A number of questions on the forum involve racks being service by a combination of shuttles and elevators. There are solutions involving network, Astar and AGV navigators, but for this example we’re just going to use TaskExecuter FlowItems and conveyors. The elevator system in particular, as described to me, seemed it would benefit from the flexibility conveyors offer – particularly spacing options, and the possibility of having dog/power-and-free based travel. For the pick face we can just use the slot and item locations to give the task executers travel command, and we can use kinematics for loading and unloading tasks. This removes the need for network nodes or control points at each location and allows fine positioning of a ‘two spot’ shuttle in front of the slot. The system has been put into a container to represent the cell/aisle and it is this object that is the instance member of the ShuttleSystemProcess process flow. The cell is designed to be duplicated with each cell becoming a new member instance of the single process flow. It comprises two racks, two elevators (conveyor loops), and a shuttle return queue (also a conveyor but with no roller visual). The system assumes that by the time an inbound item arrives at the pickup position it has been assigned a slot in one of the racks in the cell – so you should assign a slot in the normal way before it reaches a shuttle. You can additionally request items for picking out of the racks by pushing the item to the global list ItemsToPick. Currently each shuttle will store and/or pick one item in one trip with a dedicated position for each. When doing both in a single trip, the order in which this happens will depend where along the level the slots are located. In the event that there are no remaining tasks but items still need to exit the cell, the shuttles at the front of the queue will be asked to circulate empty through the system, thereby allowing the outbound items to advance to the exit position. The number of shuttles in the system is a process flow variable. In the example system there are elevators at each end of the rack(s) with a number of carriers to transport shuttles to the levels. Both elevators have a process flow variable for the number of carriers to be generated. Shuttles are not allowed to pick from the same level at the same time but in order to keep the up-elevator moving the carriers can unload the shuttle to the level even if another is active on that level. The shuttles only travel along the face of the rack in one direction towards the down-elevator and once are collected by a carrier the next shuttle on that level can start its operations. It is possible to run the system with only one rack should you wish to view the operations without the second rack obscuring your view. Since different applications will use different rack dimensions the cell has a label method called “configureToRack” which will align the conveyors and decision points to Rack1 based on the level heights and size of the racks that the user has set for Rack1. There may be some limits to very small sizes when the conveyor decision points overlap. The second rack will be configured during this method call to mirror Rack1. Here’s an example invocation of this method for an instance of the cell: Model.find("RackShuttleSystem").as(Object).configureToRack The shuttles need levels to be the same height along the length of the rack. Some effort was made to configure the system based on the shuttle and carrier sizes, so you can try adjusting those to suit your needs and hopefully the alignment will work as needed. The elevator conveyor and shuttle speeds are not set by the alignment method so you can edit those in the usual way. This is an example for both learning and perhaps as a starting point for any project should you find the approach suits your application, modelling style and skills. ShuttleLiftAndRack.fsm Time taken: 1 day to build the working model - plus another to work around holes api for auto-alignment code. 17Nov Updated: to initalize shuttles at the load point (via fast entry) added shuttleQheight label for use to set the returnQ height in the cell (used by the alignment method) added a process flow variable 'shuttleLoadTime' for the time to un/load items. aligned shuttle kinematics to the speed and acceleration of the TE FlowItem.

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:

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

One of the most powerful features of Process Flow is the ability to easily define a Task Sequence. However, many real-life situations require the coordination of multiple workers and machines to do a single task. This article demonstrates one approach you can use with Process Flow to coordinate multiple Task Executers, or in other words, to create a coordinated task sequence. This article talks about an example model (handoff.fsm). It might be easiest to open that model, watch it run, and perhaps read this article with the model open. The Example Scenario Here is a screenshot of the demo model used in this article: Items enter the system on the left. The yellow operator must carry each item to the queue in the middle, and then wait for the purple operator to arrive. Once the two operators are both at the middle queue, then the yellow operator can unload the box, and the purple operator can take it. After this point, the yellow operator is free to load another item from the left queue. The purple operator takes the item, waits for a while, and then puts the item in the sink on the right. The interesting part of this model is the hand off. The yellow operator must wait for the purple operator, and vice versa. This is the synchronization point, and it requires coordination of both operators. The approach used in this model allows you to add more operators to the yellow side, and more to the purple side. But it still maintains that a yellow operator must wait for a purple operator before unloading the box. The Example Model In addition to to the 3D layout shown previously, there are 5 process flows in the example model. The first is a general flow, and defines the logic for each task. The second is a Task Executor flow, and defines the logic for the yellow operator. The third is also a Task Executor flow, and defines the logic for the purple operator. The remaining two flows are synchronization flows, for synchronizing between the other three flows. Synchronizing on a Task The basic approach in this model uses the Synchronize activity. This activity waits for one token from each incoming connector, before it allows any of the tokens to move on. Here is the Yellow Purple Sync flow (a global Sub Flow) from the example model: The flows for the yellow and purple operators each use the Run Sub Flow activity to send a token to this flow, to their respective start activities (you can use the sampler on the Run Sub Flow activity to sample a specific start activity in a sub flow). This is what allows both the yellow and purple operators to wait for each other. However, it is important that the yellow and purple operators are both doing the same task. In this model, there is a token that represents each item that needs to be moved. Both operators get a reference to this task token. The Synchronize activity is set up to partition by that Task token. That means that a yellow and purple operators must both call this sub flow with the same task token, ensuring that each task has its own synchronization. In the example model, this kind of synchronization happens between operators, and it happens between each task and an operator. Basically, the task must wait for the operator to finish that operator's part. The Task Flow A task token is created every time an item enters the first queue. The tasks flow puts that task on both the Yellow and Purple lists. In both cases, the task token does not wait to be pulled, but keeps itself on the list. Then, the task token waits for a yellow operator to finish with it, and then for the purple operator to finish with it. There is a zone in this flow, but its only purpose it to gather statistics for how long the whole task took. The Yellow and Purple Flows These flows are easiest to understand when viewed side by side: Recall that each task is put on both the Yellow and Purple lists at the exact same model time. The yellow operator waits to get a task (at the Get Task activity). Then the operator travels to the first queue, gets the item, and travels to the second queue. At this point, the yellow operator waits. At the same time, the purple operator is also waiting for the task. The purple operator just has to travel to the second queue before waiting for the yellow. Once the yellow operator arrives, the purple operator also has to wait for the yellow operator to unload the box. On the yellow side, once the purple operator arrives, the yellow operator unloads the box, and then synchronizes with the purple operator, allowing the purple operator to load the box. Summary The purpose of this article is to show one method for synchronizing token in separate flows. That method is as follows: Have a token for each task. As each task executor (or fixed resource) needs to synchronize, they each use a Run Sub Flow activity, putting the token in a specific Start activity. The Sub Flow (a global Sub Flow) has a synchronize activity, that requires a token from each participant for that task before releasing the tokens. This is certainly not the only way to create this model. However, there are some advantages: By forcing the task to synchronize, you can gather stats on how long each phase of the task took, as well as how long the complete task took. You can add more yellow or purple operators by copy/paste. They simply follow their own logic Each set of logic is separated; tasks, yellow operators, and purple operators each have their own flows, making each one much simpler. The exact approach used in the example model will not work exactly as it is for each model. However, you can apply the general principles, and adapt them to your own situation.

O artigo "Using Fixed Resource Process Flows" que descreve algumas funções do Fixed Resource Process Flow tem um modelo de exemplo. O exemplo está em anexo e mostra como podemos utilizar do Fixed Resource Process Flow para multiplicar lógicas de fluxo e de trabalho. Esse representa uma área de despacho de produtos onde o produto vem de um estoque transportado pelo empacotador (Packer) e é preparado em uma paleteadora. Depois o produto é levado em lotes pelo empacotador para uma estação e dali ele vai ser despachado pelo carregador (Shipper). A função do Fixed Resource Process Flow facilita a replicação da lógica dessas paleteadoras e dos funcionários envolvidos. Alguns teste como se aumentássemos o número de recursos conseguiríamos suprir melhor a demanda podem ser realizados por esse recurso. Nesse artigo descreveremos o passo a passo desse modelo. A descrição segue no arquivo exemplo FR Flow em anexo.

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.

I'm attaching a user library and an example model that contains process flows for two types of merge controller. The main advantage of both of these is that the release strategy is consolidated into a single list pull query, so that you can have incredible flexibility in defining custom release strategies just by adjusting a single query. Several query examples are included and explained. The DPClearingMergeController uses the standard merge controller mechanism that utilizes decision points and the merge controller's lane clear table to determine when slug lanes are clear for release. Alternatively, the FixedGapMergeController customizes lane release times for an optimal target gap between released slugs. This, combined with a good release strategy, can maximize total merge throughput. Here's a view of the fixed gap merge control running. Notice how it tries to time the releases so the slugs line up pretty close on the take away lane (the target gap here is 3 feet). And below is a screenshot of the fixed gap merge control process flow. mergecontrollerprocessflows.zip This model and library were created in 16.1.0.

[ FlexSim 16.1.0 ] Attached is an example model that uses both of the new template process flows for AGV and AGV elevator control, available in FlexSim 2016 Update 1. Thanks @Katharina Albert (I believe), who provided the seed model, which I adjusted/extended as I implemented these process flows. This model enumerates many of the control point connections and path configurations you might use in an AGV model when using these template process flows.