function RAND()

If the inverse of the cumulative distribution function F(x) exists so that x = F-1(y) can be determined, then

1) simply generate a random number uniformly distributed on the interval 0 to 1

2) call this number y and apply the inverse transformation F-1(y) to obtain a random number x with the appropriate distribution.


Exponential random variates l.jpg
Exponential Random variates spreadsheet using the function RAND()

1) generate a random number U using the program given above

2)then apply EXPRND = -XMEAN * ALOG(U)

(This is simply using the inverse distribution method)


When analytic inverses of the cumulative distribution function are unavailable l.jpg
When Analytic inverses of the cumulative distribution function are unavailable

You can use a table function

You can use specialized algorithms that have been developed by academics over thirty-five years of cumulative research


Outputs l.jpg
Outputs function are unavailable

  • The animation reveals bottlenecks

  • We are also interested in

    • idleness, productivity, cycle time (time in the system), wait time, blocked time

    • number of trips made in a given period of time,

    • system throughput within a given period of time

  • We can get this from the statistical reports provided after the simulation is finished


Discrete stochastic simulation time advance l.jpg
Discrete stochastic simulation time advance function are unavailable

  • Time is advanced from event to event.

  • The only time instants looked at are the event times

  • The events are stored chronologically in time in an event file known as an “event calendar”

  • Corresponding to each event type is an event subroutine

  • When an event occurs, its subroutine is called


Discrete stochastic simulation as l.jpg
Discrete-stochastic simulation as function are unavailable

  • A statistical experiment

  • Running times must be long enough to ensure sufficient samples are collected

  • Several runs are often averaged together

  • The starting random number seeds are changed and the model is rerun

  • The basic idea is to get the variance to converge to the actual real-world variance


Another scenario l.jpg
Another scenario function are unavailable

  • A mufflers-shocks-brakes shop is turning away business. It is considering hiring another mechanic or adding another bay. It currently has four bays.


Another scenario32 l.jpg
Another scenario function are unavailable

  • A shipping company has just picked up additional customers and needs to add capacity. At its loading warehouse, it has four loading docks. It also has 10 trucks. Trucks currently wait upon return for four hours before they can go out on another trip. Should the company add docks, remove trucks, or both.


Another scenario33 l.jpg
Another scenario function are unavailable

  • A ski rental shop fits customers for boots and then skis. It currently has four people working in the boot area and four people working in the ski area. Lines are very long and waiting times unacceptable. Should the shop hire more help or just shift some of its existing help from skis to boots or vice versa.


Go back to 7 11 store example l.jpg
GO BACK TO 7-11 STORE example function are unavailable

  • Consider a 7-11 store in which the 7-9 a.m. period is of interest. Management is considering hiring a second clerk. Patrons arrive at the rate of one every 45 secs. Arrivals are Exponentially distributed. Patrons shop for a period that is uniformly distributed between 3 and 5 minutes. The check out time for each customer is normal with a mean of 43 secs and a std. dev. of 30 secs. Customers who encounter a queue of six customers or more upon arrival will walk away without shopping.


What are the activities l.jpg
What are the activities? function are unavailable

  • Arrivals

  • Shopping

  • Checkout

  • What about waiting in Queue?

    • This is not an activity

    • This is handled automatically by the simulation


What units on time l.jpg
What units on time? function are unavailable

  • Secs or mins?

  • Let’s go with SECONDS

  • We must be consistent!!!!


Must also identify l.jpg
Must also identify function are unavailable

  • Locations—points assoc with the starting and stopping events of an activity

  • Path network—the network the entity travels

  • Resources—permanent entities that act on ordinary temporary entities

  • Processes—the activities


Promodel l.jpg
PROMODEL function are unavailable

  • SELECT BACKGROUND--optional

  • BUILD-->locations

  • BUILD-->entities

  • BUILD-->PATH NETWORK

  • BUILD-->resources

  • BUILD-->processes and routing

  • BUILD-->arrivals

  • RUN IT


Locations l.jpg
Locations function are unavailable

  • Places where an event of importance to the model occurs

    • Like an arrival

    • A beginning of customer checkout

    • An ending of customer checkout


Entities l.jpg
Entities function are unavailable

  • These are the temporary items that pass through the model of the system

  • Chits

  • Mail pieces

  • Piece parts

  • Students

  • Cars

  • People


Path network l.jpg
Path network function are unavailable

  • The network that will be followed by the entities and/or the resources


Resources l.jpg
Resources function are unavailable

  • Mobile permanent entities that can move over a network


Processes l.jpg
Processes function are unavailable

  • A process is required everywhere the entity undergoes an operation

  • An exit process is always required


Routing l.jpg
Routing function are unavailable

  • You must specify how the entities move through the model

  • Usually you inform PROMODEL what path network to use


Arrivals l.jpg
Arrivals function are unavailable

  • The statistics of the arrival process for each entity type must be communicated to PROMODEL


Now let s look at promodel l.jpg
Now let’s look at PROmodel function are unavailable


Slide47 l.jpg

  • Exercise 1. (15 points) function are unavailable A local convenience store has a self-service island from which it dispenses gasoline. Two lines of cars may form on either side of the island. The island will accommodate no more than two cars being filled with gas on a single side. There is space for no more than three cars in each of the two queues of cars waiting for each of the two service areas.


Slide48 l.jpg

  • Cars arrive at the rate of one every minute with a distribution that is exponential. Service times are normal with a mean of seven minutes and a standard deviation of two minutes. Cars will drive away if more than six cars total are either waiting or in service (regardless of the line they are in). Once cars have entered the store’s gasoline facility, they will en-queue themselves into the shortest queue. Formulate a model in BLOCKS to determine how many cars are turned away in a day. For BRANCH/ TRANSFERS, be sure to indicate the type, such as UNCONDITIONALLY to block 12. Assuming the store is open 24 hours, setup the model to determine how many cars are turned away in one 24-hour day.


Promodel49 l.jpg

Promodel distribution that is exponential. Service times are normal with a mean of seven minutes and a standard deviation of two minutes. Cars will drive away if more than six cars total are either waiting or in service (regardless of the line they are in). Once cars have entered the store’s gasoline facility, they will en-queue themselves into the shortest queue. Formulate a model in BLOCKS to determine how many cars are turned away in a day. For BRANCH/ TRANSFERS, be sure to indicate the type, such as UNCONDITIONALLY to block 12. Assuming the store is open 24 hours, setup the model to determine how many cars are turned away in one 24-hour day.

What do we need to know??


What the following are l.jpg
What the following are distribution that is exponential. Service times are normal with a mean of seven minutes and a standard deviation of two minutes. Cars will drive away if more than six cars total are either waiting or in service (regardless of the line they are in). Once cars have entered the store’s gasoline facility, they will en-queue themselves into the shortest queue. Formulate a model in BLOCKS to determine how many cars are turned away in a day. For BRANCH/ TRANSFERS, be sure to indicate the type, such as UNCONDITIONALLY to block 12. Assuming the store is open 24 hours, setup the model to determine how many cars are turned away in one 24-hour day.

  • Locations

  • Entities

  • Path networks

  • Resources

  • Processes

  • Arrivals


What has to be specified before resources can be specified l.jpg
What has to be specified before resources can be specified distribution that is exponential. Service times are normal with a mean of seven minutes and a standard deviation of two minutes. Cars will drive away if more than six cars total are either waiting or in service (regardless of the line they are in). Once cars have entered the store’s gasoline facility, they will en-queue themselves into the shortest queue. Formulate a model in BLOCKS to determine how many cars are turned away in a day. For BRANCH/ TRANSFERS, be sure to indicate the type, such as UNCONDITIONALLY to block 12. Assuming the store is open 24 hours, setup the model to determine how many cars are turned away in one 24-hour day.

  • Path networks



In order to double the capacity of the number of turning machines and machining centers you would l.jpg
In order to double the capacity of the number of turning machines and machining centers you would

  • Go to locations and increase the capacity from 1 to two for both of these locations


Failed arrivals means l.jpg
Failed Arrivals means machines and machining centers you would

  • Arrivals that could not even get their foot in the door, in this case because the pallet was full


The important measures for this model were l.jpg
The important measures for this model were… machines and machining centers you would

  • Throughput for the product

  • Utilization of the resource

  • Utilization of the locations

  • Failed arrivals

  • Amount of blocked time there is


On the final you will be given a scenario like the ones above and asked to determine l.jpg
On the final you will be given a scenario like the ones above and asked to determine

  • Locations

  • Entities

  • Path networks—may be asked to draw these

  • Resources

  • Processes

  • Arrivals


You will also need to know l.jpg
You will also need to know above and asked to determine

  • what events and activities are

  • How random numbers are generated

  • How random variates (non-uniform) are generated

  • What is meant by MONTE CARLO