Attached is an example model and user library comprising commands to return an array of objects whose bounding boxes intersect, and a Collision Detection object to drop into your model. The Collision Detection has a ticker interval label to adjust the frequency of checks and will switch the colliding objects to selected. It looks for two groups - "Obstacles" containing static objects in the scene (which may be overlapping and not recorded as collisions) and "Colliders" which are the objects navigating the scene and should be checked for intersecting bounding boxes. In the example model I'm adding the flowitem when it is created using Group("Colliders").addMember(item) The detector code is on its FlexScript label, 'analyseScene', which is first scheduled to run by the object's reset trigger. collisionDetection3.fsm BBCollisionDetection2.fsl

If you have a contiguous conveyor network you can just route items using Conveyor.sendItem() and FlexSim will guide the item to the destination, passing through inline and side transfers as required for the shortest path. If between some conveyors you use exit and entry transfers, perhaps to easily add elevators and shuttles as transports between them - then you'll normally be faced with adding logic to figure out which exit transfer to go to and which port to take from that transfer - and in a large model that logic can be extensive and hard to maintain. The attached model and library provides commands for automated routing through multiple conveyor sub-sections connected through exit/entry transfers, to conveyor points and to connected fixed resources. This means that you may no longer have to write sendTo code with case statements on each exitTransfer to determine which port an item should exit through – nor possibly need to have decision points with case logic to decide the destination for Conveyor.sendItem(). In the example model three sources create items with random destinations which are routed through the conveyor system, transfers and port automatically to arrive at the correct destinations – some of the ports having transport to perform the move. To make this work in any model you should load the user library which will auto-install a set of user commands and a General Process Flow. The first step is to run the user command ‘createAllTravelMaps()’ which will calculate all the reachable destinations (decision points, stations, pes, attached fixed resources and transfers) from all the conveyor points and entry/exit transfers) along with estimates of the conveytime (from the conveyor class). This information consolidated to create the shortest routes and is stored in a label ‘travelMap’ on each decision point, station, pe and transfer. To make use of the travelMap data there are three additional user commands supplied that are intended to be used directly by the modeller: getNextConveyPoint(thispoint, destination) – returns the next point to send an item to from this point in order to ultimately reach the destination. getConveyExitPort(exitTransfer, destination) – returns the port through which an item should exit the exitTransfer in order to reach the destination. getConveyItemsNextConveyPoint(item, destination) – returns the next point to which an item should travel to reach the destination from its current position on a conveyor. The simple process flow in the example and library is set to listen to the Group members of EntryTransfers and ExitTransfers in order to lookup the ‘destination’ label and either sends the item to the next point or in the case of the exit transfers, overrides the sendTo port with the value from the map. I’ve added some documentation to the user commands which you can access easily via the command helper: ConveyorTravelMaps_0.3.fsl ConveyorTravelMapExample.fsm You may find createTravelMaps() takes a while which is why a progress bar has been added. You may not need all points to be evaluated exhaustively so the option to pass in a flag indicating to only start evaluation from Entry Transfers is given, which will create somewhat incomplete maps for intermediate points. A future refinement would be to account for transport time from exit transfers either by recording the times or providing port list with the expected times. Clearly if you make changes to your transfer positions or conveyor layout you should rerun createAllTravelMaps.

This article describes an example of Reinforcement Learning being used to solve scheduling problems. See the model and python files in the attached zip file. SchedulingRL.zip Problem Description This model represents a generic sheet metal processing plant. There are four machines in series. Each job requires time on all four machines. Jobs come in batches of 10. A poor sequence of jobs will cause blocking between items, lowering throughput. If the time between batches is long, such as a shift or a day, you could use the optimizer to determine the best sequence. If the time between batches is short, however, the optimizer may not be feasible. For real sequencing problems, the time to find a good sequence can be anywhere from 5 minutes to an hour, or even longer. This makes it impractical for high-velocity situations. The attached model requests a decision every time the first machine in the series is available. The only action is an index for the Nth available job. So the decision can be interpreted as "which job should I do next?" Solution The general solution is to use reinforcement learning. However, this problem required customized python scripts: The model uses custom parameters for observations. This allows arbitrary values for observations. The model uses a custom observation space. The observations include a table of the required times at each station for the remaining jobs. They also include an array of the in-progress jobs and their predicted remaining times. By using a Dict space, the python scripts can combine all the observations into a single space. The model uses an Action Mask. An Action Mask is a binary array with one value per value of the action. This tells the RL algorithm about invalid options. The python scripts require the sb3-contrib package. Use pip install sb3-contrib to install it. Results After training for 500k time-steps, the agent learns to choose jobs moderately well. If you run the inference script, you can use the experimenter to compare a random policy to a trained agent:

ContinuousUtilization_4.fsm In the attached model, you'll find a Utilization vs Time chart driven by a Statistics Collector and a Process Flow. This article is an alternative approach to the chart shown here: https://answers.flexsim.com/content/kbentry/158947/utilization-vs-time-statistics-collector.html Thanks @Felix Möhlmann for putting that article together. The concept in this model is very similar, but this is a different approach. There are pros and cons to each method. This method offers some performance improvements at the cost of writing more FlexScript code. This method also doesn't handle Warmup time, where the other method does. The general idea in this model is to keep a history of state changes for the previous hour (or other time interval). The history adds new info as states continue to change and drops old info as it "expires" by being older that the time interval. I chose to use a Bundle (stored on a token label) for the history. Bundles are optimized to add data to the end. If a bundle is paged (they are by default), then they are also optimized to remove data from the beginning. [Begin Technical Discussion - TLDR; Bundles are fast at removing the first row and adding new rows] A paged bundle keeps blocks of memory (pages) for a certain number of rows. If a page fills up, it allocates another page. It keeps track of the location in the page for the next entry. Similarly, it keeps track of the location of the first entry on the start page. If you remove the first row, that start location is moved forward on the page. If the start location gets to the end of a page, that page is dropped and the start location moves to the next page. [End Technical Discussion] The Process Flow maintains the history table. Whenever the object's state changes, the Process Flow adds a new entry to the history table. This part is straightforward. The tricky part is properly "expiring" the data. To do this, a second set of tokens wait for the last of the oldest data to expire. When the oldest set of data should expire, the tokens remove any rows that are too old and then wait for the next oldest row. If there are no expired rows, the tokens wait for one "time interval" and then check again. This is because if there is no expired data, data can't expire for at least one time interval. In this way, the history table is always kept up to date. The Statistics Collector is configured to post-process the history table. It calculates the total utilization, including the state that the object is currently in, as well as the state being "phased out" from the history table. The Statistics Collector can do this calculation at any point in the model. So the sampling interval (called Resolution in the model) is independent from the time window.

Sometimes data exists in Google Sheets that needs to be brought in to FlexSim. There are multiple ways to do this, discussed in this article. Copy and Paste This is the easiest method to get data from Google Sheets into FlexSim. Here's how it works: Open the desired sheet in your browser Click the top-left corner to select everything. Copy the data (use ctrl-C) Open FlexSim Create a Global Table if you haven't already Ensure the number of rows and columns in the Global Table is large enough to hold the pasted data. Click on the column header for the first row in the Global Table. Paste the data (use ctrl-V) Pros: Quick, easy Cons: Need to resize the global table correctly beforehand, repeat entire process if data changes. Export/Import via CSV This is also any easy method to get data. Here are the steps: Download your sheet as a csv file. In FlexSim, use the importtable() command to dump the csv into the global table. For example: importtable(Table("GlobalTable1"), "data.csv", 1) You could add this code to your model's OnReset trigger if desired. Pros: Quick, table sized to csv data automatically Cons: Repeat downloading csv if the data changes. Export/Import via XLSX You can also download a google spreadsheet as an Excel file. Then you can use the Excel importer as normal. Pros: Quick, table sized to data automatically, many options for configuring Cons: Repeat downloading xlsx file if the data changes Import via Python This method is more advanced and requires some configuration for the model and your Google account. Once complete, however, changes can be pulled in automatically without any manual steps. Follow the Sheets quickstart for python found here: https://developers.google.com/sheets/api/quickstart/python Following this guide walk you through creating a Google Cloud Project and creating credentials for that project. In addition, consider using this modified python file instead. This file creates a get_values method that the model can call, and that method is also called from main(), so it's easy to test in a python debugger: import os.path from google.auth.transport.requests import Request from google.oauth2.credentials import Credentials from google_auth_oauthlib.flow import InstalledAppFlow from googleapiclient.discovery import build from googleapiclient.errors import HttpError # If modifying these scopes, delete the file token.json. SCOPES = ["https://www.googleapis.com/auth/spreadsheets.readonly"] # The ID and range of a sample spreadsheet. SAMPLE_SPREADSHEET_ID = "----- add your sheet's ID here -------------" SAMPLE_RANGE_NAME = "A1:B" def get_values(): """Shows basic usage of the Sheets API. Prints values from a sample spreadsheet. """ creds = None # The file token.json stores the user's access and refresh tokens, and is # created automatically when the authorization flow completes for the first # time. if os.path.exists("token.json"): creds = Credentials.from_authorized_user_file("token.json", SCOPES) # If there are no (valid) credentials available, let the user log in. if not creds or not creds.valid: if creds and creds.expired and creds.refresh_token: creds.refresh(Request()) else: flow = InstalledAppFlow.from_client_secrets_file( "credentials.json", SCOPES ) creds = flow.run_local_server(port=0) # Save the credentials for the next run with open("token.json", "w") as token: token.write(creds.to_json()) try: service = build("sheets", "v4", credentials=creds) # Call the Sheets API sheet = service.spreadsheets() result = ( sheet.values() .get(spreadsheetId=SAMPLE_SPREADSHEET_ID, range=SAMPLE_RANGE_NAME, valueRenderOption="UNFORMATTED_VALUE") .execute() ) values = result.get("values", []) return values except HttpError as err: return [] def main(): values = get_values() if not values: print("No data found.") return for row in values: print(row) if __name__ == "__main__": main() Save the above script next to your model. Create a user command in your model. Format the user command for python and enter the file name and method name. It might look something like this: /**external python: */ /**/"sheets"/**/ /** \nfunction name:*/ /**/"get_values"/**/ The return type of the command should be var which means any Variant type. Use code like the following to clone the data to a global table: Array values = getValues(); // call the user command. Array colHeaders = values.shift(); for (int i = 1; i <= values.length; i++) { Array row = values; row[0] = nullvar; } Table(values).cloneTo(Table("GlobalTable1")); Add the above code to a reset trigger. Pros: automatic once complete, easy to keep data up-to-date Cons: requires complicated setup, some python coding. The script could be adjusted to download additional ranges, and then return all data at once, but that requires some code ability. Import via HTTPS Google recommends you use a client library to access its APIs. However, it is entirely possible to use HTTPS requests instead. This could all be done from FlexScript, with no additional installations required. Pros: done all from FlexScript, no extra installs Cons: very technical Conclusion There are several ways to extract data from Google Sheets into FlexSim. Each has pros and cons. Choose the one that best fits your circumstances. Good luck!

The attached model contains a basicTE to mimic some operations of a Tower Crane. You should be able to use it like any other task executer. Labels on the crane allow the speeds and operating heights to be altered. To change the jib/beam length use the label parameter and it will apply at reset. Similarly, to change the height for now just change the tower height and press reset to have the rest attached at the correct height. TowerCrane_basicTEexample.fsm Update: Added a user library that will scale the crane based on the model units. Also changed some labels so that rotational speed is specified there and the jib/beam now uses the object properties for max speed and acceleration. TowerCrane.fsl

This model contains example subflows for having a team of travellers travelling and loading/unloading. The RunSubflow for each creates that subflow's expected labels: team, teamItem, teamDesintation. Currently the offset locations are just a multiple of the x size of the first traveller. They are determined from the pick offset and place offset functions. Further work could add traveller un/loading and wait states and combine travel and unload tasks. TeamSubflows.fsl TeamSubflows.fsm Update 24Oct.'24 : Corrected incorrect label name in TeamTravel subflow should be looking for 'team', not 'travellers'

This model and library will allow you to produce a heat map of anything moving in the model - including AGVs and Flowitems. To add this to a model is simply a matter of : 1) Load the attached user library 2) Add objects to the group HeatMapMembers 3) Drop a heat map object (cylinder) into the model - reset and run. With this updated version you can now you can now have multiple mapper objects in the same model showing different groups - made easier by the addition of a 'groupName' label on the mapper. You can easily change the height at which the map is draw using the 'zdraw' label and alter the sampling interval and grid size using the 'heatInterval' and 'resolution' labels. The resolution is the number of divisions per model length unit. In the example model set to metres, a value of 2 gives 4 divisions per square metre. Currently non-flowitems are set to ignore time when the object is in an idle state. HeatMapAnything.fsl HeatMapAnything.fsm

The attached model contains functionality to depict the item flow as a 3D map using a FlowMapper3D Object (cylinder) and an associated Object Process Flow. Additionally a 'kpi' label on the object gives an indication of layout performance to which you can link and observe as you interact/experiment on the layout. To set this up in your model you'll need to add a Group of objects whose entry events will be used by the mapper - calling that Group "FlowMapperObjects". Then you'll need to add a ColorPalette called "HeatPalette". Finally you'll want to copy the FlowMapper3D object and the FlowMapperProcess to your model. Note that there is a boolean label 'showPercents' on the FlowMapper3D object to tell it whether to show percentage text or the number of flowitems for each location pair. 3DFlowMapper.fsm

Hi everyone, recently @Arun Kr posted this idea to add a utilization vs. time chart to the available chart types in FlexSim. I had previously build a relatively easy to set up Statistics Collector for use in our models. I have since cleaned up the design a bit and thought to post it here, since this seems to be a commonly desired feature. utilization_vs_time_collector_24_0_fm.fsm All necessary setup is done through labels of the collector. The first three are actually identical to labels found on the default Statistics Collector behind a state bar chart. - Objects should point at a group that contains all objects the collector should track the utilization for. - StateTable is a reference to the state table that will be used to determine which state counts as 'utilized'. - StateProfile is the rank of the state profile that should be read on the linked objects (0 for default state profile). - MeasureInterval is the time frame (in model units) over which the collector will take the average of the utilization. - NumSubIntervals determines how often that measurement is actually taken. In the example image above (and the attached model) the collector measures the average utilization over the last 3600s 12 times within that interval of every 300s. Each meausurement still denotes the utilization over the complete "MeasureInterval". The graph on the left takes a measurement every 5 minutes, the one on the right every 60 minutes. Each point on both graphs represents the average utilization over the previous hour from that time point in time. - StoredTimeMap is used to allow the collector to correctly function past a warmup time, by storing the total utilized value of each object up to that point. This should no be manually changed. Since this last label has to be automatically reset, remember to save any changes made to the other labels by hitting "Apply". The collector works by keeping an array of 'total utilized time' value for each object as row labels. Whenever a measurement is taken, the current value is added to the array and the oldest one is discarded. The difference between the newest and oldest value is used to calculate the average utilization over the measurement interval. The "NumSubIntervals" label essentially just controls how many entries are kept in that array. To copy the collector into another model you can create a fresh collector in the target model. Then copy the node of this collector from the tree of the attached model and paste it over the node of the fresh collector. I hope this can help to speed up the modeling process for some people (at least until a chart like this is hopefully implemented in FlexSim) or serve as inspiration for how one can use the Statistics Collector. I might update the post with a user library version if I get to creating it (and if there is demand for it). Best regards Felix Edit: Added user library with the collector as a dragable icon to the attached files. Edit2: I noticed a bug while using the collector. Having the tracked objects enter states that are marked as "excluded" in the state table would lead to incorrect utilization values (possibly even below 0 or above 100%). Replaced the library with an updated version that fixes this. Edit3: I fixed another bug that resulted in a wrong utilization value for the first measurement after the warmup time if the object spent time in an excluded state prior to the warmup. utilization-vs-time-collector-library-20250212.fsl

Attached is an example model that shows how you can use reversible conveyors for routing/sorting of items. ReversibleRoutingConveyor.fsm Traditionally we've sort of warned against using reversible conveyors for purposes other than accumulation buffers. The main reason I've been hesitant to promote alternative uses is that the routing system for conveyors is, and will continue to be, static. In other words, the path finding algorithm to send an item through a network of conveyors to a destination point does not change when one or more conveyors in the system is reversed. Put another way, "for routing purposes, ..., the conveyor is always assumed to be conveying in its original direction." This naturally makes using reversible conveyors for routing more complex. However, as long as you can still work within those constraints, you can actually get the desired outcome. The attached model does this by 'shortening' the routing decision so that it can always route onto conveyors in their forward direction. The attached model sorts items by color by moving them between two conveyor via a reversible conveyor that conveys in either direction as needed. In order to still work within the 'static routing' rule, I split the reversible conveyor into two separate conveyors that are directed into each other. This way, I can route items onto the reversible section by referencing a conveyor whose primary forward direction always diverts from the line a given item is on. The critical element is that I have to always make sure that when one conveyor is moving forward, the other is reversed, and vice versa. I also have to implement some mutual exclusion, blocking some items so they aren't sent to conveyors in opposite directions. This all is done in the process flow. I honestly don't know how close this example is to a real life situation. We've just received some requests for a reversible conveyor that can do more than just accumulation buffers, and routing/sorting is the main alternate example I can think of. This is one way you can achieve such a result.

FlexSim's Webserver is a query-driven manager and communication interface for FlexSim. It allows you to run FlexSim models through a web browser like Google Chrome, FireFox, Internet Explorer, etc. Since the FlexSim Web Server is a basic service to allow FlexSim to be served to a browser, you may decide you want a way to proxy to this service through a full service web server that you can control security and authentication through. This guide will walk you through proxying to the FlexSim Web Server through Apache web server. Install the FlexSim Web Server Program Download and install the FlexSim Web Server from https://account.flexsim.com Edit C:\Program Files (x86)\FlexSim Web Server\flexsim webserver configuration.txt Change the port from 80 to 8080 Start the FlexSim Web Server by double clicking flexsimserver.bat Test the server by going to http://127.0.01:8080 It should look like this: Install Apache Web Server for Windows Download Apache x64 from https://www.apachehaus.com/cgi-bin/download.plx Extract the httpd-<version>.zip file you have downloaded Go into the httpd-<version> folder you have extracted and copy the Apache24 (or Apache25) folder to C:\ Install Apache Dependencies Make sure you have the Microsoft Visual C++ 2008 SP1 package installed. You can get it here: https://www.microsoft.com/en-US/download/details.aspx?id=26368 Download the vcredist_x64.exe package and run the installation Configure Apache Open C:\Apache24\conf\httpd.conf in a text editor Look for the following lines in this configuration file and remove the # character #LoadModule proxy_module modules/mod_proxy.so #LoadModule proxy_http_module modules/mod_proxy_http.so #LoadModule proxy_wstunnel_module modules/mod_proxy_wstunnel.so Those modules should now look like this: LoadModule proxy_module modules/mod_proxy.so LoadModule proxy_http_module modules/mod_proxy_http.so LoadModule proxy_wstunnel_module modules/mod_proxy_wstunnel.so At the bottom of the httpd.conf file, add these 3 lines and save the file: ProxyPass / http://127.0.0.1:8080/ ProxyPassReverse / http://127.0.0.1:8080/ AllowEncodedSlashes On Run Apache Web Server In a file explorer or CMD line prompt, browse to the C:\Apache24\bin folder and run httpd.exe Now that you have the FlexSim Web Server proxied through Apache, you may decide you want to configure Apache to handle security, authentication and customization. Since this is out of the scope of this guide, you can find details on the Internet that can guide you to setting these customizations up. A few resources you may consider: https://community.apachefriends.org/f/ https://stackoverflow.com

FlexSim's Webserver is a query-driven manager and communication interface for FlexSim. It allows you to run FlexSim models through a web browser like Google Chrome, FireFox, Internet Explorer, etc. Since the FlexSim Web Server is a basic service to allow FlexSim to be served to a browser, you may decide you want a way to proxy to this service through a full service web server that you can control security and authentication through. This guide will walk you through proxying to the FlexSim Web Server through Nginx web server. Install the FlexSim Web Server Program Download and install the FlexSim Web Server from https://account.flexsim.com Edit C:\Program Files (x86)\FlexSim Web Server\flexsim webserver configuration.txt Change the port from 80 to 8080 Start the FlexSim Web Server by double clicking flexsimserver.bat Test the server by going to http://127.0.01:8080 It should look like this: Install Nginx Reverse Proxy From a browser, visit http://nginx.org/en/download.html Download latest stable release for Windows Extract the downloaded nginx-<version>.zip Rename the unzipped nginx-<version> folder to nginx Copy the nginx folder to C:\ Double click the C:\nginx\nginx.exe file to launch Nginx Test Nginx by going to http://127.0.0.1 It should look like this: Configure Nginx to proxy to the FlexSim Web Server Open C:\nginx\conf\nginx.conf in a text editor Find the section that says: location / {    root html;    index index.html index.htm; } Edit out the root and index directives and add a proxy_pass directive so it appears like this: location / {       proxy_pass http://127.0.0.1:8080;    #root html;    #index index.html index.htm; } Save the nginx.conf file Reload Nginx to Apply the Changes Open a command line window by pressing Windows+R to open "Run" box. Type "cmd" and then click "OK" From the command line windows, type the following to change to the nginx directory: C:\nginx>cd C:\nginx and press enter Now, type the following to reload Nginx: C:\nginx>nginx -s reload Test the FlexSim Web Server Being Proxied by Nginx From a browser window again go to http://127.0.0.1 You should now see the FlexSim Web Server interface proxied through Nginx Now that you have the FlexSim Web Server proxied through Nginx, you may decide you want to configure Nginx to handle security, authentication and customization. Since this is out of the scope of this guide, you can find details on the Internet that can guide you to setting these customizations up. A few resources you may consider: https://forum.nginx.org https://stackoverflow.com

In the attached model we use a Timetable and two MTBF/MTTR objects to define Schedule Loss, Availability Loss (breakdowns) and an element of Performance loss due to short stops (state Down). The processor sends 'bad' items to port 2 based on the send to percentage which account for QualityLosses. The processor's 'best' processing time per part (5 seconds) is stored as a label, while the processing time itself is a triangular distribution with the minumum as 5 seconds - so it also contributes to performance loss. When the Type of the item changes a setup time occurs which is the final contributor to performance loss. Two state profiles were added to the processor - one to track production time and another for availability. An object process flow on the processor detects production profile state changes (between On and off shift) and regular Flexsim state changes and determines the availability state that should prevail. A user command getOEEstat is used to access the values which it calculates on demand and stores in a label on the processor called statsMap. The syntax for this command is: getOEEstat(myMachine,"OEE") The list of stats: "ScheduleLoss","AvailabilityLoss","PerformanceLoss","QualityLoss","IdealProdTime","AvailabilityRatio", "QualityRatio","PerformanceRatio", "IdealProdTime", "RunTime", "OEE", "TEE". A group was used to indicate which objects have their OEE tracked, and a stats Collector reads the group members and adds rows at reset. Finally Performance Measures were added for the stats for processor 1. Processor_OEE_2.fsm 2023-08-22 Update: Added 'TEE' stat.

Good practice to reduce variance when experimenting is to separate streams of things that might vary in the model so that the random sampling is independent. An example might be that you have a number of processors who are members of the same breakdown profile (MTBF/MTTR object) where the individual breakdowns are dependant on the state of the processor. If during one scenario a processor is used more than before then it may sample the duration and next breakdown earlier, and therefore change the sequence with the other machines sampling of breakdown times, increasing variance. This is because the default setting for the MTBF time fields are using 'getstream(current)' - which means a single stream for the MTBF object, shared across all members. You could try to change this in the MTBF by using 'getstream(involved)' where 'involved' refers to the breakdown member machine. This causes other problems since if you're sampling processing times using the machine's stream too, then the amount of items processed will again change the breakdown times samples. You may judge this to be acceptable, but in a ideal world you'd still want separate streams and may want multiple streams for setup, processing, breakdowns, or subsystem failures. One way to accomplish this is by changing the way getstream() works such that it can generate a stream for any value you pass to it. That might be an object, as the current getstream() accepts, or it could be the string name of the object or it's path. It could also be an array which then opens a number of possibilities: In a breakdown you could replace getstream(current) with getStream([current,involved])   //generates a unique stream number for the MTBF/machine pair* In an Object Process Flow you could replace getstream(activity) with: getStream([current,activity]) // generates a unique stream for the instance and activity pair and works for the general process flow too. For a processing time on a processor instead of getstream(current) you could use getstream([current,"Processing"]) and getstream([current,"Setup"]) to generate two seperate sampling streams. The attached library contains an auto-installing user command that overrides getstream() to provide this functionality. The stream values save with the model. getStream-byvariant3.fsl * This implementation does have some limitations since during an experiment it does not communicate back the master model when trying to create new streams. For this reason you'll want to try and have all possible streams set up before running an experiment or avoid the type of actions that dynamically create the requirement for new streams - so that might be keeping all possible fixed resources and task executers, and hiding/removing them from groups rather than destroying them as the OnSet options of the parameters table do currently. Alternatively if you consistently name the dynamically created instances then the MTBF stream expression could be: getStream([current, involved.name]) Update: I've edited this post and library to use getStream (capital 'S') since the override parameter (var thing) doesn't stay in place and eventually causes FlexScript build errors. So with the updated library you'll need to find/replace from the model tree 'getstream' with 'getStream'.

This example model was created to clarify pre-emption of task sequences created in process flows. Specifically it will show: 1) that pre-empting of task sequences generated in a process flow does not require the token generating the sequence to be pre-empted. 2) that you can push a partially created sequence to a tasksequence list type. 3) how to use recursive calls to a sub flow. and afterwards: 4) how the milestone task can be used in a PF generated task sequence 5) the use a task type nodefunction Each queue is a member of the object process flow shown in the picture. Each generates a box along with a job to take it to another queue and pulls an initial task executer from the list of FreeTEs. The first task to travel and collect the box is preemptable and so we push the task sequence to a Premptable Job list before we add that travel task and pull it off the list when the task has completed. The rest of the task sequence is a standard load, travel and unload set of tasks. When the job is complete we know that the TE is free and could preempt another TE - which could be desired if the newly free TE is closer to the pickup queue than the one currently assigned. The CheckPreempt subflow is called with a label referencing the TE that has become free. This TE may not be the original TE that we pulled from the list of FreeTEs when the job was started, and so we discover the current TE by looking for the task sequence's owner object - done with the function findownerobject() since it does not cache the value in the same way ownerobject() does, and we could have had more than two TEs assigned to this tasksequence. Since the task sequence gets destroy I do this in the assign labels activity before we finish the ts: The Check Preempt subtask first tries to pull a preemptable job where the invoking TE is closer than the current executer of that job, and chooses the one where it is the most pronounced difference. The taskssequence list type has the distance field prepopulated and from that the two other field expressions otherDistance and howMuchCloser are derived. If no task sequence is chosen then we push the finishing TE to the FreeTEs list. If we choose a task sequence to preempt then we first label the token with the otherTE and the preempt the task sequence with a simple zero delay task. The we reDispatch the task sequence to the te that is calling Check Preempt. Since we now know that we've freed the otherTE from the task sequence it had we can invoke the Check Preempt subflow for that task executer, knowing that it will either get put onto the FreeTE list or get another task sequence and preempt another TE. This the is a recursive subflow call with the exit condition being that no preempatable tasksequence is found. Note that no token preemption from activities takes place and this is because the tasksequence is still tied to the token and its activity, which will still receive the call back when a task is complete, regardless of which task executer actioned the task. TEpreemptIfCloser1.fsm Next: Changing colors to indicate preemptable TEs and their destination - using milestone and nodefunction tasks. To better see the allocation and preempt activities we'll now change the model such that the TE color reflects the following: White when idle Gray when busy and not preemptable Matching the pickup queue's color when preemptable. First of all I defined two user commands - one to set the color of the TE (setTEcolor) and another (commandNode) to get the executable node of a user commands. This is so that I can add the fn() SetColor activity (it's a custom task of type nodefunction) ; specify the function using: commandNode("setTEcolor") and pass in the queue color as parameter 2 (parameter1 will be the TE that is executing this task). This means the user command code is just: Object te=param(1); Color col=param(2); te.color=col; The milestone task allows us to define a set of tasks that need to be repeat if the TE gets pre-empted. It's added again using the Custom Task activity and selecting the type as Milestone and has a parameter to specify the number of subsequent tasks that should be considered within the preemption range that will trigger tasks to be repeated. Since we want the Travel preemption to retrigger the setting of the color we set the range to 2. However since the token only needs to be notified when the travel task is complete that it should go on to the next process, and until then wait in the travel task we can uncheck the "Wait Until Complete" box of the Milestone and fn() SetColor activities: If you don't clear this for those two tasks we expect some misbehavior/desynchonization with the token. Finally there needs to be a resettrigger on the TE to set the color to white and a regular Change Visual activity after the pickup point is reached to set it to gray. Note I prefer the use of commands to sending messages, which would be another option, mostly because users can tell the intent of the call without having to inspect the message code. TEpreemptifCloser2.fsm

This article is basically a follow-up to this question: https://answers.flexsim.com/questions/98195/simultaneous-all-or-nothing-list-pulls.html In version 22.1, we added a new FlexScript feature called Coroutines. Basically, this lets you wait for events in the middle of executing FlexScript, using the "await" keyword. Recently, I decided to revisit this question (how to pull from multiple lists at once) and to see if I could use coroutines to simplify the Process Flow. The short answer is: yes! Here is a model that behaves identically (in terms of which token gets its multi-list request filled first) but replaces about 15 activities with 2 Custom Code blocks: multipullexample_coroutines_ordering_onfulfill.fsm In addition to having fewer activities, this model also runs faster (from ~12s to ~2s on my computer). To determine whether behavior was the same, I added a Statistics Collector and logged request/fullfill times and quantities. I did the same in the original model. The token IDs are off because the older model makes more tokens. Here's the older version, but with that stats collector, if you want to do your own comparisons. multipullexample.fsm The key lines of code are found in the Acquire All activity: // ~line 47 List.PullResult result = list.pull("", qty, qty, token, partition, flags); if (result.backOrder) { token.Success = 0; await result.backOrder; return 0; } // ... The new item here is the keyword "await". When the FlexScript execution reaches this line, the FlexScript execution is paused, and waits for the "awaitable" that you specify. In this case, we want to wait for the back order to be fulfilled. In both models, tokens check all lists to see if they can acquire the complete set of resources. In both models, if a token can't immediately fulfill one of its requests, tokens "go to sleep" until something changes. In the old model, tokens would "wake up" if anything was pushed to the correct list. In contrast, tokens in the new model only "wake up" if enough items are pushed to fulfill their back order. Basically, the second model has fewer false "wakeups" and so runs quite a bit faster.

This article explores an example model. In this model, items on downstream lanes are able to reserve dogs so that items on upstream lanes cannot use them: reservedogdemo.fsm About Dogs in FlexSim FlexSim simulates dogs on a power-and-free system in an extremely abstract and minimal way. A dog isn't a persistent entity at all. Instead, FlexSim calculates where dogs would be, given the speed, and when they would interact with items. This has a huge performance benefit. But if your logic needs items to interact with specific dogs, this can pose a problem: how do you interact with such an abstract entity? The Catch Condition The only time you can "see" a dog in FlexSim is during the Conveyor's Catch Condition: https://docs.flexsim.com/en/23.1/Reference/PropertiesPanels/ConveyorPanels/ConveyorBehavior/ConveyorBehavior.html#powerAndFree The catch condition fires when a dog passes by an item. If the catch condition returns a 1, the item catches the dog and transfers to the power and free conveyor. If the catch condition returns a 0, the item does not catch the dog. During the catch condition (and only during a catch condition), you can learn many things about a dog: ID - each dog has an ID. The ID is derived from the length of the conveyor and by the distance the conveyor has travelled. If a conveyor is 26 dogs long, the dogs will have IDs 1 through 26. Location - Since an item is trying to catch the given dog, you can derive the dog's location from the items location. Speed - The conveyor that owns the dog is "current" in the catch condition. So you can get the speed of the conveyor at that point. We'll use all these pieces of information in a moment. Creating Tokens to Represent Dogs The first real insight into this model is to make a dummy item. The purpose of this dummy item is to cause the Catch Condition to fire. It never gets on the conveyor. But when the catch condition fires, it makes a token that represents the dog. In this example, that item has a label called "DogFinder" Here is the relevant code from the catch condition: if (item.DogFinder?) { Object pe = current.DogPE; if (pe.stats.state(1).value == PE_STATE_BLOCKED) { return 0; } double dist = current.MaxDogDist; double speed = current.targetSpeed; double duration = dist / speed; if (!item.labels["DistAlong"]) { item.DistAlong = Vec3(item.getLocation(1, 0, 0).x, 0, 0).project(item.up, current).x; } Token token = Token.create(0, current.DogHandler); token.DistAlong = item.DistAlong; token.Conveyor = current.as(treenode); token.Duration = duration; token.DogNum = dogNum; token.Speed = speed; token.DetectTime = Model.time; token.release(1); return 0; } There's a lot going on in this code: This logic only fires for the fake dog finder item If the photo eye just upstream from the dog is blocked, that means there is an item, and this dog is not available. Return here if that's the case. Figure out how long this dog will last (the duration), assuming the conveyor runs at the same speed. In this model, there's a label on the conveyor called MaxDogDist. This is the distance from the PE to the end of the conveyor, minus 2 meters. If this is the first dog ever, calculate the position of the dog, given the position of the item. Store that on a label. Create a token with all kinds of labels. We'll need all this information to estimate where the dog is later, and to estimate how far it is from other items. Pushing Dog Tokens to a List Once the token is made, we need to push it to a list, so that items can pull them. If all your items are the same size, you can just push the token to a list directly. In this model, however, there are larger items that require two dogs. So there's a batch activity first. The dummy item is far enough back that it can detect two dogs and still push the first dog to the list in time for the first lane. So it holds the dog back in a Batch activity until one of two things happen: Either the next dog token appears, completing the batch. Or the max wait timer on the batch expires, indicating that the next dog is not available. Otherwise, there would have been a token. This duration is based on the conveyor's speed and dog interval. If the batch is complete, the first dog in the batch can be marked as a "double", meaning the dog behind it is also available. Once the flow has determined whether the dog is a single or double, it then pushes it to the list. Creating the DistToDog Field When pulling the dog from the list, an item needs to know the position of the dog relative to the item. Is it 0.3 meters upstream? Or is it 2 meters downstream? When we query the set of dogs, we need to filter out downstream dogs and order by upstream dogs, to reserve the closest one: WHERE DistToDog >= 0 ORDER BY DistToDog Here, DistToDog is positive if the dog is upstream, and negative if the dog is downstream. The code for this field is as follows: /**Custom Code*/ Variant value = param(1); Variant puller = param(2); treenode entry = param(3); double pushTime = param(4); double distAlong = Vec3(puller.getLocation(1, 0, 0).x, 0, 0).project(puller.up, value.Conveyor).x; double dt = Model.time - value.DetectTime; double dx = value.Speed * dt; double dogDistAlong = value.DistAlong + dx; return distAlong - dogDistAlong; This code assumes that the item waiting to merge is the puller. So we calculate the item's "dist along" the main conveyor. Then we estimate the location of the dog since the DogFinder item created the token. Then we can find the difference between the item's position and the dog's position. Pulling Dogs from the List Each incoming lane has a Decision Point. The main process flow creates a token when an item arrives there. At a high level, this token just needs to do something simple: pull an available downstream token. If all the items are the same size, it's that simple. But this example is more complicated! If the item is large, we also need to pull the upstream dog behind the dog we got, so that no other item can get that dog. And it gets even more complicated! It can happen that an item acquires the dog after a double dog. In that case, we need to mark the downstream dog as "not double", so that big items won't try to get it. So most of the logic in the ConveyorLogic flow is handling that case. Using the Dog Finally, the item must be assigned to that dog. The ConveyorLogic flow sets the DogNum label on the item. Then, the catch condition checks to see if the dog matches the item's DogNum. Upstream Items The final piece of this model is allowing upstream items to catch a dog on this conveyor. This model adds a special label to those items called "ForceCatch". The catch condition always returns true for those items.

The attached model contains an object process flow for a basicFR to sit between a regular conveyor and a Mass Flow. It looks at the interval between the discrete items leaving the regular conveyor and creates a new generative rate when it detects a change, allowing you to convert a stream of hundreds of items to singular events for the MFC as needed and capitalize on the advantages of MFCs. It's a simple process flow: To use just connect a BasicFR between the two conveyors and add it to the object process flow as a member instance. This is a proof of concept and does not currently handle accumulation across the interface, or aggregation from multiple discrete conveyors. MFC_RateMaker.fsm

If you've ever tried to nest groups of objects inside a hierarchy of planes, you may find the drawing of the planes suboptimal and lacking information: Using the container (modified plane) in the attached user library you can represent the container with just an outline. A settings dashboard is installed with the library along with some user commands and global variables. Corner prisms show the nesting layers under the prism: The option 'Use the container center' allows you to use it as either a plane as before or, when unselected, a bordered frame where dropping an object or clicking and dragging within the borders will behave as though you are dropping onto or clicking/dragging the model floor. You can also choose to hide the containers entirely for the cleanest visuals. I hope this will encourage users to use containers more, since when coupled with Templates and Object Process Flows they can increase the scaleability and make your developed assets more manageable. ( In those cases the container becomes the member instance of the process flow or template master and references to its components are made through pointer labels on the container rather than names which you may want to alter for reporting purposes. The pointer labels are updated automatically when creating a new instance of the container.) ContainerMarkers_v1.fsl If you want planes you already have in your model to adopt this style just add this to their draw code: return containerdraw(view,current);