En este video van a aprender cómo establecer diferentes tasas de llegadas según la hora del día utilizando el objeto Source. Para más videos tutoriales pueden acceder al canal de YouTube de FlexSim Andina y acceder a nuestra lista de reproducción de FlexTips.

En este video van a aprender cómo se establece la cantidad de FlowItems que se recibirá a través de los puertos de entrada de un Combiner usando la operación Update Combiner Component List with Labels. Para más videos tutoriales pueden acceder al canal de YouTube de FlexSim Andina y acceder a nuestra lista de reproducción de FlexTips.

En este video van a aprender cómo se cambia el color de los FlowItems y Objetos 3D en FlexSim usando la Ventana de Propiedades y los Triggers. Para más videos tutoriales pueden acceder al canal de YouTube de FlexSim Andina y acceder a nuestra lista de reproducción de FlexTips

Note: the demo models were created specifically for the FlexSim user community who speak spanish, and all the explanations, statistics, and documentation available in the dashboard, GUIs, and Model Documentation are in Spanish. Nonetheless, given that 3D animation is a universal language, you are welcome to download these models regardless of the language you speak. Manipulación de material / Material handling Demo_Crossdocking.fsm En este modelo, llegan tres tipos de productos a dos zona de recepción. Un primer grupo de operarios se encarga de transportarlos a una zona de almacenamiento intermedio. Desde allí, otro grupo de operarios los clasifica según la ciudad a la que serán despachados. In this model, three types of products arrive at two reception areas. A first group of operators transports them to an intermediate storage area, where another group of workers sorts them according to the city they will be delivered to. Preparación de pedidos / Picking Demo_Picking.fsm En este modelo, los pedidos llegan de manera aleatoria a lo largo del día y son revisados y preparados por un operario. En este modelo, se puede modificar si los operarios realizan la preparación a pie o utilizando un vehículo, así como la ubicación del almacén de estibas y el número de operarios asignados. In this model, orders arrive randomly throughout the day, which are reviewed and prepared by an operator. In this model, you can change whether the operator does the picking on foot or using a vehicle, the location of the pallet zone, and the number of assigned operators. AGV - Vehículos de guiado automático Demo_AGV.fsm Este modelo demuestra la aplicación del módulo de AGVs de FlexSim, que permite simular sistemas que utilizan AGVs para el transporte automatizado de material. En este modelo, un AGV con capacidad para cinco productos se encarga de transportarlos entre dos zonas dentro de un proceso. This model demonstrates the application of the AGV module in FlexSim, which allows simulating systems that use AGVs for auomated material handling. In this model, an AGV with a capacity of five productos is responsible for transporting them between two zones within a process. Preparación de kits / Kitting Demo_Kitting.fsm Este modelo representa un proceso productivo que utiliza la técnica de preparación de pedidos. Mediante parámetros, es posible activar o desactivar estaciones de trabajo para evaluar el impacto en la productividad del proceso. This model represents a kitting process. Through parameters, it is possible to activate or deactivate workstations to evaluate the change in throughput statistics. Centro de vacunación / Vaccination center Demo_HC.fsm En este modelo se representan un sistema de atención de pacientes en un centro de vacunación. Los pacientes llegan de forma aleatoria, se registran y esperan hasta que una enfermera los vacune. This model represents a patient care system in a vaccination center. Patients arrive randomly, complete a registration process, and wait until they are vaccinated by a nurse. Cajero automático / ATM Demo_ATM.fsm En este modelo, se representan un sistema de retiro de dinero en cajeros automáticos (ATMs). Los usuarios llegan de forma aleatoria y realizan un retiro si hay un cajero disponible; de lo contrario ,esperan en la fila. This model represents a cash withdrawal system at ATMs. Users arrive randomly and proceed with a withdrawal if a cash machine is available; otherwise, they wait in line. Fluidos / Fluid Library Demo_Fluidos.fsm Este modelo representa un sistema de embotellado. Se generan, mezclan, procesan y finalmente embotellan dos tipos de fluidos. Después de embotellados, un operario los transporta a la zona de empaque. This model represents a bottling system. Two types of fluids are generated, mixed, processed, and finally bottled. After bottling, they are transported to the packaging area by an operator. Navegador GIS / GIS Navigator Demo_GIS.fsm Este modelo muestra cómo se utiliza el módulo GIS de FlexSim para determinar la ubicación óptima de un nuevo almacén, teniendo en cuenta una red de distribución específica. This model demonstrates how the GIS module of FlexSim is used to determined the optimal location for a new warehouse, considering a specific distribution network.

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);

Last year a teacher asked me how to simulate public bus transportation, so I created a small sample model of a bus line in Nantes (France). Although all input and output data in the model are labeled in French, I thought that it could be nice to share it here with the worldwide community as 3D visualization is a universal language. Here is the model updated for FlexSim 2023: public_bus_23_0.fsm To set the travel times between the bus stops, and the stop times, I used the bus timetable that was available online in this document: Depliant-L-95-Web-2e.pdf You can check in the simulation if your bus arrives on time: Among the parameters, you can set how many buses are available at each terminus at the beginning. Be careful, if you don't have enough buses, you will not be able to stick to the timetable ! The road is built with network nodes. A checkbox allows you to show/hide the map. A listbox allows you to switch between the 3 network view modes (all details shown / edges only / hide all network).

This article is an updated version of a previous article by @Paul Toone. Thanks to Paul for the original, which you can read here: https://answers.flexsim.com/articles/23118/flexsim-xml-18.html In FlexSim, you can save any tree (including models and libraries) in XML format. There are many advantages to using this capability in your model development, including: Since XML is an ascii/text based format, it increases the utility of using content management and versioning software such as Subversion, CVS, Microsoft Visual SourceSafe, git, etc. to manage the development of a model. These systems automatically track a history of changes to a model or project, and for ascii/text based files, they allow you to see line-by-line change logs for the files, as well as revert line-item changes if needed. With the added benefit of versioning systems comes the ability to develop a model concurrently by different modellers using a much more stream-lined method. No more saving off individual t files from one modeller's changes to a model and loading them manually into the master model. When saving to XML format, if one modeller is working on one portion of the model, only that portion of the model file changes when saved, so when the modeller checks his changes into the versioning system's repository, another modeller can automatically merge those changes into his copy. If there are conflicts, where two modellers change the same part of the model, then the versioning system has tools that allow modellers to easily see in the XML file where the conflicts occur and quickly perform the manual conflict resolution. FlexSim also has the added ability to distribute a single model across multiple XML files. This increases your control over how to manage the model development in your versioning system, and makes conflict resolution even easier. The distributed save mechanism also opens the door for much more automated control of FlexSim. By saving small pieces of your model off into separate XML files, you can auto-regenerate one or more of those XML files outside of FlexSim, consequently changing the configuration of the model for the purpose of running different scenarios. Tutorial This tutorial will take you through saving a model as XML, and how you can configure the distributed save. Build a Simple Model First build a simple example model containing a Source, Queue, Processor and Sink. Save in XML Format Save the model, and in the save dialog, choose "FlexSim XML" from the drop-down at the bottom. Save the model as "PostOfficeXML". Now you've saved the file in FlexSim's XML format. You can view the file in a regular text editor like Visual Studio Code, Notepad++, etc. It's not necessary to know all the details of the file format. In simple terms, the primary tag in FlexSim XML is the <node> tag, representing a node in FlexSim. The node's name is described in the <name> tag, and the node's data, if applicable, is described in the <data> tag. If you plan on doing automated changes, and need a more detailed definition of the xml format, you can refer to FlexSim's xml schema (see the FlexSim install directory/help/MiscellaneousConcepts/FlexSimXML.xsd for more information). Version Management with Git At FlexSim, the development team uses Git to manage collaboration between developers. Part of development includes updating the MAIN and VIEW trees. In a similar way, you can use Git to collaboratively update the MODEL tree. The standard way to use Git is as follows: Set up a remote repository for the project. There are many online services for this, including GitHub, BitBucket, and many others. Each one has different pricing and terms of use. It is also possible that your company already runs a Git hosting service where you could create your remote repository on a company server. Once you have a remove repository, each user "clones" that repository to their local computer. Everyone "pulls" the latest changes from that repository and "pushes" their changes to that repository. To manage pushing and pulling, you can use Git on the command line. However, most users interact with Git through a client. One free one is called Git Extenstions: http://gitextensions.github.io/ By itself, version management is a deep topic. You might consider reading some tutorials to become more familiar with Git concepts: https://git-scm.com/docs/gittutorial https://github.com/git-guides https://github.com/skills/introduction-to-github https://www.atlassian.com/git/tutorials/learn-git-with-bitbucket-cloud Managing Changes with Git Let's assume you have a repository with the model file already in it. Let's make a change by moving the queue and saving the model: Once you save the model, Git can show you the changes you've made. In this case, you can see the spatialx and spatialy in your Git client: At this point, you could commit and push your changes. Others could then pull the changes into the model. Notice that git looks at changes in terms of lines being added and removed. In this case, we removed two old lines and added two new lines. Distributed Save As models get bigger and more complex, it can be helpful to split a single model file into multiple XML files. To do this you create a "FlexSim File Map" file. This is an xml file with the .ffm extension. It must be placed in the same directory as the model's .fsx file, and, except for the extension, must be given the same name as the .fsx file. So, let's create that file. Let's also create a subdirectory to put the distributed files into. Now edit PostOfficeXML.ffm in a text editor. The first thing we'll do is put the node MODEL:/Tools/ Workspace into a different file. This is something you'll probably want to do always if you're using version management. The node MODEL:/Tools/Workspace stores all of the windows open in the model, so if you're editing a model and change or reposition the windows that are open, that will change the model file. For version management this is often just static that doesn't need to be tracked, so we'll have the node saved off to a different file, and then we can just never (or at least rarely) check that file into the version management system. Specify the following code: <?xml version="1.0" encoding="UTF-8"?> <flexsim-file-map version="1"> <map-node path="/Tools/Workspace" file="distributedfiles/windows.fsx" file-map-method="single-node"/> </flexsim-file-map> Save the file map, then go back into FlexSim. Save the post office model again under the same name "PostOfficeXML.fsx". Now look in the distributedfiles directory. You'll see that it contains a new xml file named windows.fsx. This xml holds the definition of the node MODEL:/Tools/Workspace. All interaction with the model remains the same, i.e. from FlexSim you just load and save the main xml model file, but now the model is distributed across multiple files. If you look a the change to PostOfficeXML.fsx in your git client, you'll see that many lines have been replaced with a single line specifying the other file: This shows how much of the content of PostOfficeXML.fsx has been moved to distributedfiles/windows.fsx. In the file map, the main document element is the <flexsim-file-map> tag. Inside the main document element should be any number of <map-node> elements. Each <map-node> element should have a path attribute, a file attribute, and a file-map-method attribute. The path attribute should specify the path, from the main saved node, to the node that is going to be saved into the distributed file. The file attribute specifies the path, relative to the saved model file, to the distributed file that you want to save. The file-map-method should either have a value of "single-node" or "split-points". In this case "single-node" means you want to save all of the active node into windows.fsx. Now let's do an example of the "split-points" method. Let's say we want to save the Tools folder in one file, the Source and Queue into another file, and the Processor and Sink in a third file. To do this, add another <map-node> element to the file map: <?xml version="1.0" encoding="UTF-8"?> <flexsim-file-map version="1"> <map-node path="/Tools/active" file="distributedfiles/windows.fsx" file-map-method="single-node"/> <map-node path="" file="distributedfiles/Tools.fsx" file-map-method="split-points"> <split-point name="Source1" file="distributedfiles/Source_Queue.fsx"/> <split-point name="Processor3" file="distributedfiles/Processor_Sink.fsx"/> </map-node> </flexsim-file-map> Now save the file, save your FlexSim model, and again you'll see new files created in the distributedfiles directory. If a element uses the "split-points" method, then it can have sub-elements. Each split-point should have a name, defining the name of a node inside the map-node's defined node, and a file attribute defining the file to save to. The map-node has a path="" attribute. This means that it applies to the root node, or the model itself. The map-node's file attribute defines the first file to use. This configuration tells FlexSim to save all sub-nodes in the model up to but not including the node named "Source1" to the file "distributedfiles/Tools.fsx", save everything from Source1 up to but not including the node named "Processor3" in "distributedfiles/Source_Queue.fsx", and save everything from Processor3 to the end in "distributedfiles/Processor_Sink.fsx". Why Distribute? The main reason you would want to distribute your model across multiple files is for version management. It allows you to easily see what you've changed in your model by seeing what files, and consequently which parts of the tree, have changed. If you inadvertently changed one piece of the model, you can easily see that and then revert that change if needed. When multiple modellers are developing the model, one modeller can essentially confine himself to one part of the model tree, and by associating that part of the tree with a specific file, it makes it much easier to merge changes from different developers, it reduces the chance of conflicts in the merge, and makes it easier to do a manual merge if needed. Note on connections: FlexSim's XML save mechanism does have one catch regarding input/output/center port connections, as well as any other mechanism that uses FlexSim's coupling data. If you change connections in one portion of your model, it will actually change the serialized values of all connections/coupling data that occur after those connections in the tree, even if those connections were not changed. This can very easily cause merge issues if multiple modellers are changing connection data. However, if you distribute the model across multiple files, connection changes where both connection end-points are within the same file will only affect that file, and will not change other files. So if you can localize large "connection sets" into individual files, you can minimize the effect of changes and subsequently minimize merge conflicts.

This model shows a simple way of bringing together all the separate picks in an order to be consolidated in a putwall. It also shows a great way of tracking flowitems while on a conveyor through the use of tracked variable labels. A side concept is that the order reserves a slot in a rack for all of the picks. Conveyor-Routing-Order-Consolidation.fsm

27 July: Bug fix releases 22.0.7, 23.0.3 and 23.0.1 are now available. FloWorks 23.1.0 is now available (12 April 2023). This version of FloWorks is intended for use with FlexSim 2023 Update 1. If you are using FloWorks with FlexSim 2022, please use FloWorks version 22.0.6. If you are using FloWorks with FlexSim 2023 (LTS) please use FloWorks version 23.0.2. All versions can be found in the Downloads section of your FlexSim account on the 3rd party modules tab. Please do not hesitate to report any bugs, usability improvements and feature requests to [email protected]. About FloWorks FloWorks is a 3rd party module developed and maintained by Talumis BV ( talumis.com). It provides faster and more accurate modelling and calculation of fluid systems than the default FlexSim fluid library. It is especially useful within the oil, gas, and bulk industry both for production and supply chain optimization. This module requires a FloWorks license with active maintenance. For any questions, please email [email protected]. Release notes View the full release notes in the online documentation. FloWorks 23.1.1 All bug fixes in FloWorks 23.0.3 below. FloWorks 23.1.0 (12 April 2023) Segmented Pipe: removed virtual content and fixed properties Changed default content of flow tanks and mixer from 50,000 to 1,000. All bug fixes in FloWorks 23.0.2 below. FloWorks 23.0.3 Bug fix: Module dependency shows invalid module version number All bug fixes in FloWorks 22.0.7 below. FloWorks 23.0.2 (12 April 2023) Moved flow control to toolbox Support time series, level triggered event and workability in Tools.create and Tools.get Added context menu option to disable time series in toolbox Added dot syntax to create flow controls, get the default flow control, and request recalculation of a flow control's network All bug fixes in FloWorks 22.0.6 below. FloWorks 22.0.7 Bug fix: Multi-product FlowTank blocks simulation when layer is small but nonzero, due to rounding error. FloWorks 22.0.6 (12 April 2023) Bug fix: Loading Arm processing time defaults to 10. Bug fix: fixed length³ to volume conversion for models in non-metric units Bug fix: fixed error message "FlowConveyor content changes are not allowed" Bug fix: flow conveyor density calculation was incorrect with non-metric units Bug fix: icon grid was not filtering correctly when A-connecting Bug fix: flow conveyor properties panel fixed Bug fix: added vertices editor to polygon flow tank quick properties Bug fix: after the first vessel, vessels were positioned incorrectly in the loading point Bug fix: quick properties now shows "Max. rate" for Loading Arm and "Max. inflow" for Flow Sink. Bug fix: removed duplicate Input / Output / Content change events from event selection list Bug fix: corrected input / output in on change event at end of warmup Bug fix: on empty and on full event would sometimes fire twice

Attached is a simple example of using the GIS Module. It contains a GIS Map with 8 Cities represented as points connected by driving routes. gis_example.fsm The model's Process Flow randomly generates tokens, which create items to be carried by trucks from one city to another. After a delay, the item is removed from the model. This very simple example demonstrates how to use the GIS Module to model movement of items from sources to destinations without any FixedResource objects. All the logic can be done through Process Flow to control what happens in the model. This model also demonstrates the Min Scale and Max Scale properties of a GIS Map object. By default, as you zoom the 3D view in and out, the 3D shapes on a GIS map scale so that they remain the same size on the screen, like abstract flat billboard images. When you zoom far out, because the items remain the same size, they seem really big relative to the map. Similarly, when you zoom far in, because the items remain the same size, they seem really small relative to the map. The Map object has Min Scale and Max Scale properties to cap the scaling up and down of objects as you zoom in and out, so that they will only scale up to a certain size and only scale down to a certain size. This makes it so they scale within a certain range, but don't get too big or too small. If you set both of these values to a particular number, then they will not scale at all, but rather be that particular size.

FlexSim 2023 Update 1 is now available for download. You can view the Release Notes in the online user manual. FlexSim 23.1.0 Release Notes For more in-depth discussion of the new features, check out the official software release page: FlexSim 2023 Update 1: Mass Flow Conveyor Improvements, Table Validation, and more If you have bug reports or other feedback on the software, please email [email protected] or create a new idea in the Bug Report space or Development space.

This model showcases the latest enhancements to the mass flow conveyor object, found in FlexSim 2023 Update 1. See several new features in action, including the Randomized fill order and Width Rules for conveyors. Mass-Flow-Bottling-Demo_23-1.fsm

This sample model shows the flexibility and power of the mass flow conveyor object. It contains many examples of stations found in this production process, from depalletizing glass bottles all the way through final packaging. The model makes use of many custom shapes that add realism and visual appeal to the simulation. Mass-Flow-Bottling-Line-Custom-Shapes.fsm

FlexSim 2023 Update 1 Beta is now available. (Updated March 14) FlexSim 23.1.0 Release Notes 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. 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 [email protected] or create a new idea in the Bug Report space or Development space.

Attached is an example simulation of a rail hump yard. Trains in this hump yard are processed in three stages: Arrival - A train engine delivers an arriving train into the arrival area of the yard and then leaves Classification - The shunt engine takes trains from the arrival area to the hump. From there the train is uncoupled into sets of cars for classification, and each set of cars 'falls' to its designated departure train and couples to it. Departure - Once a train has been composed, it is transferred to the departure area, where it waits a random time until departure. I've tried to keep the logic as simple as possible so you can understand the process flow. I've implemented no traffic control between train engines/shunt engine, so they will occasionally run over each other. However, I have used AGV routing constraints to dynamically block off sections of track that are filled by trains, so the engines will move around them. HumpYardSample.fsm

Have you received this licensing error? Operations error: 7284 Cannot perform support actions on inactive fulfillment record FID_ ... (entire fulfillment ID listed) You may encounter this error during an attempted license return or repair. This error means that the fulfillment was already returned. A fulfillment is an activation of a license. Once that activation has been returned to the main license server, the fulfillment becomes inactive. It is meaningless to return or repair a fulfillment that has already been returned. So, how do you have an inactive fulfillment still on your computer if it was supposedly returned? There are a couple of ways this can happen: Someone requested that the license be force-returned. Sometimes a license can be lost, so a client contacts their local distributor to force the license back. Later on, the license is found activated on a PC, however it will not be returnable because the license was already force-returned. You returned your license normally, but later restored your computer from a backup or otherwise rolled back to a previous state. This may break your license, but in trying to repair the old fulfillment, the main license server reports to you that actions cannot be taken on that inactive fulfillment - it was already returned. The point with this error is that FlexSim's main license server has a record that your fulfillment was already returned. The fulfillment you are trying to return or repair is inactive - no actions can be taken on it. The solution is to delete the fulfillment record from your PC: License Server - Delete fulfillment Standalone - Delete fulfillment

I created this custom visual tool because I had a situation where we needed to see the progress of activites in the 3D View. I basically just change the size of a plane based on the completion level and calculate a percentage. Model is attached and contains some explanations. Enjoy, Custom visual tool.fsm

This model shows a two-floor healthcare facility with an elevator connecting the two floors. Patients are moved on a gurney from different rooms and across floors. There is a dashboard with two checkboxes so you can turn on and off the visuals for the different floors. One unique feature of this model is that each patient has a constant companion who follows them throughout their care process. This could be used either to demonstrate that companions can be modeled in FlexSim, or as a basis for another modeler to copy. FlexSim-HC-2023-MultiFloor_With_Companion.fsm

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