Matakuliah
Tahun
: D0174/ Pemodelan Sistem dan Simulasi
: Tahun 2009
Pertemuan 24
DISCRETE-EVENT SYSTEM SIMULATION
Learning Objectives
• Implementasi Discrete-Even Simulation
• Tahapan proses simulasi
• Looping Simulation
Story
•
Discrete event simulation
– Simulation time != real time
•
Key ideas:
– A Queue
• A Queue is a queue, no matter how implemented.
– Different kinds of random
– Straightening time
• Inserting it into the right place
• Sorting it afterwards
• Building a discrete event simulation
– Graphics as the representation, not the real thing: The Model and the View
Imagine the simulation…
•
There are three Trucks that bring product from the Factory.
– On average, they take 3 days to arrive.
– Each truck brings somewhere between 10 and 20 products—all equally
likely.
•
We’ve got five Distributors who pick up product from the Factory with
orders.
– Usually they want from 5 to 25 products, all equally likely.
•
•
It takes the Distributors an average of 2 days to get back to the market, and
an average of 5 days to deliver the products.
Question we might wonder: How much product gets sold like this?
Don’t use a Continuous Simulation
•
•
We don’t want to wait that number of days in real time.
We don’t even care about every day.
– There will certainly be timesteps (days) when nothing happens of interest.
•
We’re dealing with different probability distributions.
– Some uniform, some normally distributed.
•
Things can get out of synch
– A Truck may go back to the factory and get more product before a
Distributor gets back.
– A Distributor may have to wait for multiple trucks to fulfill orders (and other
Distributors might end up waiting in line)
We use a Discrete Event Simulation
• We don’t simulate every moment continuously.
• We simulate discrete events.
What’s the difference?
No time loop
• In a discrete event simulation: There is no time loop.
– There are events that are scheduled.
– At each run step, the next scheduled event with the lowest time
gets processed.
• The current time is then that time, the time that that
event is supposed to occur.
• Key: We have to keep the list of scheduled events sorted
(in order)
What’s the difference?
Agents don’t act()
• In a discrete event simulations, agents don’t act().
– Instead, they wait for events to occur.
– They schedule new events to correspond to the next thing that
they’re going to do.
• Key: Events get scheduled according to different
probabilities.
What’s the difference?
Agents get blocked
•
•
Agents can’t do everything that they want to do.
If they want product (for example) and there isn’t any, they get blocked.
– They can’t schedule any new events until they get unblocked.
•
Many agents may get blocked awaiting the same resource.
– More than one Distributor may be awaiting arrival of Trucks
•
Key: We have to keep track of the Distributors waiting in line (in the queue)
Key Ideas
• A Queue
– A Queue is a queue, no matter how implemented.
• Different kinds of random
• Straightening time
– Inserting it into the right place
– Sorting it afterwards
Key idea #1:
Introducing a Queue
• First-In-First-Out List
– First person in line is first person served
I got here
third!
This is the tail
of the queue
I got here
second!
I got here
first!
This is the
front or head
of the queue
First-in-First-out
• New items only get added to the tail.
– Never in the middle
• Items only get removed from the head.
I got here
third!
This is the tail
of the queue
I got here
second!
I got here
first!
This is the
front or head
of the queue
As items leave, the head shifts
I got here
third!
I got here
second!
This is the tail
of the queue
Now, this is
the front or
head of the
queue
I got here
first! AND
NOW I’M UP!
Served!
As new items come in, the tail shifts
I got here
fourth!
Now, this is
the tail of the
queue
I got here
third!
I got here
second!
Now, this is
the front or
head of the
queue
What can we do with queues?
• push(anObject): Tack a new object onto the tail of the
queue
• pop(): Pull the end (head) object off the queue.
• peek(): Get the head of the queue, but don’t remove it
from the queue.
• size(): Return the size of the queue
Building a Queue
> Queue line = new Queue();
> line.push("Fred");
> line.push("Mary");
> line.push("Jose");
> line.size()
3
Accessing a Queue
> line.peek()
"Fred"
> line.pop()
"Fred"
> line.peek()
"Mary"
> line.pop()
"Mary"
> line.peek()
"Jose"
> line.pop()
"Jose"
> line.pop()
java.util.NoSuchElementException:
We don’t really
want to peek()
or pop() an
empty queue,
so we should
probably check
its size first.
Building a
Queue
import java.util.*; // LinkedList representation
/**
* Implements a simple queue
**/
public class Queue {
/** Where we'll store our elements */
public LinkedList elements;
/// Constructor
public Queue(){
elements = new LinkedList();
}
Queue methods
/// Methods
/** Push an object onto the Queue */
public void push(Object element){
elements.addFirst(element);
}
/** Peek at, but don't remove, top of queue */
public Object peek(){
return elements.getLast();}
/** Pop an object from the Queue */
public Object pop(){
Object toReturn = this.peek();
elements.removeLast();
return toReturn;
}
/** Return the size of a queue */
public int size() { return elements.size();}
We’re using a linked
list to implement the
Queue.
The front of the
LinkedList is the tail.
The last of the
LinkedList is the
head.
A queue is a queue, no matter what lies
beneath.
• Our description of the queue minus the implementation is an
example of an abstract data type (ADT).
– An abstract type is a description of the methods that a data structure
knows and what the methods do.
• We can actually write programs that use the abstract data type
without specifying the implementation.
– There are actually many implementations that will work for the given
ADT.
– Some are better than others.
Array-oriented Queue
/**
* Implements a simple queue
**/
public class Queue2 {
private static int ARRAYSIZE = 20;
/** Where we'll store our elements */
private Object[] elements;
/** The indices of the head and tail */
private int head;
private int tail;
Queue = array + head index +
tail index
/// Constructor
public Queue2(){
elements = new Object[ARRAYSIZE];
head = 0;
tail = 0;
}
Queue2
methods
As the queue gets
pushed and
popped, it moves
down the array.
/** Push an object onto the Queue */
public void push(Object element){
if ((tail + 1) >= ARRAYSIZE) {
System.out.println("Queue underlying implementation failed");
}
else {
// Store at the tail,
// then increment to a new open position
elements[tail] = element;
tail++; } }
/** Peek at, but don't remove, top of queue */
public Object peek(){
return elements[head];}
/** Pop an object from the Queue */
public Object pop(){
Object toReturn = this.peek();
if (((head + 1) >= ARRAYSIZE) ||
(head > tail)) {
System.out.println("Queue underlying implementation failed.");
return toReturn;
}
else {
// Increment the head forward, too.
head++;
return toReturn;}}
/** Return the size of a queue */
public int size() { return tail-head;}
Same methods,
same behavior
But can only handle up
to 20 elements in the
queue! Less if pushing
and popping. Could shift
elements to always
allow 20.
Not as good an
implementation as the
linked list
implementation. (But
uses less memory.)
Welcome to DrJava.
> Queue2 line = new Queue2();
> line.push("Mary")
> line.push("Kim")
> line.push("Ron")
> line.peek()
"Mary"
> line.pop()
"Mary"
> line.peek()
"Kim"
> line.size()
2
> line.pop()
"Kim"
> line.pop()
"Ron"
Key idea #2:
Different kinds of random
• We’ve been dealing with uniform random distributions up
until now, but those are the least likely random
distribution in real life.
• How can we generate some other distributions, including
some that are more realistic?
Visualizing
a uniform
distribution
import java.util.*; // Need this for Random
import java.io.*; // For BufferedWriter
public class GenerateUniform {
public static void main(String[] args) {
Random rng = new Random(); // Random Number Generator
BufferedWriter output=null; // file for writing
// Try to open the file
try {
// create a writer
output =
new BufferedWriter(new FileWriter("D:/cs1316/uniform.txt"));
} catch (Exception ex) {
System.out.println("Trouble opening the file.");
}
// Fill it with 500 numbers between 0.0 and 1.0, uniformly distributed
for (int i=0; i < 500; i++){
try{
output.write("\t"+rng.nextFloat());
output.newLine();
} catch (Exception ex) {
System.out.println("Couldn't write the data!");
System.out.println(ex.getMessage());
}
}
// Close the file
try{
output.close();}
catch (Exception ex)
{System.out.println("Something went wrong closing the file");}
By writing out a
tab and the
integer, we don’t
have to do the
string conversion.
}
}
How do we view a distribution?
A Histogram
Then graph the result
A Uniform Distribution
Frequency
70
60
50
40
Frequency
30
20
10
1
0.
8
0.
6
0.
4
0.
2
0
0
A Normal
Distribution
// Fill it with 500 numbers between -1.0 and 1.0, normally distributed
for (int i=0; i < 500; i++){
try{
output.write("\t"+rng.nextGaussian());
output.newLine();
} catch (Exception ex) {
System.out.println("Couldn't write the data!");
System.out.println(ex.getMessage());
}
}
Graphing the normal distribution
Frequency
30
25
20
15
Frequency
10
5
2
0.
4
0.
8
1.
2
1.
6
0
-2
-1
.6
-1
.2
-0
.8
-0
.4
0
The end aren’t
actually high—
the tails go
further.
How do we shift the distribution where we
want it?
// Fill it with 500 numbers with a mean of 5.0 and a
//larger spread, normally distributed
for (int i=0; i < 500; i++){
try{
output.write("\t"+((range * rng.nextGaussian())+mean));
output.newLine();
} catch (Exception ex) {
System.out.println("Couldn't write the data!");
System.out.println(ex.getMessage());
}
}
Multiply the random nextGaussian()
by the range you want, then add the
mean to shift it where you want it.
A new normal distribution
Frequency
18
16
14
12
10
8
6
4
2
0
10
9.5
9
8.5
8
7.5
7
6.5
6
5.5
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
-0.5
-1
-1.5
-2
Frequency
Key idea #3: Straightening Time
• Straightening time
– Inserting it into the right place
– Sorting it afterwards
• We’ll actually do these in reverse order:
– We’ll add a new event, then sort it.
– Then we’ll insert it into the right place.
Exercising
an EventQueue
public class EventQueueExercisor {
public static void main(String[] args){
// Make an EventQueue
EventQueue queue = new EventQueue();
// Now, stuff it full of events, out of order.
SimEvent event = new SimEvent();
event.setTime(5.0);
queue.add(event);
We’re stuffing the EventQueue
with events whose times are out of
order.
event = new SimEvent();
event.setTime(2.0);
queue.add(event);
event = new SimEvent();
event.setTime(7.0);
queue.add(event);
event = new SimEvent();
event.setTime(0.5);
queue.add(event);
event = new SimEvent();
event.setTime(1.0);
queue.add(event);
// Get the events back, hopefull in order!
for (int i=0; i < 5; i++) {
event = queue.pop();
System.out.println("Popped event time:"+event.getTime());
}
}
}
If it works right, should look like this:
Welcome to DrJava.
> java EventQueueExercisor
Popped event time:0.5
Popped event time:1.0
Popped event time:2.0
Popped event time:5.0
Popped event time:7.0
Implementing an EventQueue
import java.util.*;
/**
* EventQueue
* It's called an event "queue," but it's not really.
* Instead, it's a list (could be an array, could be a linked list)
* that always keeps its elements in time sorted order.
* When you get the nextEvent, you KNOW that it's the one
* with the lowest time in the EventQueue
**/
public class EventQueue {
private LinkedList elements;
/// Constructor
public EventQueue(){
elements = new LinkedList();
}
Mostly, it’s a queue
public SimEvent peek(){
return (SimEvent) elements.getFirst();}
public SimEvent pop(){
SimEvent toReturn = this.peek();
elements.removeFirst();
return toReturn;}
public int size(){return elements.size();}
public boolean empty(){return this.size()==0;}
Two options for add()
/**
* Add the event.
* The Queue MUST remain in order, from lowest time to highest.
**/
public void add(SimEvent myEvent){
// Option one: Add then sort
elements.add(myEvent);
this.sort();
//Option two: Insert into order
//this.insertInOrder(myEvent);
}
There are lots of sorts!
• Lots of ways to keep things in order.
– Some are faster – best are O(n log n)
– Some are slower – they’re always O(n2)
– Some are O(n2) in the worst case, but on average, they’re better
than that.
• We’re going to try an insertion sort
How an insertion sort works
• Consider the event at some position (1..n)
• Compare it to all the events before that position backwards—
towards 0.
– If the comparison event time is LESS THAN the considered event time,
then shift the comparison event down to make room.
– Wherever we stop, that’s where the considered event goes.
• Consider the next event…until done
Insertion Sort
public void sort(){
// Perform an insertion sort
// For comparing to elements at smaller indices
SimEvent considered = null;
SimEvent compareEvent = null; // Just for use in
loop
// Smaller index we're comparing to
int compare;
// Start out assuming that position 0 is "sorted"
// When position==1, compare elements at indices
0 and 1
// When position==2, compare at indices 0, 1, and
2, etc.
for (int position=1; position < elements.size();
position++){
considered = (SimEvent) elements.get(position);
// Now, we look at "considered" versus the
elements
// less than "compare"
compare = position;
Trace this out to convince yourself it
works!
// While the considered event is greater than the compared
event ,
// it's in the wrong place, so move the elements up one.
compareEvent = (SimEvent) elements.get(compare-1);
while (compareEvent.getTime() >
considered.getTime()) {
elements.set(compare,elements.get(compare-1));
compare = compare-1;
// If we get to the end of the array, stop
if (compare <= 0) {break;}
// else get ready for the next time through the loop
else {compareEvent = (SimEvent) elements.get(compare1);}
}
// Wherever we stopped, this is where "considered" belongs
elements.set(compare,considered);
} // for all positions 1 to the end
} // end of sort()
Useful Links on Sorting
• http://ciips.ee.uwa.edu.au/~morris/Year2/PLDS210/sortin
g.html
• http://www.cs.ubc.ca/spider/harrison/Java/sortingdemo.html
• http://www.cs.brockport.edu/cs/java/apps/sorters/inserts
ort.html
Recommended
These include animations that help to
see how it’s all working
Option #2: Put it in the right place
/**
* Add the event.
* The Queue MUST remain in order, from lowest time to highest.
**/
public void add(SimEvent myEvent){
// Option one: Add then sort
//elements.add(myEvent);
//this.sort();
//Option two: Insert into order
this.insertInOrder(myEvent);
}
insertInOrder()
/**
* Put thisEvent into elements, assuming
* that it's already in order.
**/
public void insertInOrder(SimEvent thisEvent){
SimEvent comparison = null;
// Have we inserted yet?
boolean inserted = false;
for (int i=0; i < elements.size(); i++){
comparison = (SimEvent) elements.get(i);
Again, trace it out to convince
yourself that it works!
// Assume elements from 0..i are less than thisEvent
// If the element time is GREATER, insert here and
// shift the rest down
if (thisEvent.getTime() < comparison.getTime()) {
//Insert it here
inserted = true;
elements.add(i,thisEvent);
break; // We can stop the search loop
}
} // end for
// Did we get through the list without finding something
// greater? Must be greater than any currently there!
if (!inserted) {
// Insert it at the end
elements.addLast(thisEvent);}
}
Finally: A Discrete Event Simulation
• Now, we can assemble queues, different kinds of
random, and a sorted EventQueue to create a discrete
event simulation.
Running a DESimulation
Welcome to DrJava.
> FactorySimulation fs = new FactorySimulation();
> fs.openFrames("D:/temp/");
> fs.run(25.0)
What we see (not much)
The detail tells the story
Time:
1.7078547183397625
Time:
1.7078547183397625
>>> Timestep: 1
Time:
1.727166341118611
Time:
1.727166341118611
>>> Timestep: 1
Time:
1.8778754913001443
Time:
1.8778754913001443
>>> Timestep: 1
Time:
1.889475045031698
Time:
1.889475045031698
>>> Timestep: 1
Time:
3.064560375192933
Time:
3.064560375192933
>>> Timestep: 3
Time:
3.444420374970288
Time:
3.444420374970288
Time:
3.444420374970288
>>> Timestep: 3
Time:
3.8869697922832698
Time:
3.8869697922832698
Time:
3.8869697922832698
>>> Timestep: 3
Time:
4.095930381479024
>>> Timestep: 4
Time:
4.572840072576855
Time:
4.572840072576855
Time:
4.572840072576855
Distributor: 0
Distributor: 0
Arrived at warehouse
is blocking
Distributor: 3
Distributor: 3
Arrived at warehouse
is blocking
Distributor: 4
Distributor: 4
Arrived at warehouse
is blocking
Distributor: 2
Distributor: 2
Arrived at warehouse
is blocking
Distributor: 1
Distributor: 1
Arrived at warehouse
is blocking
Truck: 2
Distributor: 0
Distributor: 0
Arrived at warehouse with load
unblocked!
Gathered product for orders of
13
Truck: 0
Distributor: 3
Distributor: 3
Arrived at warehouse with load
unblocked!
Gathered product for orders of
18
Distributor: 0
Arrived at market
Truck: 1
Distributor: 4
Distributor: 4
Arrived at warehouse with load
unblocked!
Gathered product for orders of
Notice that
time 2 never
occurs!
11
12
20
19
What questions we can answer
• How long do distributors wait?
– Subtract the time that they unblock from the time that they block
• How much product sits in the warehouse?
– At each time a distributor leaves, figure out how much is left in the
warehouse.
• How long does the line get at the warehouse?
– At each block, count the size of the queue.
• Can we move more product by having more distributors or more
trucks?
– Try it!
How DESimulation works
Turtle
LinkedList
-heading
-XPos
-YPos
+forward()
+turn()
+setColor()
+setPenDown()
+frames
*
1
1
+init()
+die()
+getClosest()
+countInRange()
+act()
1
#output
*
#simulation
*
1
-blocked
-blocked
+isBlocked()
+isReady()
+validTime()
+waitFor()
+unblocked()
+processEvent()
-world
Simulation
Queue
DEAgent
+show()
+replay()
#agents
Agent
#speed
FrameSequence
+push()
+peek()
+pop()
+empty()
+size()
1
+getAgents()
+add()
+remove()
+openFrames()
+setUp()
+openFile()
+run()
+endStep()
+lineForFile()
+closeFile()
1
+setPicture()
1
EventQueue
*
-events
1
*
Resource
-amount
+amountAvailable()
+consume()
+add()
+addToList()
World
DESimluation
-now
+getTime()
+addEvent()
+log()
+run()
*
+peek()
+add()
+pop()
+size()
+empty()
+insertInOrder()
+sort()
FactorySimulation: Extend a few classes
Turtle
LinkedList
-heading
-XPos
-YPos
+forward()
+turn()
+setColor()
+setPenDown()
+frames
*
1
1
1
#output
*
#simulation
*
1
Queue
-blocked
DEAgent
-blocked
+isBlocked()
+isReady()
+validTime()
+waitFor()
+unblocked()
+processEvent()
+push()
+peek()
+pop()
+empty()
+size()
*
+getAgents()
+add()
+remove()
+openFrames()
+setUp()
+openFile()
+run()
+endStep()
+lineForFile()
+closeFile()
-amountOrdered
+newLoad()
+tripTime()
+init()
+processEvents()
+newOrders()
+timeToDeliver()
+tripTime()
+init()
+processEvents()
+isReady()
+unblocked()
+setPicture()
1
EventQueue
*
+peek()
+add()
+pop()
+size()
+empty()
+insertInOrder()
+sort()
-events
1
Resource
DESimulation
+amountAvailable()
+consume()
+add()
+addToList()
Truck
1
World
1
-amount
Distributor
-load
-world
Simulation
+init()
+die()
+getClosest()
+countInRange()
+act()
+show()
+replay()
#agents
Agent
#speed
FrameSequence
1
-now
+getTime()
+addEvent()
+log()
+run()
-product
*
FactorySimulation
*
+setUp()
+getFactory()
DESimulation: Sets the Stage
• DESimulation calls setUp to create agents and schedule
the first events.
• It provides log for writing things out to the console and a
text file.
• When it run()’s, it processes each event in the event
queue and tells the corresponding agent to process a
particular message.
What a DESimulation does:
// While we're not yet at the stop time,
// and there are more events to process
while ((now < stopTime) && (!events.empty())) {
topEvent = events.pop();
// Whatever event is next, that time is now
now = topEvent.getTime();
// Let the agent now that its event has occurred
topAgent = topEvent.getAgent();
topAgent.processEvent(topEvent.getMessage());
// repaint the world to show the movement
// IF there is a world
if (world != null) {
world.repaint();}
// Do the end of step processing
this.endStep((int) now);
}
As long as there are
events in the queue,
and we’re not at the
stopTime:
Grab an event.
Make it’s time “now”
Process the event.
What’s an Event (SimEvent)?
/**
* SimulationEvent (SimEvent) -- an event that occurs in a simulation,
* like a truck arriving at a factory, or a salesperson leaving the
* market
**/
public class SimEvent{
/// Fields ///
/** When does this event occur? */
public double time;
/** To whom does it occur? Who should be informed when it occurred? */
public DEAgent whom;
/** What is the event? We'll use integers to represent the meaning
* of the event -- the "message" of the event.
* Each agent will know the meaning of the integer for themselves.
**/
public int message;
It’s a time, an
Agent, and an
integer that the
Agent will
understand as a
message
DEAgent: Process events, block if needed
• DEAgents define the constants for messages: What will be the main
events for this agent?
• If the agent needs a resource, it asks to see if it’s available, and if
not, it blocks itself.
• It will be told to unblock when it’s ready.
• Agents are responsible for scheduling their OWN next event!
An Example: A Truck
/**
* Truck -- delivers product from Factory
* to Warehouse.
**/
public class Truck extends DEAgent {
/////// Constants for Messages
public static final int FACTORY_ARRIVE = 0;
public static final int WAREHOUSE_ARRIVE = 1;
////// Fields /////
/**
* Amount of product being carried
**/
public int load;
How Trucks start
/**
* Set up the truck
* Start out at the factory
**/
public void init(Simulation thisSim){
// Do the default init
super.init(thisSim);
this.setPenDown(false); // Pen up
this.setBodyColor(Color.green); // Let green deliver!
// Show the truck at the factory
this.moveTo(30,350);
// Load up at the factory, and set off for the warehouse
load = this.newLoad();
((DESimulation) thisSim).addEvent(
new SimEvent(this,tripTime(),WAREHOUSE_ARRIVE));
}
The truck gets a load,
then schedules itself
to arrive at the
Warehouse.
tripTime() uses the normal distribution
/** A trip distance averages 3 days */
public double tripTime(){
double delay = randNumGen.nextGaussian()+3;
if (delay < 1)
// Must take at least one day
{return 1.0+((DESimulation) simulation).getTime();}
else {return delay+((DESimulation) simulation).getTime();}
}
newLoad() uses uniform
/** A new load is between 10 and 20 on a uniform
distribution */
public int newLoad(){
return 10+randNumGen.nextInt(11);
}
How a Truck processes Events
/**
* Process an event.
* Default is to do nothing with it.
**/
public void processEvent(int message){
switch(message){
case FACTORY_ARRIVE:
// Show the truck at the factory
((DESimulation) simulation).log(this.getName()+"\t Arrived at factory");
this.moveTo(30,350);
// Load up at the factory, and set off for the warehouse
load = this.newLoad();
((DESimulation) simulation).addEvent(
new SimEvent(this,tripTime(),WAREHOUSE_ARRIVE));
break;
Truck Arriving at the Warehouse
case WAREHOUSE_ARRIVE:
// Show the truck at the warehouse
((DESimulation) simulation).log(this.getName()+"\t Arrived at warehouse with load
\t"+load);
this.moveTo(50,50);
// Unload product -- takes zero time (unrealistic!)
((FactorySimulation) simulation).getProduct().add(load);
load = 0;
// Head back to factory
((DESimulation) simulation).addEvent(
new SimEvent(this,tripTime(),FACTORY_ARRIVE));
break;
What Resources do
• They keep track of what amount they have available (of whatever
the resource is).
• They keep a queue of agents that are blocked on this resource.
• They can add to the resource, or have it consume(d).
– When more resource comes in, the head of the queue gets asked if it’s
enough. If so, it can unblock.
How Resources alert agents
/**
* Add more produced resource.
* Is there enough to unblock the first
* Agent in the Queue?
**/
public void add(int production) {
amount = amount + production;
if (!blocked.empty()){
// Ask the next Agent in the queue if it can be unblocked
DEAgent topOne = (DEAgent) blocked.peek();
// Is it ready to run given this resource?
if (topOne.isReady(this)) {
// Remove it from the queue
topOne = (DEAgent) blocked.pop();
// And tell it it’s unblocked
topOne.unblocked(this);
}
}
}
An example blocking agent: Distributor
/**
* Distributor -- takes orders from Market to Warehouse,
* fills them, and returns with product.
**/
public class Distributor extends DEAgent {
/////// Constants for Messages
public static final int MARKET_ARRIVE = 0;
public static final int MARKET_LEAVE = 1;
public static final int WAREHOUSE_ARRIVE = 2;
/** AmountOrdered so-far */
int amountOrdered;
Distributors start in the Market
public void init(Simulation thisSim){
//First, do the normal stuff
super.init(thisSim);
this.setPenDown(false); // Pen up
this.setBodyColor(Color.blue); // Go Blue!
// Show the distributor in the market
this.moveTo(600,460); // At far right
// Get the orders, and set off for the warehouse
amountOrdered = this.newOrders();
((DESimulation) thisSim).addEvent(
new SimEvent(this,tripTime(),WAREHOUSE_ARRIVE));
}
Distributors have 3 events
• Arrive in Market: Schedule how long it’ll take to deliver.
• Leave Market: Schedule arrive at the Factory
• Arrive at Warehouse: Is there enough product available?
If not, block and wait for trucks to bring enough product.
Processing Distributor Events
/**
* Process an event.
* Default is to do nothing with it.
**/
public void processEvent(int message){
switch(message){
case MARKET_ARRIVE:
// Show the distributor at the market, far left
((DESimulation) simulation).log(this.getName()+"\t Arrived at market");
this.moveTo(210,460);
// Schedule time to deliver
((DESimulation) simulation).addEvent(
new SimEvent(this,timeToDeliver(),MARKET_LEAVE));
break;
Leaving the Market
case MARKET_LEAVE:
// Show the distributor at the market, far right
((DESimulation) simulation).log(this.getName()+"\t Leaving market");
this.moveTo(600,460);
// Get the orders, and set off for the warehouse
amountOrdered = this.newOrders();
((DESimulation) simulation).addEvent(
new SimEvent(this,tripTime(),WAREHOUSE_ARRIVE));
break;
Arriving at the Warehouse
case WAREHOUSE_ARRIVE:
// Show the distributor at the warehouse
((DESimulation) simulation).log(this.getName()+"\t Arrived at warehouse");
this.moveTo(600,50);
// Is there enough product available?
Resource warehouseProduct = ((FactorySimulation) simulation).getProduct();
if (warehouseProduct.amountAvailable() >= amountOrdered)
{
// Consume the resource for the orders
warehouseProduct.consume(amountOrdered); // Zero time to load?
((DESimulation) simulation).log(this.getName()+"\t Gathered product for orders of \t"+amountOrdered);
// Schedule myself to arrive at the Market
((DESimulation) simulation).addEvent(
new SimEvent(this,tripTime(),MARKET_ARRIVE));
}
else {// We have to wait until more product arrives!
((DESimulation) simulation).log(this.getName()+"\t is blocking");
waitFor(((FactorySimulation) simulation).getProduct());}
break;
Is there enough product?
/** Are we ready to be unlocked? */
public boolean isReady(Resource res) {
// Is the amount in the factory more than our orders?
return ((FactorySimulation) simulation).getProduct().amountAvailable()
>= amountOrdered;}
If so, we’ll be unblocked
/**
* I've been unblocked!
* @param resource the desired resource
**/
public void unblocked(Resource resource){
super.unblocked(resource);
// Consume the resource for the orders
((DESimulation) simulation).log(this.getName()+"\t unblocked!");
resource.consume(amountOrdered); // Zero time to load?
((DESimulation) simulation).log(this.getName()+"\t Gathered product for orders of \t"+amountOrdered);
// Schedule myself to arrive at the Market
((DESimulation) simulation).addEvent(
new SimEvent(this,tripTime(),MARKET_ARRIVE));
}
The Overall Factory Simulation
/**
* FactorySimulation -- set up the whole simulation,
* including creation of the Trucks and Distributors.
**/
public class FactorySimulation extends DESimulation {
private Resource product;
/**
* Accessor for factory
**/
public FactoryProduct getFactory(){return factory;}
Setting up the
Factory
Simulation
public void setUp(){
// Let the world be setup
super.setUp();
// Give the world a reasonable background
FileChooser.setMediaPath("D:/cs1316/MediaSources/");
world.setPicture(new Picture(
FileChooser.getMediaPath("EconomyBackground.jpg")));
// Create a warehouse resource
product = new Resource(); //Track product
// Create three trucks
Truck myTruck = null;
for (int i=0; i<3; i++){
myTruck = new Truck(world,this);
myTruck.setName("Truck: "+i);}
// Create five Distributors
Distributor sales = null;
for (int i=0; i<5; i++){
sales = new Distributor(world,this);
sales.setName("Distributor: "+i);}
}
The Master Data Structure List:
We use almost everything here!
• Queues: For storing the agents waiting in line.
• EventQueues: For storing the events scheduled to occur.
• LinkedList: For storing all the agents.
Daftar Pustaka
TERIMA KASIH