Building a Model In this training section we will introduce to you how to begin a modeling project. To begin, you need to gather all available information that describes the system, from how it looks to how it behaves. To build a model we take an incremental approach to the design and implementation of the model; think layers. We will be building a model in the following 5 steps: Create a 3D layout (this includes a CAD drawing, if available) Create a flowchart of the process steps Define the logic for each step, testing as you go Revise Logical steps as needed Run the model View results Post Office: Phase 1 Purpose Gain familiarity with Abstraction in models, Flowcharting steps, basic ProcessFlow activities Description Customers arrive at a post office every 45 seconds, on average. The statistical probability distribution which best simulates the inter-arrival pattern is an exponential distribution with a location value of 0 and a scale value of 45. The service time at the one and only service window in the post office is lognormal2(45,3.1,0.5) seconds. When the delay for service is complete, Customers leave the post office Follow Up (see video below at time 7:16) If there is more than one Token at the Delay activity, what does that say about how many Customers can be served at once? How can we apply limits to Tokens at activities? Video of Post Office: Phase 1: If you'd like to download the completed Post Office: Phase 1 model, you will find it here.

In this example model you'll see two identical elevator setups. However, you will notice that ElevatorBank1 allows the patient to move to the next floor properly, whereas ElevatorBank1_2 will float the patient up the network node instead of using the elevator. There are a few steps you must follow to ensure you will have a properly working elevator in your model. Make sure everything is working the way you intended, without an elevator. Now you can add in the elevator, select it and then check the 'Connect to Path' box as seen below Now ensure that the elevator is connected to the nearest path node and any nodes above it. One thing to note is that after resetting if you click on any of the Path nodes that the elevator is connected to you will see that the On Arrival Trigger now says Send Message to Request Elevator. This is the code that actually calls the elevator when a patient arrives at the node. The elevator automatically adds this to the nodes connected to when resetting, but this trigger option can be added to any node. Another good practice, especially if patients walk by the elevator without always using it, is to make a separate node off on a spur. That way patients aren't triggering the elevator every time they walk by.

In version 2018 and forward, you can make this chart using a Chart Template. You can simply drag and drop the chart from the dashboard library. This article may help you understand how the chart template works. You can use the Install button on the chart template to view the Process Flow, Statistics Collector, and Calculated Table that make the chart. This article reviews how to use the Zone, along with the Statistics Collector, to create a bar chart of the current work-in-progress (WIP) by item type. The method scales to as many types as you need (this example uses 30 types), and can easily adapted to text data, like SKU. An example model ( zonecontentdemo.fsm) demonstrates this method. Creating the Process Flow To create this chart, we first need to gather the data for this chart. In this case, it is easiest to build off the capabilities of the Zone. In particular, we will use Zone Partitions to categorize all of our object. After we set up the Zone, we'll use a Statistics Collector to gather the data that we need. Create a new General Process Flow. You can have as many General Process Flow objects as you want, so let's one that just deals with gathering statistics. This way, gathering statistics will not interfere with the logic in our model. The process flow should look something like this: Here's how it works. The Listen to Entry is configured to listen to a group of objects. In this case, the group contains all the sources in the model, and it's listening to the OnExit of the sources. However, it could be OnEntry or OnExit of any group of 3D objects. If you want to split the statistics by Type or SKU, then any flowitem that reaches the entry group already has the appropriate labels. In this example model, when a flowitem leaves any source, a token gets created. The token makes a label called Item that stores a reference to the created item, as shown in the following picture. The next step is to link the flowitem with the token that represents it. The Link Token to Item is configured like this: Now, the Item has a label that links back to the token. The token then enters a zone. The Zone is partitioned by type: At last, the token comes to a decide activity. The decide is configured not to release the token. The token will be released by the second part of the flow. Once the token is released, it exits the zone, and goes to a sink. The second part of the flow also has an event-triggered source, that is configured to listen to all the sinks in the model. Again, the entry objects and exit objects are arbitrary; you can gather data for the entire model, or for just a small section of the model, using this method. The event triggered source also caches off the item in a label. At this point, we need to release the token that was created when items entered the system. To do this, the Release Token activity is configured as seen here: The token created on the exit side has a reference to the item, which has a reference back to the token created on the entry side. We release this token to 1, which means connector 1. Note: We could have used a wait for event activity in the zone, and then used the match label option to wait for the correct item to leave the system. However, this method is much, much faster, especially as the number of tokens grows. Creating the Bar Chart Statistics Collector The next step is to create a statistics collector that gathers data appropriate for a bar chart. Note that this method will grow the number of rows dynamically, so that it won't matter how many types (or SKUs) your model has; you will still get one bar per type/SKU. In order to make the number of rows dynamic, we need to listen to the OnEntry and the OnExit of the zone activity: Notice the shared label on this collector. Because this label is shared, both the OnEntry and OnExit events will create this label on the data object. The value of this label is the item's type. Next, we move to the Data Recording tab. Set the Row Mode to Unique Row Values, and set the row value to Partition. This means that whenever an event fires, the statistics collector will look at the partition label on the data object. If the value is new to the statistics collector, the collector will make a new row for this value. If not, then the collector will use the row that is already present. Finally, we need to make our columns. We only need two columns: one for the Partition, and one for the Content of that partition. Both of these columns can use the Integer storage type, and raw display format. However, if your partition value was text-based, like an SKU, you should use the String storage type. The value for Partition is just the data object's Partition label: Notice that the Update option is set to When Row is Added. This way, the statistics collector knows that this value will not change, and that it's available at the time the row is created. The other column is a little harder, because we need to use the getstat command: The getstat command arguments depend on the stat you are trying to get. In this case, we are asking the zone (current, the event node) for the Partition Content statistic. We want the current value. Since this is a process flow activity, we pass in the instance as the next argument. Finally, we pass in which partition we want to get the data from, the row value. In this case, we could have identically passed in data.Partition. Also, notice that this column is updated by event dependency. To make sure this does what we want, we need to edit the event/column dependency table. We want the Content column to be updated when items enter and exit, so it should look like this: Now, open the table for the statistics collector. You should see two columns. When you run the model, rows will be added as items of different types are encountered. The table will look something like this: This screenshot came from early in the model, before all 30 types of item has been encountered, so it doesn't have 30 rows yet. Making the Bar Chart This is the easiest part. Create a new dashboard, and add a new bar chart. Point the chart at the statistics collector. For the Bar Title option, choose the Partition column. Be sure to include the Content column. Also, make sure that the "Show Percentages" checkbox on the Settings tab is cleared. The settings should look like this: The resulting chart looks something like the following image. You can set the color on the Colors tab. Ordering the Data Because the rows of this table are created dynamically, the order of the rows will likely change run to run. To force an ordering, you can use a calculated table. Since the number of rows on this table don't grow indefinitely, and the number is relatively small, it's okay to set the Update Mode on the calculated Table to always. Here's what the properties of that calculated table look like: We simply select all columns from the target collector (CurrentContent, in this case) and order it by the Partition column. That yields an ordered bar chart: Example and Additional Charts The attached example model demonstrates this method, as well as how to create a WIP By Type vs Time chart: Happy data collecting! zonecontentdemo.fsm

This article reviews one method for making a state Gantt chart for the default and alternate state profiles: Example Model You can download the model for this walkthrough ( stateganttdemo.fsm). The model has two multiprocessors, in a Group called Multiprocessors. Each multiprocessor has two processes: Process1 and Process2. To make the chart, we will first make a Statistics Collector, and then a Calculated Table. Making the Statistics Collector Make a new Statistics Collector. On the Event Listening tab, use the Sampler to listen to On State Change of the group of multiprocessors. You can leave the parameter names alone. However, we need to add a label, so we can record the profile number. Select the new event, and then use the green plus button in the Event Labels area to add a label for this event. Set its name to ProfileNum, and its value to the following code: data.StateProfileNode?.rank The event settings should look something like the following: Next we need to set the row mode. Make sure it's set to Add Per Event, with no row value. As the final configuration step for the statistics collector, we need to set up the columns. There should be four columns in this collector: Time - In the pick options, select Time, then Model Date/Time Object - In the pick options, select IDs, then ID of Event Object Profile - Type data.ProfileNum for the value. The default storage and display format are fine. State Type the following code: data.eventNode.as(Object).stats.state(data.ProfileNum).profile[data.ToState + 1][1] Set the Storage Type to String The code is necessary because On State Change occurs before the state is set to the new state. So the code is looking up the name of the future state in the profile table. When you reset and run this model, you will see a table like the following: Making the Calculated Table Make a new Calculated Table, and give it the following query: SELECT Object, Time as StartTime, LEAD(Time) OVER (PARTITION BY Object) AS EndTime, State FROM StatisticsCollector1 WHERE Profile = 1 This query creates an Object column as well as a Time column. To get the time that the current state ends, we look to when the next state begins. The LEAD() function looks ahead in the table, and the OVER(PARTITION BY Object) clause makes sure that LEAD() makes sure to look to the next row with the same Object. We also record the state column, and filter out the standard state profile, keeping the special multiprocessor state profile. Once you get this query to work, change the Update Mode to By Interval, and set the interval to 20 or 30. Since the Statistics Collector table will get longer and longer, the query will become more and more expensive as the model runs. To control how much time is spent running the query, we use an interval. The final configuration of the Calculated Table should look like this: You will need to set the Display Format of each column on the Display Format tab (Object, Date/Time, Date/Time, and Raw). Making the Chart Make a new dashboard, and create a Gantt chart. Point it at the Calculated Table. When you do that, the chart should fill in all the other columns correctly. Charting Both State Profiles for Both Objects In order to chart both profiles on the same chart, we first need to add a column to the Statistics Collector, and then update the query in the Calculated Table. The new column should be named ObjectAndProfile, and a Storage Type of String. Use the following code for a value: data.eventNode.name + " - " + string.fromNum(data.ProfileNum.as(int)) Then change your query to the following: SELECT ObjectAndProfile, Time as StartTime, LEAD(Time) OVER (PARTITION BY ObjectAndProfile) AS EndTime, State FROM StatisticsCollector1 With these changes, you should be able to view both profiles for both multiprocessors.

This article demonstrates how to use the Statistics Collector and Calculated Tables to create three utilization pie charts: a state pie chart an individual utilization pie chart a group utilization pie chart Example Model You can download that model (utilizationdemo.fsm) to see the working demonstration. The model has a Source, a Processor, a Sink, a Dispatcher, and several operators. The operators carry flow items to and from the processor, as well as operate the processor. The operators are in a group called Operators. State Pie Chart First, we need to make a Statistics Collector that collects state data for the operators. The easiest way to do that is to use the pin button to pin the State statistic for any object in the model. Use the pin button to pin a pie chart to a new dashboard. The pin button creates a new Statistics Collector, as well as a new chart. Open the properties for new Statistics Collector (double click on it in the toolbox), and change its name to OperatorStatePie. On the Data Recording tab, remove the object from the Enumerated Rows table. Using the sampler, add the Operators group (you can sample it in the toolbox). Now, when you reset and run the model, the state chart should work. Utilization Pie Chart Often, users need to combine sever states into a single value that can be used to determine the utilization of an object in the model. In order to gather this data, we can use a calculated table. Make a new Calculated Table, and give it the following query: SELECT Object, (TravelEmpty + TravelLoaded + Utilize) / Model.statisticalTime AS Busy, 1 - Busy AS NotBusy FROM OperatorStatePie This query sums the time in several states into a total, and then divides by the statistical time. Be sure to set the name of the table (the part after FROM) to the name of your Statistics Collector. Run the model for a little bit of time, and then click the Update button on the properties window. You should get a table like the following: Instead of viewing the data as just numbers, change the Display Format of each column to better represent the data. On the Display Format tab, set the Object column to display Object data. Then set the other two columns to display percentages. When you switch back to the Calculations tab, the data will be formatted: Once we get the query right, set the update mode to Always. This will updated the data in the table whenever the data is needed, including every time the chart draws. If updating the table is computationally expensive, you can use the By Interval or Manual options. Generally, a small number of rows (1-100) is small enough to use the Always mode. Regardless of the update mode, we can make a chart based on this table. In the dashboard, create a new Pie Chart. For the Data Source, select the calculated table. For the Pie Title, select the Object column. For the Center Data, select the Busy column. Be sure to include the Busy and NotBusy columns. This should show you a pie chart, comparing the operator's busy and not busy time. Group Utilization To make the final utilization chart, make a second calculated table. The query for the second table should be as follows: SELECT AVG(Busy) AS AvgBusy, AVG(NotBusy) AS AvgNotBusy FROM CalculatedTable1 Again, use the Update button to be sure the query is correct. Once it is, set the update mode to Always. Finally, you can make the pie chart for this data: Things to Try If you feel comfortable with this model, you can try a couple extra tasks, such as: Remove one of the operators from the group, reset, and run. The charts will update accordingly. Add the Processor to the group, reset, and run. The state chart should work automatically.

In version 2018 and on, you can make this chart by dragging the Throughput Per Hour by Type template from the dashboard library. If you install the template (available on the Advanced tab), you will see a Process Flow and a Statistics Collector appear in your toolbox. One of the most common questions from FlexSim users is as follows: How do I make a chart that shows the output every hour? You can make this chart in three steps. Configure the Statistics Collector First, you need a Statistics Collector. Make a new one in the toolbox (click the green plus button, select Statistics, and then select Statistics Collector). On the event listening tab, use the green plus button to add a timer event, and configure as shown here: This timer event will fire every hour (every 3600 seconds) in the model. Notice the shared label, that is storing all members of the Processors group as an array. We will use this label in the next step. Once you have configured the timer, then you need to set up the row mode for this collector. We want one row per processor, and we need to use the Processors label as the row value. Since the Processors label is an array, we will get three rows per timer event, each row corresponding to a processor. Finally, we can add the columns. The three columns are as follows: Time - use the pick list to select Model Date/Time from the Time menu Object - use the pick list to select ID of row value from the IDs menu Output - use the pick list to select Statistic by Object from the Object Statistics menu Use data.rowValue as the object value in the popup If you use the pick options to choose these options, then the storage type and display format options should be set automatically. With these three columns in place, we can watch the table populate. Reset and run the model at high speed. Every model hour, you should see a new set of rows appear, one for each processor in the group. The table will look something like this: Configure the Calculated Table The Statistics Collector table from the previous steps is close to what we want, except that the output value always increases as the model runs. But what about the output for just a single hour? To get that value, we can use a Calculated Table. Make a new calculated table, and give it the following query (in the Query field): SELECT Time, Object, ISNULL(Output - LAG(Output) OVER (PARTITION BY Object), 0) AS OutputPerHour FROM StatisticsCollector1 This query uses SQL window functions. Basically, it says that each row's value should subtract the previous row's value for the object. In addition, if that value is NULL (because it's the first row), then just use a value. If you reset and run the model, so that the collector table has at least a few rows in it, click the Update button to run the query. Notice that the Time and Object columns show numbers. This is because the Calculated Table can't infer the formatting of the column. To set the formatting, use the Display Format Tab. You may also wish the table to update every hour, with the Statistics Collector. Make the Chart Now that our data is correct, we can make a chart. Make a new dashboard, and create a Time Plot chart. Point the chart to the calculated table. Let's use the Time column for the X values, and let's use the OutputPerHour column for the Y values. In addition, make sure to split by the Object column. If the calculated table updates every hour, then running the model should create the chart shown at the beginning of the model. Here is the model used to create this chart (should work in 2017 Update 2 Beta or later; beta must be built on or after August 21, 2017). outputperhourdemo.fsm

In the last little while we've had several questions regarding Picking Line operations. (How to make one, how to simplify, how to set up one using Process Flow & Lists, etc.) So I've put together a very simple little model utilizing Process Flow and a basic List structure, that shows a basic picking line. It creates orders, and sends them by tote via a conveyor, and Operators will pick items from their respective stations to fulfill the orders into the tote, before sending it to Checking and Shipping. Note that this could easily be adapted into an assembly line situation as well. pickingstations.fsm

Quando desenvolvemos um modelo de simulação que possua diversas ou até mesmo elevado número de variáveis de entrada, faz-se necessário analisar diversos cenários dentro de um simulador para termos o maior número de detalhes possíveis com relação ao comportamento do sistema. No FlexSim, o recurso que permite esse tipo de análise é o Experimenter. Com ele, podemos estimar como as variáveis de entrada, afetam as respostas de um experimento e pode-se planejar de forma racional os cenários a serem executados. O vídeo explicando um pouco sobre experimentos e também falando sobre o Experimenter, encontra-se em nosso Canal no Youtube.

A narrated video demonstration of the FlexSim Healthcare Tutorial described in the FlexSim 2020 User Manual has been released! Here is a link to the written documentation: https://docs.flexsim.com/en/20.0/Tutorials/FlexSimHC/OverviewFlexSimHC/ Here's a link to the video: https://vimeo.com/394012280

Modeling LWBS patients is tricky business and can cause exception errors under certain circumstances if not done correctly. The problems usually arise due to patients who exit the model early while there are pending requests queued up in the model for future activities on the patient. The attached model demonstrates the current best practice for safely modeling LWBS patients without the possibility of generating unwanted errors. The modeling technique is very simply. Use the On Entry trigger of the waiting room object(s) to send a delayed message to itself in X minutes, where X is a sample time from an "impatience curve" representing the amount of time a typical patient is willing to wait before they strongly consider leaving. In the On Message trigger of the waiting room object(s), I have written a code snippet that you will want to copy and modify to suit your own modeling requirement. The code snippet in the example model checks to make sure the patient is still in the waiting room waiting for an exam room at the end of the "impatience time" when the message trigger fires. Then I roll the dice using a bernoulli distribution to decide whether or not to have the patient actually leave. The code snippet shows a different probability for each of two patient types (PCI's) and then a default probability of 50 percent for anyone else. Not only does this modeling approach avoid undesirable exception errors, but it is also more accurate and definitely more efficient than using the Quick Properties fields in the Patient Condition panel of a waiting room which requires repetitive function calls every so many minutes throughout the model run!

Rápida demonstração de como usar o Graphical User Interface para controlar todo o modelo de simulação, desenvolver um painel de controle ou cockpit para alterar propriedades específicas de objetos dentro do modelo de simulação ou ainda, destruir ou recriar variáveis de objetos de forma a customizar ainda mais seu modelo. Tudo isso usando a estrutura do 'Tree' no FlexSim e também o recurso GUI, disponível no ícone em 'Tools'. Veja em nosso canal da FlexSim Brasil no Youtube.

What is ODBC? FlexSim can use ODBC, and ODBC can connect to many different kinds of data sources, including files (like Excel) and databases. It allows you to use SQL queries to get data from any supported data source. ODBC uses a driver determine how to get data from a given data source. A driver is translator between ODBC and whatever data source you are querying. For example, there is a driver for Excel files. If you have that driver on your system, you could use ODBC to query data from Excel files. As another example, there is a driver for SAP HANA, the database for SAP. If you have that driver, then ODBC will know how to talk to SAP. This means that if the correct driver is installed, and is working correctly, that you can use FlexSim to query any ODBC data source. Using the Database Connector FlexSim has a tool called the Database Connector. You can use it to configure a connection to a database. This article uses that tool. For more information, see https://docs.flexsim.com/en/20.2/Reference/Tools/DatabaseConnectors/ As an example, let's say that you want to connect to Excel using ODBC. Assuming you have the Excel ODBC driver installed on your system, you can configure a Database Connector to look like this: Note that you must specify the full connection string. In the connection string, you can see that the driver and the file are specified. Then you can query the data in a given sheet with a query like this: SELECT * FROM [Sheet1$] You can find more information on querying Excel files at websites like this: https://querysurge.zendesk.com/hc/en-us/articles/205766136-Writing-SQL-Queries-against-Excel-files-Excel-SQL- If you use Office 365, you may need to install the Microsoft Access Database Engine 2016 Redistributable. This includes newer drivers for Excel and Access Be sure to install it with the /quiet flag on the command line. Instructions can be found in this troubleshooting guide: https://docs.microsoft.com/en-us/office/troubleshoot/access/cannot-use-odbc-or-oledb Note that FlexSim has an Excel tool, which is usually easier to use. This tool requires Excel to be installed, but does not require the ODBC driver for Excel to be installed on your computer. For more information, see https://docs.flexsim.com/en/20.2/Reference/Tools/ExcelInterface/. Excel makes a good example because most people have it, and it's easy to get the driver for it. Connection Strings Different kinds of connections require different connection strings. The following list has an example connection string for a few data sources: Excel Driver={Microsoft Excel Driver (*.xls, *.xlsx, *.xlsm, *.xlsb)};DBQ=Path\To\Excel\File.xlsx Access Driver={Microsoft Access Driver (*.mdb, *.accdb)};DBQ=Path\To\Access.accdb SQLite Driver={SQLite3 ODBC Driver};Database=Path\to\sqlite.db SAP HANA DRIVER={HDBODBC};UID=myUser;PWD=myPassword;SERVERNODE=myServer:30015 Checking for Drivers Note that each connection string specifies a driver, and then additional information. The additional information depends on the driver you are using. In order to determine which drivers are on your system, you need to open the ODBC Data Sources Administrator window. To do that, hit the windows key, and then type ODBC. Then choose the option called ODBC Data Sources (64 bit). If you are running 32-bit FlexSim, open the 32 bit version. Go to the Drivers tab. Here is what my Drivers tab looks like: You can see I have drivers for Access, Excel, SQL Server, and SQLite3. I don't have drivers for SAP HANA. If I did, you'd see a driver named HDBODBC in the list. To access that kind of database, I'd need to install that driver. You can also see that the name of the driver used the the connection string must match exactly to what is shown here. Other Info You may see an exception appear when you test the connection to your database. If the view shows that the connection succeeded, then it has succeeded. The exception happens because FlexSim tries to get a list of tables from the database that it's querying. FlexSim may not guess correctly for your particular data source. That exception can be safely ignored. If you used the old db() commands in the past, consider upgrading to using the Database Connector. It will be orders of magnitude faster to read in an entire table.

One of the most powerful uses of a Fixed Resource Process Flow is to unify a set of machines and operators as a reusable entity. For example, many factories have several production lines. Simulation modelers often wonder what would happen if they could either open or close more production lines. If you were to open a new line, would the increased output be worth the cost? Or if you were to close a line, would the factory still be able to meet demand? These are the kinds of questions modelers often ask, and by using a Fixed Resource Process Flow, you can answer this question. This article demonstrates how to use a Fixed Resource Process Flow (or FR Flow) to coordinate several machines and operators as a single entity. The example model represents a staging area, where product is staged before being loaded on to a truck. However, most of the concepts that are discussed could be used in any Fixed Resource Process Flow. Creating a Collection of Objects When you set out to build a reusable FR Flow, it is usually best to start by making a single instance of the entity you want to replicate. Often, it is convenient to build that entity on a Plane. When you drag an object in to a Plane, it is owned by the Plane, which makes the Plane a natural collection. If you copy and then paste the Plane, the copy will have all the same objects as the original Plane. This makes it very easy to make another instance of your entity: just copy and paste the first. If the Plane is attached to an FR Flow, then the copy will also be attached to the same Flow, and will then behave the exact same way. In the example model, you will find a Plane with a processor, five queues, and two operators. This makes up a single staging area. The Plane itself is attached to the FR Flow, meaning an instance of the FR Flow will run for the Plane. It also means the Plane can be referenced by the value current . (You may find it helpful to set the reset position of any operators, especially if they wander off the plane at any point during the model run.) Using Process Flow Variables In order to drive the logic in your entity using an FR Flow, you will need to be able to reference the objects in each entity, so that they will be easily accessible by tokens. For example, if items are processed on Machine A, Machine B, and Machine C, in a production line, then you would want an easy way to reference those machines in each line. This is where Process Flow Variables come in. In the example model, you will find the following Process Flow Variables: Every staging area has a Packer, a Shipper, and a Palletizer, each referenced using the node command. Remember that current is the instance object, which is the Plane. Once these variables are in place, you could create an FR Flow like the following: If you ran this model with the Plane shown previously (complete with correctly named objects) and this flow with the shown variables, then the Packer operator would travel to the Palletizer. If you then copied the Plane, you would see that all Packers go to their respective Palletizers, as shown below: Using Variables and Resources Together Sometimes, you may want to adjust the number of operators per line, or even the number of operators in a specific role. To do this, you can again you Process Flow Variables. The sample model includes these variables: The sample model also includes these resources; the properties for the Shipper Resource are shown: Because this resource is Local, it is as if each attached object has its own version of this resource. Because it references a 3D operator, it will make copies of that operator when the model resets. The number of copies it creates will depend on the local value of the ShipperCount variable. To edit the value for a particular instance object, click on that object in the 3D view, and edit the value in the quick properties window. Disabling Entities Often, modelers simply want to "turn off" parts of their model. If that part of the model is controlled by a fixed resource flow, then you can easily accomplish this task. In the example model, you will find the following set of activities: The Areas resource is numeric, and it is global. The Limit Areas activity is configured so that any token that can't acquire the resource immediately goes to the Area Disabled sink. If the token that controls the process dies, then the area for that token is effectively disabled. The example model uses a Process Flow Variable to control how many areas can be active at a time: The Areas resource uses this value to control how many shipping areas are actually active. If this number is smaller than the number of attached objects, then some of the areas won't run. Note that the order the objects are attached in matters. The first attached area will be the first to generate a token in the Init Area source, and so will be the first one to acquire the area. Using Process Flow Variables in the Experimenter/Optimizer Currently, only Global and User Accessible variables can be accessed in the Experimenter. In the sample model, the only variable that can readily be used in an experiment is the TotalAreas variable, which dictates how many areas can be active. This allows the Experimenter or Optimizer to disable lines. To make it possible for the Experimenter or Optimizer to vary the number of Shippers or Packers per Area, the ShipperCount and PackerCount could be changed to Global Variables. You could put labels on the instance object (the Plane) that are read by the variable, like so: Then the experimenter could set the label value on the Plane object that is attached to the FR Flow. The Experimenter would set the label, which would affect the value of the Variable, which would affect how many copies of the Packer were created by the Packer resource. You could do the same for the ShipperCount variable. Sample Model The attached model (usingfixedresources-6.fsm) provides a working model that can demonstrate the principles in this article. It comes with only one Plane object attached to the FR Flow. Here are some things to try with the model: Reset and run the model to see how it behaves with a single area, where that single area has only one Packer and one Shipper. Create a copy (use copy/paste) of the Plane. Reset and run the model again to see how a second area is automatically driven by the same flow. You may need to adjust the TotalAreas variable if the second doesn't run. Adjust the number of Packers and Shippers (using the Process Flow Variables) on each Plane. Reset and run the model to see how adding more packers and shippers affects it. Create many copies of the first Plane (it is best to set the PackerCount and ShipperCount to 1 before copying). Reset and run the model, to observe the effect. Add or remove some staging areas (queues) to an Area. This model handles those changes as well (in the Put Stations On List part of the flow). Try to set up an experiment or optimization, that varies how many lines are active, and how many Packers and Shippers are in each. In general, play around with the sample model, or try this technique on your own. Other Applications This technique can be used to create production/packaging lines, complex machines, or any conglomerate of Fixed Resources and Task Executers. If you follow the methods outlined in this article, you will be able to create additional instances of complicated custom objects with ease. You will also be able to configure your model for use with the experimenter or optimizer.

Nesse vídeo, fazemos um overview sobre o FlexSim e demonstramos como modelar diretamente pelo 3D usando as conexões padrão disponíveis no software e as diferentes formas de inserir os objetos na área de modelagem (Grid). Também demonstramos como analisar de forma bem básica as estatísticas de cada um dos objetos e uma rápida introdução sobre o Dashboard e a variável estatística presente em cada um dos objetos individualmente. Para assistir o vídeo, acesse nosso canal do youtube.

FlexSim 2022 introduced a Reinforcement Learning tool that enables you to configure your model to be used as an environment for reinforcement learning algorithms. That tool makes connecting to FlexSim from a reinforcement learning algorithm easier, but that tool is not absolutely necessary for this type of connectivity. The same socket communication protocols that are used by that tool are available generally in FlexScript. Attached (ChangeoverTimesRL_V22.0.fsm) is the FlexSim 2022 model that you build as part of the Using Reinforcement Learning documentation that walks you through the process of building and preparing a FlexSim model for reinforcement learning, training an agent within that model environment, evaluating the performance of the trained reinforcement learning model, and using that trained model in a real production environment. Also attached (ChangeoverTimesRL_V6.0.fsm) is a model built with FlexSim 6.0.2 from 2012 that does the exact same thing, but with custom FlexScript user commands instead of the Reinforcement Learning tool. You can use this model with the example python scripts and FlexSim 6.0.2 in the same way that you can use the other model with those same scripts in FlexSim 2022. I'm providing this FlexSim 6 model as an example that demonstrates how you can communicate between FlexSim and other programs. The Reinforcement Learning tool certainly makes this type of communication easier and simpler, with a nice UI for specifying RL-specific parameters, but the fundamental principles of how this works have been available in FlexSim for many years using FlexScript. Hopefully this example can help teach and inspire those who wish to control or communicate with FlexSim from external sources for purposes other than just reinforcement learning. FlexSim is flexible, and the possibilities are endless.

Many simulation models need to show operator or machine utilization by time period of day, or per shift: shiftutilization.fsm Under Construction Summary Until this article is completed, here is the basic idea. I use a process flow to create one token per operator in the group. Each operator token creates one token per shift. The shift token creates its own Categorical Tracked Variable (which is how state information is stored on all objects). The shift token just remains alive, holding the tracked variable. The oeprator token goes into a loop, listening to OnChange of the 3D Operator's state profile node. Whenever the state changes, the token sets the shift token's state label to the same state. This essentially copies the state changes onto another tracked variable. Another child token listens for when the shift changes. When the shift changes, the operator token puts the current shift profile into some unused state (STATE_SCHEDULED_DOWN, in this case), and then applies new state changes to the correct shift token's label. A Statistics Collector listens for when the Operator token starts the main loop at the beginning of each shift. When this happens, the collector saves a row value that is an array, composed of the operator and the shift token. The row mode is set to unique, so that each unique combo of operator/profile gets its own row. Each column in the collector is made to show the data from the shift corresponding to that row. The result is the table shown in the article, which can be plotted with a pie chart as shown above.

In this article, you will learn how to export data from individual replications to an external database. This is an advanced tutorial, and assumes some exposure to databases and SQL. It also assumes that you are familiar with the Experimenter and/or Optimizer feature in FlexSim. This tutorial uses PostgreSQL, but should be compatible with the most popular SQL database engines. Model Description Let's assume you have a model with at least one Statistics Collector, as well as an experiment ready to go. The model used in this example is fairly simple: As the model runs, a Statistics Collector fills in a table. The table makes a new row for every item that goes into the Sink, recording the time, and the Type label of the item: For the experiment, we don't have any variables, but we do have one Performance Measure: Input of the Sink. Of course, an actual model would have many Variables, Scenarios, and Performance Measures, but this model leaves out those details. For reasons which come up later, this model also has two global variables, called g_scenario and g_replication. We will use these during the experiment phase. Connecting to the Database To connect to a database, you can use the Database Connector tool. Each Database Connector handles the connection to a single database. If you need to connect to more than one database, you will need more than one Database Connector. For this model, I have added a Database Connector, and configured the connection tab as follows: This configuration allows me to connect to a PostgreSQL database called "flexsim_test" running on my computer at port 9001. You can test the connection by clicking the "Test Connection" button. The test attempts to connect, as well as query the list of tables found in the database. Setting Up the Export Next, take a look at the Export tab: This tab specifies that we are exporting data from StatisticsCollector1 to the table called experiment_results in the database (this table should exist before you set up the export). The Append to Table box means that when the export occurs, the data will be added to the table. Otherwise, the data would be cleared from the target table. The other interesting thing here is that we are exporting more columns than the Statistics Collector has, namely the Scenario and Replication number. However, the expression in the From FlexSim Column column must be valid FlexQL (FlexSim's internal SQL language). Wrapping the values in Math.floor() leaves the values unchanged, and works well with FlexQL. Setting up the Experimenter Finally, let's take a look at just a little bit of code that makes the Experimenter dump data to a database. There is code in Start of Experiment, Start of Replication, and End of Replication: In the Start of Experiment, we need to clear the table. The code in this trigger connects to the database, runs the necessary query, and the closes the connection to the database. In the Start of Replication trigger, the code simply copies the replication and scenario values into the global variables we created for this purpose. In the End of Replication trigger, the code uses function_s to call "exportAll" on the database connector that we created. This uses the settings on the Export tab to dump the data from StatisticsCollector1 to the database table, including the replication and scenario columns. These same triggers run during an optimization, so the same logic will apply. Run the Experiment Finally, you can run the experiment. When each replication ends, the Statistics Collector data will be exported to the database. Databases can handle many connections simultaneously. This is important, because the child processes that run replications will all open individual connections to the database at the end of each replication. This leads to many concurrent connections, which most databases are designed to handle. But how can we tell that it worked? If you connect to the database with another tool, you can see the result table. Note that there are 1700 rows, and that data from Replication 5 is included. Additionally, you could use the Import tab to import all the data, or summary of the data data, into FlexSim. Conclusion Storing data in a database during an experiment is not difficult to do. There are many excellent tools for analyzing and visualizing database tables, which you could then use to further explore and understand your system. postgresqlexperimentdemo.fsm

Tokens and Flow Items can be very difficult to add to a chart. This is true because they don't exist on Reset, making them difficult to select. This article shows how you can use a Process Flow to allow a Statistics Collector to record a token's changing label value, and also to chart that value over time: The model for this example is attached (graphlabeldata.fsm). It is a very simple model: The Scheduled Source creates three tokens, each of which create a label called data. This label is created by choosing "Add Tracked Variable" for the value, which opens this dialog box: The reason we want the label to be a Tracked Variable is that Tracked Variables emit an OnChange event. We want to listen to that event. If you use the time interval collection method, discussed later in this article, you don't need to make the label a Tracked Variable. Each token then goes through a loop, where it waits, and then updates the value. This is meant to represent a much more complicated model, where the token travels through many activities, any of which could change its label value. For this example, the model randomly changes the value on the label. Now that we have a token and a label whose value is changing as the model runs, we can work on making a chart. We want to eventually make a Statistics Collector, but Statistics Collectors can only listen to events of objects that exist after the model has been reset. Tokens and FlowItems (along with their labels) are destroyed on reset, and so we can't listen directly to them. However, some Process Flow activities can listen to events on tokens and flowitems, and the Statistics Collector can listen to those events. For that reason, we make a second Process Flow: This flow has an Event-Triggered Source, which listens for tokens to leave the "Init Tracked Variable" activity in the first flow. When that happens, the source creates a token, and that token immediately gets a reference to the label node (note that this is different than the value of the label node). Next, the new token goes to a Start activity. The Start activity called "Log Change." This activity is just a placeholder. While you could technically live without this activity, it makes things a little clearer, as we will discuss later. Other than providing OnEntry and OnExit events, the Start activity has no internal logic whatsoever. After passing through the Log Change activity, the new token waits for the label value to change: In order to listen to this event, you can first sample a Tracked Variable in the Toolbox. This provides the OnChange event. Then you can update the Object field to the code shown above. Notice that every time this event happens, the token simply passes through the Log Change activity, and then resumes listening. When the original label value changes, it emits an OnChange event. When that event fires, the token listening to that even travels through the Log Change activity, which emits OnEntry and OnExit events. We can use these events in the Statistics Collector. The key to this technique is that we used Process Flow, which is good at listening to token and flowitem events, to generate Activity events, which can be used in the Statistics Collector. In the attached model, the first Statistics Collector is configured like this: It simply listens to the On Entry of the Log Change activity. The columns are defined as follows: The first two columns are simpler; the Time column uses the Model Date/Time option: The second column gets the ID of the token as an integer: The third column gets the current value of the Data label: Now that the Statistics Collector is set up, we can configure the chart to use this collector, and split by the Token ID. The process to record the label value every interval (rather than on every change) is very similar. The downside is that the data is less granular, but the upside is that a label doesn't have to be a Tracked Variable to be charted. The example model simply uses a Split activity to copy the data from the Event Triggered Source, and sends it to a similar listening loop: Instead of waiting for the value to change, the second token waits for a fixed time interval. A similar Statistics Collector will allow you to create the following chart: This approach works for every token created by the scheduled source. No matter how many tokens you create, each will show up on the chart:

Como criar animações customizadas em máquinas ou equipamentos no FlexSim é o que demonstramos nesse rápido vídeo. Customizamos uma animação no objeto processor, criando a movimentação real de uma maquina envolvedora de filme stretch. Veja o tutorial completo de como executar essa tarefa passo a passo, acessando o Canal Youtube da FlexSim Brasil. Ainda neste artigo, estou anexando uma pasta chamada 'Wrapper' com os arquivos em 3D para os interessados que quiserem criar as customizações acessarem e usarem os arquivos. wrapper.raranimationcreator.fsm

Using Mixamo Fuse, Mixamo, and (optionally) 3ds Max, you can create animated characters for use with FlexSim. For modifying the existing operators' animations, see Adding More People Animations From Mixamo Character Creation You can customize a character’s look using Mixamo Fuse: After creating the character, you can save your character as a .fuse file in case you want to edit it again later in Mixamo Fuse. Character Rigging From Mixamo Fuse, you can press the Animate button in the upper-right corner to upload the character to Mixamo’s website to apply animations. You will need to log in with a Mixamo account. You will be presented with the Mixamo Auto-Rigger. After a few moments, your character will appear in a view with a default animation applied. Choose whether to enable facial blendshapes and select the appropriate level of detail for the skeleton, such as 2 Chain Fingers, and then press Update Rig. The auto-rigger will reapply and the view will be updated with the newly rigged character. If the settings are good for your needs, press the Finish button. Character Animating Once the character is rigged, you can select it on the Mixamo website and press Find Animations to find animations to apply to the character. You can search for animations and add them to packs. For each animation, you can adjust various parameters to fit your character to that animation better. Exporting the Character and Animations Once you have applied and customized animations for your character, you can add them to the Cart and checkout in order to have them available for download. After checking out, you can select an animation or animation pack to download on the My Animations page. You can apply the same animation sets to different characters using the Change Character button after selecting the animation set. Press the Queue Download button to tell the Mixamo server to create the files for download. You want to use the FBX format so that we can edit it with 3ds Max. The pose can probably be T-Pose or Original because the Original is probably a T-Pose anyway. I use 30 Frames per Second for consistency and ease of timing animations. Keyframe Reduction didn’t seem to do anything in my tests, and you can do it yourself with more control in 3ds Max so I use “none.” If you exported multiple animations, the output will contain one fbx file that is the character shape and several other fbx files that contain only animation data for each animation. (Update) Important Note: Starting with FlexSim 2017 Update 2, the rest of the steps in this document are no longer necessary. FlexSim 17.2 added support for embedded textures, specular maps, and gloss maps that fix the material issues directly without modification in 3ds Max. FlexSim 17.2 also added support for assigning animations to a shape from multiple files, so you can import the mixamo output files directly without having to merge all the animations into a single file. The following steps can be used if you want to optimize or otherwise modify the shape files, but are no longer required. Preparing the Character using 3ds Max Import the character shape .fbx file into 3ds Max and save the file as a .max file. You will want to resave as a .max file at different points with different file names as you progress so that you can easily revert back to a previously known working point if something messes up. The FBX Import window will show you lots of options you can modify. The defaults seemed to work fine. I tried messing with the “Units” scale factor and file unit conversions, but I was unable to find any options that improved the scaling in FlexSim. Ultimately, I found it best to just use Automatic and sort out the units myself using FlexSim’s shape factors. Export the file as fbx and bring it into FlexSim to see how it looks. Again, the default options seem to work fine. In FlexSim, the size will probably be 100x too big (no matter what “unit conversion” options I seemed to set in the import/export options). Resize the character to be 100x smaller. Fixing the Materials in 3ds Max When you first bring in a Mixamo Fuse character into FlexSim, it will probably look shiny and dark. That can be fixed in 3ds Max. Open the Material Editor using the button Double-click each material in the list of Scene Materials on the left to add it to the middle view for editing. Press the “Lay Out All – Vertical” button to arrange the nodes nicely in the middle view. When you imported the .fbx file, 3ds Max should have automatically unpacked its textures into a directory called CharacterFileName.fbm. When you double-click on a texture node in the Material Editor view, you should be able to see and edit the path where it is referencing each texture in the Material Parameter Editor pane on the right. Removing the shininess Each material has Ambient, Diffuse and Specular colors, in addition to any textures that are mapped to various channels. The operator is shiny everywhere because each material’s Specular color is set to White and their specular maps are mostly black. Since FlexSim doesn’t currently read specular maps and gloss maps, the white specular color is being applied everywhere instead of the black color from the specular maps. To fix it, click each specular map and gloss map in the center view and delete them with the Delete key. Then double-click on each main material and set its specular color to black to turn off the shininess. Save the .max file with a new name, and re-export the .fbx file to test it in FlexSim. The shininess should be gone. Brightening the darkness FlexSim uses Assimp to read fbx files. Assimp’s fbx importer is setting a default Ambient color value of dark gray to the materials instead of leaving it off. This is why the character looks darker than he should. To fix it, we can simply specify an ambient color of white on each material. For each material, set the ambient color to white and check the Ambient Color box under Maps: Save the .max file with a new name, and re-export the .fbx file to test it in FlexSim. The darkness should be fixed. Setting local texture paths Based on the procedure above, the texture paths are likely to be absolute paths when you export the fbx file. You can fix that by saving the .max file, the .fbx file, and the textures all in the same directory. In Windows Explorer, copy all the useful textures from the CharacterName.fbm directory into the same directory where you have been exporting the .fbx file. Then also save your .max to that same directory. Once everything is in the same directory, you can reselect the texture file paths for each material so that they are pointing at these files instead of the other files. Then, when you export the fbx file, each texture’s path will be relative so you can copy all the necessary files onto any computer and use them correctly. After changing all the materials, export the fbx file again. You should be able to copy the fbx file and all its textures into a directory on a different machine and have them display properly. Save your .max file with all the updated materials. Making part of the object change color To make a material show the FlexSim color, append _fsclr to its name in 3ds Max: Configuring Animations With the character loaded, you can import the fbx files with just animation data, and it will automatically apply that data to the shape. The slider at the bottom controls the currently applied keyframe. The buttons in the bottom corner play the animation, pause, or step between keyframes. Animation data can be stored beyond the relevant range. You can open the Time Configuration dialog by pressing the button in the bottom corner. In that dialog, you can specify the speed of the playback and set a Start Time (keyframe number) and End Time for the animation you want to see in the viewport when you play. This dialog is only changing the playback options in the software; the actual data outside the specified range is still preserved. You may need to adjust these values as you import/modify/combine animations into one Timeline. Keyframes If you push the button on the top toolbar, it will open the Track View – Curve Editor. You can edit and modify Keyframes through this view. Click in the left pane and press Ctrl-A to select everything in the model. You should see keyframes appear if you have animation data loaded. Sometimes this window doesn’t refresh correctly and you can’t find keyframes. You can try closing it and reopening it, or pressing the and buttons to re-center the view on the keyframes in the configured time range. To edit the animations, you need to be able to see the keyframes in this view. You can drag the current frame by dragging the yellow bar around. You can zoom by scrolling the mouse wheel. Ctrl-mouse wheel will zoom the timeline (X-axis) without zooming the key values (Y-axis). You can select keyframes by dragging a rectangle around them. You can delete selected keyframes with the Delete key. You can move keyframes by dragging them. If you hold Ctrl while dragging them, then it will force the movement to be only along the X-axis and not along the Y-axis so that you don’t actually edit the keyframe’s values, just its time. You can duplicate keyframes by holding Shift and dragging them. Again, holding Ctrl will prevent the new keyframes’ values from shifting by holding the Y-axis still. You can scale keyframes by selecting them, putting the key keyframe indicator on the first keyframe in the selection, picking the menu option Edit > Transform Tools > Scale Keys Tool, and then clicking and dragging on one of the selected keyframes to scale all of the selected keyframes towards the yellow bar. This can be helpful to sync the timing of animations, such as walking empty vs. walking loaded. Preparing Animations for Merging When you load an animation into the file, the Track View – Curve Editor will show you the individual components that have keyframe animations. Different animations may affect different components and the interpolation of transformation information between keyframes may get messed up when you merge different animations that affect different components together into one timeline. To fix this, you can stamp a keyframe at the beginning and end of each animation that stores each component’s position at that point. You can also duplicate the first and the last keyframes to make it easier to specify perfectly-repeating animation clips in FlexSim later. Stamp a keyframe by moving the current frame (yellow line) to the keyframe you want. Then select all within the Track View – Curve Editor to select every component. Finally, press the Set Keys button ( ) at the bottom of the main 3ds Max window to store each component’s value as a keyframe. Duplicate keyframes by holding Shift and dragging them. Also holding Ctrl will prevent the new keyframes’ values from shifting by holding the Y-axis still. Merging Multiple Animations into One File To merge multiple animations into one file, you need the export each of the animations as an animation file (XML Animation File (*.xaf)). Then you can import each of these animations into a specific range of keyframes in the timeline. From the .max file without any animations loaded, import one of the fbx animation files. It will automatically apply it to the shape. Click in the Perspective view and press Ctrl-A to select everything. Then prepare the animation using the information above. Lastly, select the menu option Animation > Save Animation to save the animation as an XML Animation File (*.xaf). Reload the .max file without any animations and repeat this process for each animation you want to merge. Reload the .max file without any animations loaded. Click in the Perspective view and press Ctrl-A to select everything. Then select the menu option Animation > Load Animation to load an animation into the timeline. On the right side of the Load XML Animation File dialog, you can specify the keyframe number at which the animation should be inserted. Do not insert the animation at keyframe 0; make sure that keyframe 0 is the bind T-pose. This will be important later if you want to edit the mesh without breaking the skeletal rigging. You also need to be sure to specify Absolute and not Relative. Select the animation you want to import, specify the keyframe you want to insert at, and then press Load Motion to load the keyframes into the animation. Do this for each animation file you want to merge. Use the Track View – Curve Editor to determine the keyframe at which to insert each subsequent animation. Save your .max file with all the merged animations to a new file. Modifying the Mesh without Breaking the Rigging Sometimes you may want to modify the mesh after you have applied animations. I can’t guarantee that this always works perfectly, but there is a way to make some tweaks even after animations have been applied. Before trying to edit a skinned mesh, be sure to save your work so you can revert back to a clean state if something don’t work correctly. First, you need to be sure that the bind pose is frame 0 and that you are set to frame 0 on the timeline. Second, click on a mesh in the scene and click on the Modifiers tab. You will probably see an Editable Poly with a Skin modifier applied. If you click on the Skin modifier and expand the Advanced Parameters, you will see an Always Deform checkbox. If you clear the Always Deform box, you can then click on the Editable Poly and modify it. You then need to recheck the Always Deform box on the Skin modifier once you are done making edits. Ensure that your animations still work after making edits. In my tests with the operator shape above, when I cleared Always Deform, the mesh moved up slightly. This made the Tops mesh not line up correctly with the Body mesh. To fix that, I cleared and immediately checked Always Deform for each mesh (each mesh then moved up the same amount). Then I verified that the animations still worked and that the arms still lined up correctly with the shirt. Reducing Polygon Count 3ds Max has features for reducing the polygon count of an object. You can apply these modifiers without breaking the rigging by following certain steps. You can display the polygon count of your shape by clicking in the Perspective view and pressing the 7 key on the keyboard. As stated above, before modifying the mesh, clear Always Deform on the Skin modifier. To reduce the polygon count, you can use the ProOptimizer modifier. After clearing Always Deform, click on the Editable Poly and then select ProOptimizer from the Modifier List to add it to the stack between the Editable Poly and the Skin modifiers. In the Optimization Options, be sure to check Keep Textures. Then press the Calculate button. Then you can specify a Vertex % and it will start to remove vertices from your mesh. After applying the ProOptimizer modifier, the normals on your mesh might be messed up. You can recalculate the normals based on a crease angle using the Smooth modifier. Apply the Smooth modifier after the ProOptimizer and before the Skin modifier. Check the Auto Smooth box and specify a Threshold. You can use a value of 89 to get a very smooth surface, only applying a crease to angles that are greater than 89 degrees. After making these changes, be sure to recheck Always Deform on the Skin modifier, and test your animation to make sure the rigging is all still working properly.