This site uses cookies to deliver our services and to show you relevant ads and presentations. By clicking on "Accept", you acknowledge that you have read and understand our Cookie Policy , Privacy Policy , and our Terms of Use.
X

Download Discrete Event Simulation Georgia Institute of Technology PowerPoint Presentation


Login   OR  Register
X


Iframe embed code :



Presentation url :

X

Description :

PowerPoint presentation on Discrete Event Simulation Georgia Institute of Technology, download now ppt of Discrete Event Simulation Georgia Institute of Technology

Tags :

Discrete Event Simulation Georgia Institute of Technology

Home / Business & Management / Business & Management Presentations / Discrete Event Simulation Georgia Institute of Technology PowerPoint Presentation

Discrete Event Simulation Georgia Institute of Technology PowerPoint Presentation

Ppt Presentation Embed Code   Zoom Ppt Presentation

About This Presentation


Description : PowerPoint presentation on Discrete Event Simulation Georgia Institute of Technology, download now p... Read More

Tags : Discrete Event Simulation Georgia Institute of Technology

Published on : Jan 08, 2015
Views : 367 | Downloads : 0


Download Now

Share on Social Media

             

PowerPoint is the world's most popular presentation software which can let you create professional Discrete Event Simulation Georgia Institute of Technology powerpoint presentation easily and in no time. This helps you give your presentation on Discrete Event Simulation Georgia Institute of Technology in a conference, a school lecture, a business proposal, in a webinar and business and professional representations.

The uploader spent his/her valuable time to create this Discrete Event Simulation Georgia Institute of Technology powerpoint presentation slides, to share his/her useful content with the world. This ppt presentation uploaded by worldwideweb in this Business & Management category is available for free download,and can be used according to your industries like finance, marketing, education, health and many more.

SlidesFinder.com provides a platform to marketers, presenters and educationists along with being the preferred search engine for professional PowerPoint presentations on the Internet to upload their Discrete Event Simulation Georgia Institute of Technology ppt presentation slides to help them BUILD THEIR CROWD!!

User Presentation
Related Presentation
Free PowerPoint Templates
Slide 1 - Problem Solving with Data Structures using Java: A Multimedia Approach Chapter 17: Discrete Event Simulation
Slide 2 - Chapter Objectives
Slide 3 - 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
Slide 4 - 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?
Slide 5 - 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)
Slide 6 - We use a Discrete Event Simulation We don’t simulate every moment continuously. We simulate discrete events.
Slide 7 - 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)
Slide 8 - 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.
Slide 9 - 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)
Slide 10 - Key Ideas Already presented, but now used: 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
Slide 11 - Key idea: 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? The classic bell-shaped curve
Slide 12 - Generating 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.
Slide 13 - Generating a Histogram Now we have lots of numbers between 0 and 1. We want to count the number between 0.9 and 1.0, 0.8 and 0.9, 0.7 and 0.8, and so on. We need to count them into bins. Great job for a Map!
Slide 14 - HistogramGenerator import java.util.*; import java.io.*; /** * Class to generate a histogram * @author Mark Guzdial * @author Barb Ericson */ public class HistogramGenerator { /** the map to hold the values */ private Map valueMap = new TreeMap(); For a given value (like 0.8), we want a count. That’s Double->Integer map
Slide 15 - /** * Method to read a set of values from the inputFile and create * bins based on the array of keys. This will count the number * of values in each bin. Any value larger than the last key * will be put in the last bin. * @param inputFile the file to read from * @param an array of key values to use */ public void countValuesForKeys(String inputFile, double[] keys) { BufferedReader reader = null; String line = null; double doubleValue = 0.0; boolean found = false; int lastIndex = keys.length - 1; // put the keys in the map using a count of 0 for (int i = 0; i < keys.length; i++) { valueMap.put(keys[i],0); }
Slide 16 - try { // open the file reader = new BufferedReader(new FileReader(inputFile)); // loop reading from the file while ((line = reader.readLine()) != null) { doubleValue = Double.parseDouble(line); found = false; for (double key : keys) { if (doubleValue < key) { valueMap.put(key,valueMap.get(key) + 1); found = true; break; } } if (!found) valueMap.put(keys[lastIndex], valueMap.get(keys[lastIndex]) + 1); } // close the file reader.close(); } catch (Exception ex) { System.out.println(ex.getMessage()); ex.printStackTrace(); } } If the double value we read is less than this key, increment the count at that key
Slide 17 - /** * Method to read a set of values from the inputFile create even * bins based on the passed factor. This will count the number * of values in each bin. * @param inputFile the file to read from * @param factor the factor to use to break the values into bins */ public void countValues(String inputFile, int factor) { BufferedReader reader = null; String line = null; double doubleValue = 0.0; double key = 0.0; int currCount = 0; try { // open the file reader = new BufferedReader(new FileReader(inputFile));
Slide 18 - // loop reading from the file while ((line = reader.readLine()) != null) { doubleValue = Double.parseDouble(line); doubleValue = doubleValue * factor; key = Math.ceil(doubleValue) / (double) factor; if (valueMap.containsKey(key)) { currCount = valueMap.get(key); currCount++; valueMap.put(key,currCount); } else { valueMap.put(key,1); } } // close the file reader.close(); } catch (Exception ex) { System.out.println(ex.getMessage()); ex.printStackTrace(); } }
Slide 19 - Now, write the counts to a file for graphing /** * Method to output the keys and values in the histogram * to a file * @param fileName the name of the file to write to */ public void writeFile(String fileName) { BufferedWriter writer = null; double key = 0; int value = 0; Set keySet = null;
Slide 20 - try { // create the writer writer = new BufferedWriter(new FileWriter(fileName)); // get the keys and loop through them keySet = valueMap.keySet(); Iterator iterator = keySet.iterator(); while (iterator.hasNext()) { key = iterator.next(); value = valueMap.get(key); writer.write(key + "\t" + value); writer.newLine(); } // close the writer writer.close(); } catch (Exception ex) { System.out.println(ex.getMessage()); ex.printStackTrace(); } }
Slide 21 - Using it /** * Generate the histogram from the uniform data */ public static void genUniform() { HistogramGenerator histGen = new HistogramGenerator(); double[] keyArray = {0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0}; histGen.countValuesForKeys("C:/dsBook/uniform.txt",keyArray); histGen.writeFile("C:/dsBook/uniformHist.txt"); } /** * Method to generate the normal histogram */ public static void genNormal() { HistogramGenerator histGen = new HistogramGenerator(); double[] keyArray = {-1.0, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1,0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0}; histGen.countValuesForKeys("C:/dsBook/normal.txt",keyArray); histGen.writeFile("C:/dsBook/normalHist.txt"); }
Slide 22 - How do we view a distribution? A Histogram
Slide 23 - Load the values into a Spreadsheet, Then graph the result A uniform distribution
Slide 24 - A Uniform Distribution
Slide 25 - 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()); } }
Slide 26 - Again, in a Spreadsheet
Slide 27 - Graphing the normal distribution The end aren’t actually high—the tails go further.
Slide 28 - 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.
Slide 29 - A new normal distribution
Slide 30 - Key idea: Ordering Events by 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.
Slide 31 - 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); 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()); } } } We’re stuffing the EventQueue with events whose times are out of order.
Slide 32 - 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
Slide 33 - 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(); }
Slide 34 - 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;}
Slide 35 - 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); }
Slide 36 - 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
Slide 37 - 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
Slide 38 - Insertion Sort public void 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;
Slide 39 - // 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(compare-1); } } // Wherever we stopped, this is where "considered" belongs elements.set(compare,considered); } // for all positions 1 to the end } // end of sort()
Slide 40 - Sorting is expensive It takes a lot of time to re-sort the event queue each time. Better option: Put each new event in the right place as it enters the queue.
Slide 41 - 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); }
Slide 42 - 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);
Slide 43 - // 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);} }
Slide 44 - Option #3: Min-Heap So, we don’t really need to sort or insert in order. The critical thing is that each pop() gives us the smallest time. A min-heap is a kind of binary tree that is complete and where the value at each node is less than or equal to the values stored in the children nodes. A complete binary tree has all nodes filled except the last (bottom/deepest) level, and the bottom is filled left-to-right.
Slide 45 - Example Min-Heap The top value here (the 5) is guaranteed to be the lowest value
Slide 46 - Adding a “3” Add 3 to right But can’t have 3 below 10! And can’t have 5 above 3 Final version!
Slide 47 - Java’s PriorityQueue does this! The Java class PriorityQueue already implements a min-heap. Our third option is just to use the PriorityQueue for the EventQueue. But now you know what the options are and how they work.
Slide 48 - Finally: A Discrete Event Simulation Now, we can assemble queues, different kinds of random, and a sorted EventQueue to create a discrete event simulation.
Slide 49 - Running a DESimulation Welcome to DrJava. > FactorySimulation fs = new FactorySimulation(); > fs.openFrames("D:/temp/"); > fs.run(25.0)
Slide 50 - What we see (not much)
Slide 51 - The detail tells the story Time: 1.7078547183397625 Distributor: 0 Arrived at warehouse Time: 1.7078547183397625 Distributor: 0 is blocking >>> Timestep: 1 Time: 1.727166341118611 Distributor: 3 Arrived at warehouse Time: 1.727166341118611 Distributor: 3 is blocking >>> Timestep: 1 Time: 1.8778754913001443 Distributor: 4 Arrived at warehouse Time: 1.8778754913001443 Distributor: 4 is blocking >>> Timestep: 1 Time: 1.889475045031698 Distributor: 2 Arrived at warehouse Time: 1.889475045031698 Distributor: 2 is blocking >>> Timestep: 1 Time: 3.064560375192933 Distributor: 1 Arrived at warehouse Time: 3.064560375192933 Distributor: 1 is blocking >>> Timestep: 3 Time: 3.444420374970288 Truck: 2 Arrived at warehouse with load 13 Time: 3.444420374970288 Distributor: 0 unblocked! Time: 3.444420374970288 Distributor: 0 Gathered product for orders of 11 >>> Timestep: 3 Time: 3.8869697922832698 Truck: 0 Arrived at warehouse with load 18 Time: 3.8869697922832698 Distributor: 3 unblocked! Time: 3.8869697922832698 Distributor: 3 Gathered product for orders of 12 >>> Timestep: 3 Time: 4.095930381479024 Distributor: 0 Arrived at market >>> Timestep: 4 Time: 4.572840072576855 Truck: 1 Arrived at warehouse with load 20 Time: 4.572840072576855 Distributor: 4 unblocked! Time: 4.572840072576855 Distributor: 4 Gathered product for orders of 19 Notice that time 2 never occurs!
Slide 52 - 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!
Slide 53 - How DESimulation works
Slide 54 - FactorySimulation: Extend a few classes
Slide 55 - 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.
Slide 56 - 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.
Slide 57 - 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
Slide 58 - 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!
Slide 59 - 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;
Slide 60 - 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.
Slide 61 - 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();} }
Slide 62 - newLoad() uses uniform /** A new load is between 10 and 20 on a uniform distribution */ public int newLoad(){ return 10+randNumGen.nextInt(11); }
Slide 63 - 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;
Slide 64 - 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).getFactory().add(load); load = 0; // Head back to factory ((DESimulation) simulation).addEvent( new SimEvent(this,tripTime(),FACTORY_ARRIVE)); break; }
Slide 65 - 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.
Slide 66 - 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); } } }
Slide 67 - 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;
Slide 68 - 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)); }
Slide 69 - 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.
Slide 70 - 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;
Slide 71 - 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;
Slide 72 - 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? FactoryProduct factory = ((FactorySimulation) simulation).getFactory(); if (factory.amountAvailable() >= amountOrdered) { // Consume the resource for the orders factory.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).getFactory());} break;
Slide 73 - 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).getFactory(). amountAvailable() >= amountOrdered;}
Slide 74 - 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!"); ((FactoryProduct) 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)); }
Slide 75 - The Overall Factory Simulation /** * FactorySimulation -- set up the whole simulation, * including creation of the Trucks and Distributors. **/ public class FactorySimulation extends DESimulation { private FactoryProduct factory; /** * Accessor for factory **/ public FactoryProduct getFactory(){return factory;}
Slide 76 - 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 factory resource factory = new FactoryProduct(); //Track factory 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);} }
Slide 77 - A Class that Shouldn’t Exist /** * FactoryProduct -- Represents the products * in the factory, which is the resource that * the Truck produces and the Distributor consumes. **/ public class FactoryProduct extends Resource { }
Slide 78 - 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.
Slide 79 - Thin-line between data and program What happens in a computation is about an interaction between the program and the data. Sometimes the data ‘tells’ the computer what to do, like the operations in the branches of the scene graph. Just seeing the source code doesn’t tell you what’s going to happen. Data is important, and data can specify behavior.