The ideal way to access the User Manual is by viewing an electronic copy either online or inside the software. Online User Manual The User Manual is available online at docs.flexsim.com. There you can select a software version and manual language. We hope to provide additional languages in the future. In-software User Manual There are two ways to open the manual inside FlexSim: Click the User Manual button on the main toolbar. Click the Help menu and then click User Manual. Why the electronic manual is preferred The online / in-software version of the User Manual is better than a print or PDF version for many reasons, including: Regular updates - The electronic version keeps up with every new FlexSim release. A printed manual unfortunately goes out of date every four months with each new release of FlexSim. The online manual allows you to also view older versions of the manual. Animated GIFs - Animations enhance communication by making it possible to see FlexSim in real time. Animated GIFs won't work in a printed copy. Better search - The online version allows you to search the manual using Google. The in-software version also has a search program which is easier than trying to find relevant keywords within a large document. Styled and reflowable tables, images, and tip boxes - While these elements look good on the web, print or PDF versions don't always correctly format their placement within the document. Better organization - A printed manual won’t always maintain the same organization as the online and in-software manual, making it more difficult to navigate. Mini-table of contents - Each topic has easy navigation within each topic via inner-links. A printed or PDF mini-table of contents (found in most topics) can't function as a clickable index, making it difficult to easily skip to relevant sections within a topic. Printing topics from the User Manual While we don't recommend printing the entire manual, you can easily print individual topics for later reference. Print from the online manual Visit docs.flexsim.com using your favorite modern web browser. Use the table of contents in the left navigation bar to visit the topic you want to print. Use the printer button in the upper right of the topic window to print the page: Print from the in-software manual In FlexSim, open the User Manual and browse to the topic you'd like to print. Press the 'world' button to open the topic in your web browser. Choose the print function inside your web browser - depending on your browser, you may be able to right-click the web page and choose Print, or you can use the keyboard combo Ctrl+P.

O presente tutorial tem como objetivo explicar o significado dos principais estados assumidos pelos objetos padrões do FlexSim em um modelo convencional que contenha tanto objetos fixos (Fixed Resources), quanto executadores de tarefas (Task Executers). Link para o vídeo: Explicação dos Principais Estados no FlexSim Obrigado.

O link que segue leva a um vídeo no qual é apresentado uma introdução à nova ferramenta do FlexSim, o Statistics Collector. No vídeo, explica-se quais são as configurações básicas da ferramenta. Também pode-se acompanhar um exemplo simples, desde a coleta de uma estatística básica, até a apresentação dos dados em um gráfico, do tipo histograma. Vídeo Tutorial: Overview sobre Statistics Collector Esperamos que aproveitem mais este vídeo tutorial.

We recently had a customer ask how to draw a flow item label on the flow item object. I thought that this information would be useful to other FlexSim users as well. In the 3D model, drag and drop a Text visual from the Library. Select the text in the 3D model, then copy it to the clipboard using Ctl + C Go to the Toolbox tab, open the FlowItem Bin, and double click the Flow Item of interest. Right click the 3D flow object, and click "Explore Tree" Click in the white space underneath the Flow Item's node, and use Ctl + V to paste the text as a subnode to the flow item. If you zoom out in the Flow Item's 3D view, you will see the text. You can double click on the text and use the General tab to position it wherever you would like. Double click the 3D Flow Item. Underneath the Labels tab, select the green plus sign to add your label. In my example, I added a number label called "Type" with a default setting of 0. Click "OK". Double click the text that you added in step 5 and 6. Underneath the Display tab, click the custom code button next to Text Display. Within the custom code type something like the following: /**Custom Code*/ Object current = ownerobject(c); treenode textnode = param(1); textnode.value = "Type: " + string.fromNum(current.up.Type); //Note: If your label is called something other than "Type", make sure to replace it in both places. Click out of the "FlowItem Bin" tab and back into the "Model" tab. Double click the source where your Flow Item is being created. Underneath the Triggers tab, add an On Creation trigger. Click the green plus next to the On Creation trigger. Hover over Data and select "Set Label". Make sure the text next to the Label field matches the text of the label you set in step 7 and 8. By following these steps, each Flow Item will have a text label that displays the Flow Item label next to the Flow Item object. Please see the attached gif for a demonstration. flowitem-label.gif

Attached is a sample model that uses Google's OR-Tools python module to find optimal AGV dispatching solutions. I recently stumbled on Google's OR-Tools, which includes several classes for finding optimal solutions to things like vehicle routing, scheduling, bin packing, etc. Since FlexSim now has a mechanism for easy connection to python, I decided to try and see if/how it could be connected to FlexSim for testing AGV dispatching strategies. I threw together this model just to see how/if the connection can work. All source/destination locations are chosen at random, and work inter-arrival rates are random with a user-defined mean inter-arrival time. To get this model running on your side: Install a version of python Run the following from the command-line: python -m pip install ortools In FlexSim, make sure your preferences are configured for the correct version of python, and that python is part of your PATH environment variable. Open the model. In the Parameters table, set DispatchMode to 'VRP Solver'. This model uses the Vehicle Routing Problem solver to find optimal AGV assignment strategies. The main work generation logic is in the 'Work Generation' process flow. At random intervals, work requests arrive. They are assigned to random source and destination locations. Then, when dispatching in 'VRP Solver' mode, the logic calls the optimizeVRP() user command. This command packages the current state of the model into a valid vehicle routing problem, and then passes it to the py_optimizeVRP() user command, which is implemented in python, in the AGVVRP.py file. The command creates the VRP problem using the OR-Tools classes, and then calls the solver, returning the results. OptimizeVRP() then interprets the results and assigns AGVs as needed. Note that the VRP is re-solved every time new work arrives. You'll see little 'freezes' in the execution of the model, because it is solving the VRP at each work arrival. Note that the standard Vehicle Routing Problem is slightly different from the problem this model needs optimized: In an AGV model, there’s no depot. Instead AGVs may be currently located anywhere in the warehouse. There’s no ‘depot-loaded’ capacity of an AGV, and no ‘demand’ at customers. The standard VRP is a situation where trucks are loaded at the depot, and then depleted as they visit each customer in the route. This is not present with single-capacity AGVs. When an AGV picks up at an origin location, it must immediately deliver to the destination location. In order to wrangle the AGV problem into a valid vehicle routing problem that can be solved by OR-Tools, I constructed the problem as follows: I made each AGV’s ‘current location’ a node in the graph The distance from the depot to the AGV’s current location is 0 The distance from the depot to any other node in the graph is prohibitively large. This will cause vehicles to always go to their 'current location' first, with 0 cost. The distance from any node in the graph back to the depot is 0 A given AGV must visit its current location as part of its route. This can be added as a constraint to the problem in OR-Tools For immediate unload after loading, I initially tried adding this rule as a constraint, but the solver hung when solving. So, instead of graph nodes being locations in the facility, I made graph nodes represent ‘tasks’, i.e. visiting this node means picking up the item AND dropping it off. As such, the ‘cost’ of ‘visiting’ a ‘task’ node is the cost to travel from the ‘destination’ of the previous node to the ‘origin’ of this ‘task’ node, plus the cost to travel from the ‘origin’ of this task node to the destination of this ‘task’ node. Once I did this, OR-Tools was able to solve the problem 'optimally'. By optimally, I mean it was finding the AGV routing that minimized the maximum 'travel makespan', which is the maximum distance route of all of the AGVs. Once I had done this, I wanted to compared it with various heuristic-based scenarios. So I set up a 'Closest' dispatch mode. Here, when an AGV finishes a task, it will take up the next task whose pickup point is closest to its current position. I also created a 'FIFO' dispatching mode, which is that work will be dispatched to AGVs always in FIFO order. These three dispatching modes I compared with the experimenter. My initial experiments showed some interesting results. Most interesting was that in 'VRP Solver' mode, work task time-in-system was relatively high. This is because the objective function completely ignored time in system of the work, and was only optimizing for vehicle travel distance. So some work was being pushed off until much later because vehicles could get better travel distances by pushing it off. To account for this, I added a 'soft upper bound', which is kind of like a 'due date' for the work. Namely, work is due to be finished 800 'meters-of-agv-travel' after it arrives. This was a quick-and-dirty workaround and could certainly be improved, but it did serve to get the time in system for VRP Solver mode down. Below are some of the resulting experimenter results. AGVTaskTime - Time from starting a task to finishing it (i.e. a kind of takt time) The VRP solver performed the best across all scenarios here, and was especially better than the other strategies in low-demand scenarios. This intuitively makes sense. When there are a lot of under-utilized AGVs, the closest and FIFO strategies will always dispatch idle AGVs to do work, which could potentially make them travel long distances. However, the VRP solver can find opportunities to decrease travel distance by waiting to dispatch an AGV that will be near a task in the future, and leave other AGVs idle. Note that I think the 'closest' strategy only finds the 'closest' next task for an AGV that just finished a task, not the 'closest' idle AGV for an arriving task. Obviously that could be changed for a better performing 'closest' strategy. On the other hand, I think in this model all idle AGVs go back to the same park location, so such a change would require distributed park locations to take advantage of closer idle AGVs. AGVWorkStaytime - time-in-system for a given AGV task Here the 'closest' strategy actually performed better than the VRP. This would seem counter-intuitive at first, but upon further evaluation, it does make sense. The VRP, in its current form, only optimizes for total AGV travel distance. It completely ignores job time in system/due dates/etc. So the solver will always assign a route that is shorter even if that route pushes back jobs that have been in the queue for a long time. The solver also re-solves every time a new job arrives. So we may be having scenarios where some jobs are always 'better' to be pushed to the end of the route, and so they keep getting pushed back, resulting in poor time-in-system performance. The solver does include soft and hard job 'due dates', so we could make adjustments to the problem to make the VRP get better time-in-system results. AvgAGVUtilization AvgAGVUtilization is where the VRP especially shines in low-demand scenarios. It finds opportunities to leave AGVs parked because there will be opportunities for busy AGVs to take up jobs in the future with minimal extra travel overhead, whereas the 'FIFO' and 'Closest' strategies will always dispatch idle AGVs to unassigned jobs, causing extra unnecessary empty travel. I am still a bit perplexed by the high-demand scenarios though. Here the 'Closest' and 'FIFO' strategies both beat the VRP in the 120/hr and 102/hr scenarios. This probably would warrant further investigation as to why the other strategies do better here. It may be that, in these scenarios, the AGVs cannot keep up with demand. So there is a queue of jobs that is ever-increasing. The VRP solver is optimizing the full plan, meaning it is scheduling job assignments, and finding travel distance minimization opportunities, that are way out into the future. And it is not getting the opportunity to execute those optimized routes because the problem is being re-solved at each job arrival. With an increasing job queue, the 'closest' and 'fifo' strategies might be actually doing better specifically because they are short-sighted. Just take the closest job to you. On the other hand, if we have increasing job queues (i.e. the AGVs can't keep up), then the AGV utilization should be around 100% anyway, which it's not. Anyway, it's something still to churn on. ThroughputPerHour The throughput per hour indicator tells us whether the AGVs actually kept up with the jobs. If the AGVs were able to keep up with jobs, then the resulting means should be right around the scenario's throughput/hr number. It looks like FIFO got way behind on both the 120/hr and 102/hr scenarios. 'Closest' and VRP both got a little behind in the 120/hr scenarios. One exciting possibility of using this design is that the python script is not technically dependent on FlexSim. So you can use FlexSim to test your python-based optimization, and then you can deploy that python script in a live setting. AGVVehicleRoutingProblem.zip

Neste vídeo, abordamos de forma rápida e bem resumida, como funciona simulação em ambientes hospitalares e elaboramos uma rápida demonstração através de um passo a passo, o funcionamento do software FlexSim Healthcare. O vídeo encontra-se disponível no canal do Youtube da FlexSim Brasil.

Nesse vídeo disponível no canal Youtube da FlexSim Brasil, demonstramos como retornar as licenças standalone do FlexSim em um procedimento simples e rápido. Para licenças em rede, o procedimento é diferente, no entanto, o arquivo com as instruções para retorno das licenças standalone e em rede, encontra-se anexo nesse artigo. passoa-passo-retorno-de-licenca-flexsim.pdf flexsimlicenseserver-upgrade.pdf flexsimlicenseserver-installation.pdf

The Statistics Collector is a powerful object. Its purpose is to record data about the model run as a table, and it is very effect at doing that. However, it is very abstract; being abstract allows it to be highly flexible, but makes it more difficult to learn. This article is meant to help you understand (or remember) how the Statistics Collector models the problem of converting model data from objects and events into a table of data. This article compares a Statistics Collector to a train being loaded an unloaded. It is NOT an article about how to model a trainyard or other rail-industry systems. The purpose is to compare the Statistics Collector, which is very abstract, to something most people understand. So we'll start by talking about the story of a simple freight train. This story has the following elements: Starting - the train starts its journey a the beginning of the line Loading Stops - the train makes several stops along the way to load goods Unload Planning - unlike a normal train, this train determines where it needs to go after this point based on what was loaded into the train Unloading Stops - the train makes several stops along the way to unload goods This is a very simple story. Just keep the sequence in mind - the train starts, makes several load stops, figures out its destinations, and then makes several unload stops. To see how this is like a Statistics Collector, take a look at this model: StatsCollectorVisualization.fsm This model is very simple. It consists of a Source, Queue, Processor, and a final Queue: Items are generated at the Source and flow through the Model. Items have a Type label, so that there are three Types of items: Type 1, Type 2, and Type 3. If you zoom out, you'll see a much bigger section of the model: This section simulates the logic of a Statistics Collector. As the model runs, you'll see AGVs run along the track, like a train. You'll see them make several stops and pick up boxes. Then you'll see them stop at the "Table" (the bigger black box in the image above) and "unload" data into the table. The Statistics Collector tool is a lot like this simulation. It has an internal "train", and you reference that train with the keyword data. When you use the Statistics Collector, you are configuring the journey of the train, which determines what data is recorded in your table. Starting the Train The statistics collector creates a train (the data object) when it observes an event. You configure which events a Statistics Collector cares about on the Events tab. You can listen to many different things, including: A flowitem enters or exits an object A token enters or exits an activity The model is reset The model gets to the warmup time Whenever any of these things happen, the Statistics Collector creates a train (again, called data) and starts it going on its journey. In this example, the Statistics Collector listens for items to enter Queue1. It also listens for items to exit the processor. Either event will start a train. Loading the Train Once the train is going, the Statistics Collector "loads" the train with information. In reality, this happens by adding labels and setting values on data. First, data gets all the values associated with the event (the event parameters) assigned as labels. Then, data gets all the labels specified in the "Additional Labels" section. Finally, data visits the Row Value station. Here, the special property called rowValue is set. Note that as you load the train, you can use anything already on the train to get more information loaded on train. In the example model, the row value is set to "data.item.Type"; data is the train, and the "item" label is already on board, so the row value field can use that to load additional information. Unload Planning The next stop for the train is something that you can't really see, but that is very important. The Statistics Collector inspects the row value on data. The Statistics Collector remembers all row values that it has ever inspected, and it adds a row to the table for each unique row value. This is where the row value got its name. If the Statistics Collector sees a value it has never seen before, it adds a row to the table, and associates the new row with the row value. Once the row is either added or found, the Statistics Collector sets the property rowNum on data. So now, the train knows which row it is going to visit. All that is left in this phase is to figure out which columns the train will visit. First of all, if the row was added, the train will visit all the columns to set their initial value, which is called the Row Add Value (the value to set when the row is added). Whether or not the row was added by the event, the train will also visit any columns that are connected to the event, setting the value to the Event Value (the value to set when a connected event happens). So in summary, the Statistics Collector figures out which row the train will visit based on the row value, adding a row if necessary. If the row was added, then the train visits all columns in the row, to set the Row Add Value. If the event is connected to any columns, the train will also visit those columns, and set the Event Value. Unloading the Train Finally the train can unload data into the table. As it visits each cell, it unloads information into that cell. As the user, you configure what gets unloaded at each cell. You can unload anything into the cell, including anything on the train, anything connected to anything on the train, or anything globally accessible. In reality, whenever the Row Value or Event Value is evaluated, you can access and labels or properties on data. You can also use values on data to access other values. Or you can access global values, such as the current model time, or a Global Variable, or a value in a Global Table. In the example model, the Event Value field accesses the value that is currently in the table at the cell being visited by data. Does the statistics collector really have to travel? In the visualization model, the data train takes up time on the clock to load, plan, and unload. However, for the true Statistics Collector, all of that happens in zero time. For example, when an item enters Queue1, the real statistics collector in the model finishes updating instantaneously. In contrast, the visualized statistics collector train is still moving to get more labels. This is for illustration purposes only. What about "Update when accessed" columns? All Statistics Collectors know when something is trying to read their values. For example, if you have a chart pointed at a Statistics Collector, and that chart repaints, it will read the Statistics Collector. Or if you access the table through FlexScript with code like Table("MyStatisticsCollector")[1][1], that will read the Statistics Collector also. So in a way, it's just another event. However, there are two differences: You can't load custom labels on the train for this event; it "skips" those stops The train visits all rows in the table When you set a column to update when the value is accessed, you are basically connecting it the "OnAccess" event. So when the table is accessed, the data train visits all rows of the table. For each row, it gets the rowValue and rowNum properties set, and then it visits all the "update on access" columns. What about other features? This article just covers the basics, and doesn't cover other features of the Statistics Collector. Just know that there are many ways to configure the train's journey. For a full reference, see https://docs.flexsim.com/en/21.0/Reference/Tools/StatisticsCollector/ However, all of the features discussed in the reference add more detail; you can just improve your mental image of how the Statistics Collector works.

Here on Answers it's often helpful to post code in a question or an answer. When you do so, there are certain steps that should be taken to avoid being blocked by Answers' internal security mechanisms. This article describes the proper way to submit code without arousing suspicion from our threat detection software. Some of our customers are all too familiar with the screen below after asking a question or adding a comment that includes code. What is Cloudflare? Cloudflare is a 3rd party threat detection software specifically designed to detect malicious code/software that may threaten a website. Once you've seen this screen above, there is no way for us to un-block you, as it is an automated 3rd party software. Unfortunately, at this point you have to wait a variable amount of time until Cloudflare decides your IP address is no longer a threat and you will again be allowed access to answers.flexsim.com. What am I doing wrong? This warning usually presents itself when code has been typed or pasted directly into the body of your post, like this: How do I properly format my code? First paste or type your code into the body of your post. Then, highlight your code and click the Code button in the toolbar. It looks like this: < > How can I tell if my code is properly formatted? After highlighting your code and clicking the code < > button, you will see that your code now has a border and light background: Remember, if you have multiple blocks of code, you can select just the code you want to be grouped together in the first block, hit the code button in the toolbar, and then select the second group of code and again hit the code button to make the second code block, etc. After you submit your post, you will see your code formatted with syntax highlighting, a mono-spaced font, and the lines numbered, like this: Conclusion Adding code to your post is an essential task for many of our customers. If you follow these guidelines, you should be able to avoid being blocked and ensure you have uninterrupted service on our answers site. If you feel you have properly entered code and are still being blocked, please inform us and include the code and method of formatting you were using when you were blocked. We'll gladly look into it. As a fallback, you can take a screenshot of your code and add it to your post as an image, but still let us know there was an issue, and we'll look into it. Finally, if you're in a hurry and Answers troubles are keeping you from getting the help you need, remember to contact your local FlexSim representative directly by phone or email.

FlexSim 2021 Update 2 Beta is now available (Updated 26 July 2021) To get the beta, log in to your account at https://account.flexsim.com, then go to the Downloads section, and click on More Versions. It will be at the top of the list. The More Versions button does not appear when logged in as a guest account. Subsequently the beta is available only to licensed accounts and accounts that have a license shared with them. Learn more about downloading the best version of FlexSim for your license here. If you have bug reports or other feedback on the software, please email dev@flexsim.com or create a new idea in the Development space. FlexSim 21.2.0 Release Notes Added a Template system for inheriting properties from other objects. Added new manipulation handles for moving, rotating, scaling, and connecting objects in the 3D view. Added ability to delete connector lines. Added a StateTable object. Added a getstatetableutilization() command. Added a Pass-through parameter type. Added a checkbox so transporters can move their forks separate from traveling. Added more Task Executer shapes. Improved treenode.getPath() to return a relative path to nodes that aren't a direct ancestor. Added a Frustrum Culling checkbox in Visuals. Agent Added an AgentSystems property. AGV Allowed the user to define some basic routing cost multipliers, including: An option to divide by speed, allowing for fastest path routing (instead of shortest path) An option to define a custom path-based multiplier to the cost Added Path Conditions, which allow you to "close" certain paths, based on either model state or agv state, etc. Added a Prioritize Control Point Lookahead option. A* Added the capability to have oblong grid node sizes. Allowed barriers to be inside visual tools. Added the ability to add dynamic barriers to the A* system. This is done by calling AStar.navigator.addDynamicBarrier(), then AStar.navigator.removeDynamicBarrier() to remove those barriers. Added several triggers to A* objects: AStar.Navigator: OnGridChange: called when dynamic barriers are added or removed AStar.Traveler: OnCalculatePath: called just before a traveler's path is being calculated AStar.Traveler: OnNavigatePath: Called just before a traveler runs the algorithm to define kinematics and cell allocations for traveling its path. Also allows the ability to override the default behavior and define your own. Made several additions to AStar.Traveler API, AStar.Navigator API, etc. Added a TravelPatterns property. Backwards Compatibility Note: The following changes may slightly change the way updated models behave. Improved the deadlock recovery algorithm. Now it will add each traveler in the deadlock, as well as any travelers that are waiting on those travelers, as dynamic barriers. Then it will go through each traveler in the deadlock, remove it from the dynamic barriers, add any additional travelers that are adjacent to that traveler as barriers, then try to recalculate the path to the traveler's destination. If it finds a traveler that can still travel all the way to its destination, that traveler will be rerouted. Otherwise, if it finds a traveler that can travel at least on grid square, it will reroute that traveler. Otherwise, it will give a notification of unrecoverable deadlock. GIS Added the GIS module to the Flexsim Installer. Process Flow Added a ProcessFlows property. Added a ProcessFlowVariables property. Backwards Compatibility Note: The following changes may slightly change the way updated models behave. Removed instance object editing from Object Process Flows.

This article borrows from @jordan.johnson's original guide for deploying a distributed experiment/optimization with Amazon. Distributed Terminology Please be familiar with the terminology related to experiments and optimizations. Replication - a distinct model run. We'll use this term below for both replications (experimenter) and solutions (optimizer). Instance - a system, whether virtual or bare-metal, local or remote, meeting FlexSim's recommended requirements, and used for running distributed replications. Main PC - the system from which the user configures and initiates the distributed experiment or optimization. The Cloud - a shorthand term for remote servers/systems hosted in a data center, sometimes by a 3rd party such as Amazon, Microsoft, Google, or others. Background Concepts In discussing distributed experiments or optimization, you should start with a good understanding of both the Experimenter and the Optimizer. Please read and understand this user manual entry which includes explanations of key concepts that will be important as you continue. Other user manual articles provide additional guidance on configuring your own experiments or optimizations. Search the online user manual and this community if you have additional questions regarding experiments or optimizations, as needed. Using the experimenter requires a FlexSim license. The optimizer requires a license for the OptQuest add-on. Please contact your local distributor for more information or to request a test license for FlexSim and/or OptQuest. What are distributed experiments or optimizations? Experiments or optimizations can create dozens, hundreds, or even thousands of distinct model runs (or replications) to test various scenarios, build confidence intervals of the results, or zero-in on an optimal solution. A distributed experiment or optimization takes those individual model runs and assigns them (distributes them) to run across a group of computers. If you needed to run 1000 replications, and you have 4 computers available, each computer could run 250 replications, getting your results 4 times faster. When should you distribute? Using distributed replications can significantly reduce the required time to run an experiment if: The single-run time for your model is high (a couple minutes or more) You anticipate running a high number of replications If the time per replication is short, then the increased communication overhead may outweigh the benefit of using distributed replications. The communication overhead increases because all distributed replications still report results to a single FlexSim process on the Main PC, and that communication occurs over the network (local instances) or internet (remote instances), rather than on a single computer. If an experiment or optimization completes in an acceptable amount of time, you may not need to use distributed replications. Financial Costs 3rd party cloud providers such as Amazon, Microsoft, Google, and others, charge for their services. Your cost is usually based on the hardware you provision for your Windows instances, and the length of time those instances are live. Typically you only run your instances when running your experiment or optimization, and cancel or decommission your instances when your replications are complete. In this way you minimize the cost of your distributed experiment or optimization. The costs and process of decommissioning your provisioned instances differ based on which 3rd party cloud provider you use. You may have access to local computing resources that could be configured for use in your FlexSim experiments or optimizations. In this case you may avoid additional costs relating to 3rd party cloud providers by using your own on-premises computers. Licensing for distributed Windows instances Windows instances used solely for distributed replications DO NOT need an individual FlexSim license. Only the FlexSim installation on the Main PC must be licensed. System requirements for distributed Windows instances Graphics A Windows instance should meet or exceed FlexSim's recommended requirements. However, because replications run as background processes without graphics, they need not meet the graphics requirements and do not need hardware accelerated graphics. CPU An instance can run as many concurrent replications as it has CPU cores. If, for example, an instance has 32 CPU cores, it can run 32 replications of your model simultaneously. RAM For this example of a 32-CPU Windows instance, if you want FlexSim to run 32 replications simultaneously - one on each core - then the instance must also have enough RAM to handle 32 concurrent model runs. On your Main PC, use Windows Task Manager to watch a single model run's RAM usage to determine its peak RAM utilization. If your model's RAM utilization peaks at 3GB throughout the course of a model run, you should make sure your 32-CPU system has at least 32*3 = 96GB of RAM for replications alone, along with a good ~10% more for additional overhead used for the Webserver and the statistics gathering and reporting from all the replications (this number could be more or less, depending on the model's stats gathering). In addition, you must account for your system's baseline RAM utilization - how much RAM it uses just to run the operating system and all its background processes. You can find this baseline by checking the Task Manager at a moment when you're not running any simulations. Add all these together to see how much RAM would be necessary to run a replication on each core of the instance. If your instance doesn't have enough RAM to handle that many simultaneous replications, you'll need to limit the number of CPUs FlexSim will use when configuring your Cloud Computing settings from the Main PC. Disk Disk space is usually not an issue. However, if you are using the Store Data on Hard Drive option in the Statistics Collector, you will need to be sure that there is enough disk space to run the model to completion on the hard drive, multiplied by the number of cores. The amount of disk space on each instance may affect the total cost of using a 3rd party cloud provider. More information See the related sections in the article Recommended System Requirements for a more in-depth discussion of system components such as CPU and RAM, and how they relate to running your simulations and experiments. Provisioning distributed Windows instances The process of provisioning 3rd party cloud instances for running distributed FlexSim replications will differ from provider to provider. FlexSim has guides for the following cloud providers: Amazon Web Services Wherever you decide to host your instances, the important part is that eventually you end up with a group of Windows instances, each with a unique IPv4 address accessible from the Main PC. A custom Windows image Your 3rd party cloud provider probably allows you to save a custom Windows image that can include software and settings that you need for each Windows instance. Doing this once for a custom Windows image saves you time when starting new instances - each instance will start with the software and settings preconfigured for your purpose. On your custom image, do the following Download and install FlexSim. Use the same version of FlexSim as is licensed on the Main PC. Remember, you DO NOT need to activate a license. Run FlexSim. This creates a directory that is needed later. After FlexSim finishes its first start, close FlexSim. Download and install the FlexSim Webserver for your installed version of FlexSim. Edit the Webserver's configuration file appropriately for your situation. Default location is here: "C:\Program Files (x86)\FlexSim Web Server\flexsim webserver configuration.txt" Start the FlexSim Webserver from your Windows Start menu, or manually from the default location here: "C:\Program Files (x86)\FlexSim Web Server\flexsimserver.bat". The Webserver will download necessary files the first time it is run. Close the Webserver after it has completed its initial startup. Allow both FlexSim and node.js through the Windows Firewall. To do so, use Windows' Allow an App through the Windows Firewall tool. You will need to browse for both FlexSim and Node.js. Example locations (exact locations may vary by version): C:\Program Files\FlexSim 2023\program\flexsim.exe C:\Program Files\nodejs\node.exe If you are not familiar with the FlexSim Webserver, please review its documentation and test it out on a local machine to understand what it does, its configuration options, etc. If you cannot configure a custom Windows image, you will need to do the above on each instance individually. Initialize instances Launch your instances. On each instance, do the following: Log in to the instance with Remote Desktop or some other solution. Start the FlexSim Webserver from your Windows Start menu, or manually from the default location here: "C:\Program Files (x86)\FlexSim Web Server\flexsimserver.bat". Note the instance's IPv4 address. Once all instances are running the Webserver, you are ready to configure a distributed experiment or optimization from the Main PC. Author's Note: There is probably a way to make it so that when instances start up, they automatically run the Webserver, so that you don't have to manually connect to each one. I welcome any suggestions or steps for how to make that happen. Configuring a distributed experiment or optimization Once you have a list of running instances available, launch FlexSim on the Main PC. Open the experimenter interface from FlexSim's Main Menu > Statistics > Experimenter. Configure your experiment. As part of your configuration, under the Advanced tab, select the option to Use Distributed CPUs. Press the button to Configure Cloud Nodes. This will open the Global Preferences' Environment tab. Enter the IP addresses of your instances into FlexSim. Enter the port numbers you configured in their FlexSim Webservers. If your instances will max out RAM before CPUs, enter a CPU count representing the maximum number of concurrent replications your instance can handle. For more information on using remote computers for Experiment Jobs, see Running Jobs on the Cloud for more information.

Run FlexSim "in the cloud"? Instead of provisioning a desktop or laptop PC that meets FlexSim's recommended system requirements, you may prefer for your FlexSim users to use a thin client PC which connects to a more powerful back end system that actually runs FlexSim. FlexSim does not test our products in such environments, so your user experience may vary. Meet the system requirements It is important that the remote system meet or exceed FlexSim's recommended system requirements. Usually for CPU and RAM that is not a problem. In the past, the biggest hurdle was typically graphics. Does the remote machine support hardware accelerated graphics, and support streaming those graphics to the thin client where the user is actually sitting? If the graphics are not accelerated, or if accelerated graphics can't be streamed to the user, then the system doesn't meet FlexSim's requirements and you may have a bad user experience. In the past In the past, provisioning such a system to meet FlexSim's requirements was quite a specialized combination of hardware and software. Even today not every cloud provider has the infrastructure to stream accelerated graphics. Our anecdotal experience is that such set ups are becoming more common among 3rd party cloud providers. Going forward Major players now seem to have mainstream support for hardware accelerated graphics: https://aws.amazon.com/ec2/elastic-graphics/ https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu https://cloud.google.com/gpu/ As mentioned above, FlexSim is not targeted to these environments and the software is not tested in these environments, so your mileage may vary. Conclusion With more and more cloud providers enabling hardware accelerated graphics, there is a chance that FlexSim could work well on a cloud-based platform. If you have tried running FlexSim remotely using one of these or another cloud provider, we'd love to hear your experience. Please comment below to let us know about your setup, what worked, and what didn't.

Você tem dúvidas sobre o que é, ou como funciona o recurso List? Este Tutorial vai apresentar uma visão geral do recurso e fazer um rápido exemplo. Acesse o link : https://youtu.be/P7wGuQ7UKuQ Você pode entrar nesses outros tutoriais em português que apresentam outros exemplos da utilização do recurso: 1) https://answers.flexsim.com/articles/39239/tutorial-em-portugues-como-direcionar-os-itens-usa-3.html 2) https://answers.flexsim.com/articles/39233/tutorial-em-portugues-como-utilizar-o-recurso-list-1.html

"switch by case" is a stucture in many programming languages. It is a availbale in Flexscript. A case must be a number for a switch by case structure in Flexscript. A decide activity can switch even to string value cases. All you need is to rename the connector from a decision activity to a following activity. process_flow_by_case.fsm

One of the new features in FlexSim 2018 Update 2 is the ability to use distributed CPUs for the Experimenter or Optimizer. Those cores can exist on any machine that meets the following requirements: The machine is accessible by IPv4 address The machine has FlexSim installed The machine is running the FlexSim Webserver (version 18.2.0 or later) on port 80 The machine meets hardware requirements to support running the specified number of simultaneous replications. This article describes how to use Amazon Web Services to create remote machines that meet the above requirements. Using AWS, you can launch and connect to as many servers as you want, and pay only for the time used on those instances. Anticipated Use Case Using distributed CPUs can significantly reduce the required time to run an experiment if: The time to run a single replication is high (a couple minutes or more) The number of replications to run is high If the time per replication is short, then the increased communication overhead may outweigh the benefit of using distributed CPUs. The communication overhead increases because all CPUs still report results to a single FlexSim process, and that communication occurs over the internet, rather than on a single machine. If an Experiment or Optimization completes in an acceptable amount of time, you may not need to use distributed CPUs. Financial Costs Amazon charges for its services. Costs for running instances (and transferring data out of them) are found here : https://aws.amazon.com/ec2/pricing/on-demand/ Costs for storing data (including AMIs) are found here: https://aws.amazon.com/ebs/pricing/ These instructions include steps to create an AMI, which will likely cost around $1 per month at current rates. It also includes steps for launching high-performance instances, which have an hourly cost. Suitable instances can be as cheap as $0.50 per hour. Note that there are also charges for transferring data from an instance to another computer. Step 0: Complete an EC2 Tutorial If you are not already familiar with EC2 instances, you should complete the following tutorial: https://docs.aws.amazon.com/AWSEC2/latest/WindowsGuide/EC2_GetStarted.html Step 1: Create a Security Group You will need to create a security group with the following rules: Allow HTTP traffic on port 80. This allows incoming connections to the FlexSim Webserver. You can choose which inbound IP addresses to accept. You should make this as strict as possible. The strictest setting is to only allow incoming connections from the current IP address. Allow TCP traffic on ports 9000-9200. This is a Custom TCP rule. Note that you can make this range smaller, if all machines that use this Security Group have fewer than 200 cores. For example, if the biggest machine launched with this Security group has 72 cores, you would only need ports 9000-9072 in this rule. Allow RDP traffic on the default RDP port. This allows you to connect to the instance using remote desktop. Name the Security Group something that shows that this group is related to running FlexSim instances, and add a description if you want. Here is an example Security Group that works, but allows all TCP traffic, and so should only be used for testing purposes: Step 2: Create a Custom Amazon Machine Image (AMI) You will need to create a custom AMI. A custom AMI allows you to launch an instance that has the software you need already installed. To create one, follow these steps: Download the FlexSim installer and the Webserver installer. If your upload speed is slow, you can skip this step. Launch the cheapest instance available, with the Windows 2016 Server Base image. There are probably free-tier instances available in the list Connect to that Instance with Remote Desktop Copy (or download, if upload speed is slow) the FlexSim installer to the remote machine, and run it to install FlexSim. You do NOT need to activate a license. Run FlexSim. This creates a directory that is needed later. Then close FlexSim. Copy (or download, if your upload speed is slow) the Webserver installer to the remote machine, and run it to install the webserver. If using the 2018 Update 2 Beta, be sure to change the Webserver configuration file accordingly. Run the Webserver. It will download a bunch of files the first time it is run. Allow both FlexSim and node.js through the Windows Firewall. To do so, use the Allow an App through the Windows Firewall tool. You will need to browse for both FlexSim and Node.js. Both are in the ProgramFiles directory. Disconnect the Remote Desktop session Go to the list of Instances in the AWS Console From the Actions menu, select Image, then select Create Image: Add a name and description. Click the Create Image button On the next dialog, click the View Pending link, or navigate to the list of AMIs: Once the image becomes Available, terminate the running instance. Step 3: Launching Usable Instances Before running an Experiment or Optimization, you need to be sure that all remote instances you will use are current running. To launch and initialize these instances, follow these steps: In the EC2 Console, choose Launch Instance Select the AMI you created, from the list of My AMIs Choose the Instance Type and quantity Set the Security Group to use the Security Group you created Launch the instance Once each instance is initialized, connect to each one with Remote Desktop. Note that when you launch an instance with a given Key File, the password for each instance will be the password associated with that Key File, so you only need to decrypt the file once. Start the FlexSim Webserver in each instance Once all instances are running the Webserver, you are ready to run an Experiment or Optimization. Author's Note: There is probably a way to make it so that when instances start up, they automatically run the Webserver, so that you don't have to manually connect to each one. I welcome any suggestions or steps for how to make that happen. Step 4: Using the Instances in FlexSim Once you have a list of running instances available, you just need to input their IP Addresses into FlexSim, on the Advanced tab of the Experimenter: Once you have input this information, you just need to click the Experiment or Optimize button. FlexSim will then use the remote CPUs for running replications. Considerations Spot Pricing Amazon offers two pricing models: On Demand and Spot Pricing. FlexSim recommends On Demand instances. Spot instances can be significantly cheaper, but they can also be shut down by Amazon and given to an On Demand user at any time, if On Demand requests exceed the number of available machines. FlexSim does not try to recover from this kind of error, and it may lead to unexpected behavior. CPU Count, RAM, and Disk Space When you pick an instance type, be sure to understand how much ram you will need. If a model requires 4 GB of RAM while running, and you use an instance with 128 cores, be sure that the instance has at least 512 GB of RAM, or don't use all 128 cores. Disk space is usually not an issue. However, if you are using the Store Data on Hard Drive option in the Statistics Collector, you will need to be sure that there is enough disk space to run the model to completion on the hard drive, multiplied by the number of cores. The amount of disk space on each instance also affects the total cost of using this service.

Greetings, I would like to share my latest FlexSim creation with the community, HUGE thank you to the flexsim overlords for creating such an awsome software https://youtu.be/WyfNS6vfLZE. G

Você tem dúvidas sobre o que é, ou como funciona o FloWorks? Este Tutorial, composto de 3 vídeos, garante um overview sobre o FloWorks: Vídeos: Introdução ao FloWorks: O que é? Como funciona? Como construir um modelo com o FloWorks: Aprenda a reproduzir um modelo simples Analisando Sistemas com o FloWorks: Observe um exemplo de análise de modelo com o FloWorks Esperamos que aproveitem mais este vídeo tutorial.

O link que segue leva a um vídeo no qual é apresentado um exemplo de Análise de Sistema utilizando-se o módulo FloWorks FlexSim. No vídeo, um modelo Portuário é analisado com o objetivo de encontrar o gargalo do Sistema. Vídeo Tutorial: FloWorks - Análise de Modelo Esperamos que aproveitem mais este vídeo tutorial.

One of the most powerful commands in FlexSim is the query() command. It allows you to run SQL-like queries on virtually any data structure in the tree. If you don't know what that means, or what SQL is, or why it would be good to have SQL in FlexSim, this article is for you. If you have general ideas, but you would like to know more, then this article is for you. This article is not about connecting FlexSim to an external database. If that is your goal, then this article is not for you. Basics - Tables Data is often arranged in a table, which very loosely means that data is organized into rows and columns. However, SQL (and therefore the query() command) was designed to work only on certain kinds of tables, that look something like this: Name Age Fur Color Scooter 6 Brown Spot 3 Brown Whizby 4 Black Notice the following points about this table: Each column has a unique name There are no row headers (although if your table in FlexSim has data in the row header, that's okay; we'll get to that later) The table is a list of things, and each row in the table represents a single thing. The columns tell you the data that is stored about each thing. In this table, each thing is a pet, and each row stores the name, age, and hair color (the columns) of each pet. If our data included another pet, it would make another row in the table. In database lingo, a row in a table is called a record. One other thing you need to know about this table is that it has a name (in this case, "Pets"). All tables in SQL have a name, and follow the format described previously. Basics - Tables In FlexSim FlexSim has several different ways you can store data. Most users employ Global Tables and Lists (found in the toolbox) to manage data in their model. More advanced users sometimes create tables or bundles on labels. Then there's the model itself, which stores your object's locations in a tree data structure. All the data about where and object is, and how big it is, is stored in the model tree. So which of these data structures meet the standards for a SQL table? As far as the query() command is concerned, all of them do. The query() command is flexible enough to handle any data structure. The trick is to imagine your data as a table. To do this, you need to be able to answer the following questions: How many rows are there? What columns will the table have? How do you get the data for each cell? Phrased another way, given a row and a column, how do you put data in the table cell? If you can answer these questions, you can use the query() command. Let's apply these questions to two data structures in FlexSim: the Global Table, and the Global List. For the Global Table, the answers are very straightforward, but follow along to get the hang of it: Q: How many rows are there? A: The number of rows in the Global Table. Q: What columns with the table have? A: The same columns as in the Global Table. Q: How do you get the data for each cell? A: Given the row and column, look up the value in the Global Table For the Global List, things get a little more interesting: Q: How many rows are there? A: The number of entries on the list. Q: What columns with the table have? A: There is a value column, and there is a column for each field on the list. Q: How do you get the data for each cell? A: Remember that each row represents an entry on the list. For the value column, I get the value that was pushed on to the list to create the entry. For each field column, I get the value of the field for that entry. For example, suppose you have this list: Then supposed that you pushed a 5, 23, -12, 7, and 2 on to this list. When you view the entries on the list, you can see the data on this list laid out as a table: Notice that the value column has the values that were pushed on the list. The field columns (Doubled, Tripled, OddOrEven, and IsNegative) are each filled with the values of that field. The query() command has built-in support for Global Tables and Global Lists. However, it is always important to remember that to the query() command, the data is laid out in a table. If the data is not laid out in a table, you can still use the query() command, but you will need to use the methods explained in the Explicitly Defined Tables section. Basics - Queries SQL stands for Structured Query Language, so we can guess that it is a language for making queries. So what is a query? In a general sense, a query is a question. In SQL, a query is a question with two parts: who are you asking, and what do you want to know? There is a general answer to both parts of the question, and this is where tables come in. The answer to the first part (who are you asking?) is always a table. A SQL query is always directed at one (or more) tables. The answer to the second second part (what do you want to know?) is usually a new table. A SQL query takes in a table, and gives back a new table. The power in SQL comes from the new tables you can make. You can make a table that is a sorted version of the old table, like sorting data in excel. For example, if you have a list of items that need to be processed, you can sort that list by the item's priority. You can also make a table that is a filtered version of the old table, meaning you only kept certain rows. For example, if certain operators can only do certain tasks, you can make sure to only include the tasks that operator can do. In fact, you can make a table that is filtered, sorted, and modified in several other ways all with one query statement. Basics - Queries in FlexSim Let's look at examples of the query() command. You can follow along using the attached model ( numberlist.fsm). That model has a script that pushes values on to the list, and then queries the list. It also puts the result into the Global Table called QueryResult. Let's look at the line of that script with the query() command: int newTableRows = query("SELECT value FROM NumberList"); This line has a lot of new material. You can see that the query() command takes at least one argument, which is the text of a SQL query. We'll discuss the query itself in a moment. The query() command takes in the SQL query, and creates an internal (and invisible) temporary table that contains the result of the SQL query. It then returns the number of rows in the temporary table. Since you can't readily see the new table made by the query() command, you may want to copy the temporary table to a global table, using the dumpquery() command, as the script does: dumpquery(reftable("QueryResult"), 1); The temporary table lasts until you run the query() command again. Now for the query itself. You can see two parts of this query. The question "What do you want to know?" is answered by the SELECT statement. The question "Who are you asking?" is answered by the FROM clause. In this case, the FROM clause just lists one table, the NumberList. Recall that even though the NumberList isn't technically a table, you can visualize it's data as a table, which is what the query() command does (you can always right-click the NumberList in the Toolbox and choose the "View Entries" option to see the data as a table). In this example, the SELECT statement lists a column name. This means that the resulting table will have 1 column, because only one is required by the SELECT statement. When you run the script, it will put the result of the query in the QueryResult table: Notice that this table has only one column, the value column. If you look at the NumberList entries, you will see that this column is identical to the value column in that table. Examples To give you a general idea of what you can do with queries, these examples show a few common tasks. The examples all query the NumberList, which can be visualized as the following table: Selecting Multiple Columns SELECT value, IsNegative FROM NumberList Filtering SELECT value, IsNegative FROM NumberList WHERE IsNegative == 1 SELECT value, OddOrEven FROM NumberList WHERE value >= 5 Sorting SELECT value, Doubled FROM NumberList WHERE value >= 5 ORDER BY value Advanced - Callback Values It is often necessary to create a dynamic query. In FlexSim (not in regular SQL), you can do this by replacing a value in a query with a $n variable. This variable corresponds to additional parameters in the query() command. If you specify $1, then the value in the query will be the first additional parameter in the query() command, $2 will correspond to the second, and so on, up to $9. For example: query("SELECT Col1, Col2 FROM MyTable WHERE Col1 > $1 AND Col2 < $2", 10, 20) This query creates a table with Col1 and Col2, but only the rows where Col1 is greater than 10, and Col2 is less than 20. It is possible to include the values 10 and 20 in the query text. However, the $ variables allows you to easily change the value in the query, and replace them with more complicated expressions. Note that using callback values in a query where the FROM value is a Global List is not supported before versions 16.0.6/16.2.1. Advanced - Explicitly Defined Tables and the $iter() command Another FlexSim-specific feature of the query() command is the ability to explicitly define a table. This means you can query data that isn't arranged in the table format. In order to do so, you will need to answer the questions posed above, but shown again here: How many rows are there? What columns will the table have? How do you get the data for each cell? Once these questions are answered, the query() command can treat your data like a table. Each of the following subsections will answer these questions in detail. Defining the Number of Rows Let's start with a simple example: query("SELECT 1 FROM $1", 5) Notice that we are selecting from a number, not a Global Table or Global List. When the query() command sees a number instead of a table in the from statement, it assumes that you are querying a table with that number of rows. The statement above yields the following table (after a call to the dumpquery() command): This table has five rows, because $1 (the first additional parameter) evaluates to 5. All the rows have the value 1 because SELECT can handle expressions as well as column names (or expressions involving column names). Note that defining a table will only work if you use callback variables. A from statement like FROM 5 is not valid. To determine the number of rows in the table, use callback variables in the FROM statement. Defining the Columns To define the columns of your table, use the SELECT statement. Each expression in the SELECT statement will result in a column. When you are explicitly defining a table, you will often use callback variables as columns. This is where we can take advantage of the query() command. The query() command is very different from most other commands, because most commands only evaluate their parameters once. The query() command, however, can evaluate its parameters as many times as it needs to. Let's look at another example: query("SELECT $2 FROM $1", 5, normal(0, 1)) The result of this query is the following table: Notice that the second parameter, corresponding to $2, was re-evaluated as many times as the query needed it. Getting the Value in Each Cell Finally, we need a way to access the correct value for a given row and column. To do that, we need a way to know which row we are currently dealing with. This is where the $iter() command comes in. For example: query("SELECT $2 FROM $1", 5, $iter(1)) The $iter() command accepts an argument. That argument should match the value of the callback variable, e.g. if you used FROM $1, then the $iter() command should take a one as an argument. The $iter() command is linked to the query() command, so that its return value depends on which row is being evaluated. The above query results in the following table: Notice that $iter(1) returned the row number for each row. Now we can begin to query the tree, as if it were a table. Let's start with a simple example. Suppose you wanted a table of all the names of the first-level subnodes of the model tree. Let's start by answering the three questions: Q: How many rows are there? A: The number of first-level subnodes of the model Q: What columns with the table have? A: A column for the name of each of those subnodes Q: How do you get the data for each cell? A: Given the row number, get the name of that object Let's translate those answers into a query. First, we need to query something that isn't in table format already, so we know we need to use callback variables in the FROM statement: query("SELECT ... FROM $1", ..., ...) Next, we need a column for the names of all the subnodes, so we add one to the select statement: query("SELECT $2 FROM $1", ..., ...) Next, we need to answer the question of how many rows there are. Since this is the number of subnodes, you can write that as content(model()): query("SELECT $2 FROM $1", content(model()), ...) Finally, we need a way to get the name of each of the subnodes. We know that $iter(1) will give us the values from 1 to content(model()), so we can use those values in the rank() command, and we can pass the result of the rank() command to the getname() command: query("SELECT $2 FROM $1", content(model()), getname(rank(model(), $iter(1))) ) After dragging in a few random objects into the model, you can run that query. If you use the dumpquery() command, you can get a table like this: In the previous example, we used the value from $iter(1), and passed it in to rank(), and from rank() to getname(), all to get the name of the nth subnode of the model. Since querying a node in this way is fairly common, the query() command also allows you to specify a node as a table, rather than a number. In this case, $iter(1) will iterate over all the subnodes of $1, rather than the values 1 to $1. This makes our query much simpler: query("SELECT $2 FROM $1", model(), getname($iter(1))) Advanced - Flattening Data There is another option for what you can query in a FROM statement. If you use a callback variable (like $1) as your table, then you can use flattening syntax, shown in the following example: query("SELECT $3, $4 FROM $1x$2", 3, 2, $iter(1), $iter(2)) This query makes the following table: Notice that for every possible value of $1, there is a row for every possible value of $2. That is the purpose of the x (called the flattening operator); it ensures that there is a row for each permutation of all the callback variables involved. A query containing FROM $1x$2x$3x$4 would produce a table with enough rows for each unique combination of $iter(1), $iter(2), $iter(3), and $iter(4), and then execute the query on that table. The user manual contains an excellent example of when flattening syntax might be helpful, shown in the help manual in Miscellaneous Concepts > SQL Queries > Example.

O link que segue leva a um vídeo no qual é apresentado um exemplo de modelagem utilizando-se o módulo FloWorks FlexSim. No vídeo, modela-se um processo de fabricação de Suco de Laranja, desde sua extração até seu envase. Recursos do FloWorks utilizados no vídeo: ItemToFlow: Extração do suco FlowPipe: Transporte do fluido FlowTank: Armazenamento FlowMixer: Processo de mistura de conservantes Vídeo Tutorial: FloWorks Exemplo de Modelagem Esperamos que aproveitem mais este vídeo tutorial.