AnyLogic-based intelligent workshop production logistics modeling simulation method

Abstract

The invention discloses an Anylogic-based intelligent workshop production logistics modeling and simulation method. The method comprises the following steps of 1, opening Anylogic simulation software to construct a basic factory environment model, and adopting a modular design mode; 2, data collection is carried out, so that model construction can be carried out conveniently; step 3, constructing a two-dimensional workshop layout map; selecting a drawing view, adding basic layout elements, carrying out layout according to the data, and connecting a workstation; 4, constructing a three-dimensional physical model, setting a three-dimensional environment, adding a three-dimensional object, adjusting object attributes, and creating a visual interface; 5, constructing a production logistics simulation logic chain; step 6, calculating production logistics logic by adopting an external algorithm; step 7, setting data collection to facilitate subsequent analysis and optimization; and displaying the simulation result and the performance index by using a chart or report function. According to the invention, high precision, high reliability and high accessibility can be achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent workshop modeling and simulation methods, and in particular to an intelligent workshop production logistics modeling and simulation method based on AnyLogic. Background Art

[0002] AnyLogic simulation platform provides a multi-method modeling method for manufacturing, logistics, supply chain, transportation, healthcare, etc. The three basic modeling methods commonly used are discrete event simulation, continuous system dynamic modeling, and agent-based modeling, and the existing technology supports visual modeling environment and multi-level simulation.

[0003] The patent "Establishment of a basic engineering construction control simulation model based on Anylogic and its application research" with the prior art application number 202110890741.1 discloses the establishment of a basic engineering construction control simulation model based on Anylogic and its application research. This invention classifies and optimizes the corresponding scene parameters to achieve construction organization optimization for similar projects or new projects. Enterprises can draw on this construction knowledge and experience to make good assessments and judgments on existing projects, ensure that the construction period is completed on time and efficiently, improve production efficiency, and save manpower and material costs.

[0004] In this invention, the functions encapsulated in AnyLogic mainly support the model operation under flat and simple heuristic rules. However, the problem is that when the model is large in scale and complex in complexity, such a modeling scheme makes AnyLogic face the challenge of model operation efficiency. In addition, AnyLogic has relatively few high-precision model simulation cases. This is mainly because high-precision model simulation requires a large amount of real-time data and complex algorithms, and usually requires more professional domain knowledge and specific data collection methods. Summary of the invention

[0005] The present invention provides an AnyLogic-based intelligent workshop production logistics modeling and simulation method to solve the problems of low operating efficiency and low precision in the prior art.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: The AnyLogic-based intelligent workshop production logistics modeling and simulation method includes the following steps: Step 1. Open AnyLogic simulation software to build a basic factory environment model. For large-scale and complex models, a modular design method is adopted to make it easy to expand and update. It includes three parts: two-dimensional workshop layout, three-dimensional physical model, and production logistics simulation logic chain; Step 2: Collect data to facilitate model construction, where the workshop layout CAD drawing is used for two-dimensional workshop layout construction; the production equipment model drawing and AGV model drawing are used for three-dimensional physical model construction; and the production flow chart, process flow chart and BOM list are used for the production logistics simulation logic chain; Step 3: Build a 2D workshop layout: Create a new model in AnyLogic, set the model properties, and select the system operation unit as seconds; Select the drawing view: In the model interface, click the drawing view Drawing View to create a visual space for the workshop layout; Add basic layout elements: Select the Shapes tool from the toolbox and use basic shapes such as rectangles and circles to add walls, machines, and workstations of the workshop; Layout based on data: Based on the shop floor data collected in step 2, adjust the size and location of machines and workstations to ensure that the layout meets actual needs; Connect workstations: Use the Connector tool to represent the flow path between workstations and save the layout diagram; Step 4: Build a 3D physical model: In AnyLogic, create a new 3D View; select Add View in the Model Tree and then select the 3D View option; Set up the 3D environment: In the 3D view, set up the model's environment, including workshop wall obstacles and lighting; according to the data in step 2, adjust the size and position of each shape to ensure that they conform to the actual workshop layout; Add 3D objects: Select 3D objects from the toolbox to add equipment, workstations, machines, transportation tools and shelves in the workshop; specific models can be added to the scene by importing external 3D model files in OBJ format through the import function; Adjust object properties: set properties for each 3D object; Create a visual interface: Add user interface elements, including buttons, sliders, and charts, to the model for interaction and data display; use the UI Designer to lay out these elements, ensure that they are consistent with the 3D model, and save the model; Step 5: Construct the production logistics simulation logic chain, including: Create agent objects: In the model tree, right-click Agent and select New Agent to create different agent tasks, transport vehicles, workstations, and materials; Define the task agent: Create properties for the task agent, including task type, priority, and required time; define the life cycle of the task, including receiving, executing, and completing states; use a statechart to describe the different states of the task; Design behavioral rules: After receiving a task, select an idle workstation to process it and update the task status to in progress; Define the transporter agent: Define the movement properties for the transporter agent, including speed, load, and path; use the path tool to create the transporter's driving route and set transportation rules in the properties, including automatically selecting the best path to avoid obstacles; Design behavioral rules: automatically receive tasks within a specified time, transport materials along a predetermined route, and notify the workstation when arriving at the workstation; Define workstation agents: Set processing capacity, working hours, and task queues for workstation agents; define workstation status including idle, busy, and maintenance; use state diagrams to manage workstation workflows, including the process of receiving and completing tasks; Design behavioral rules: When a task is received, update the status to busy, and mark the task as completed after completion; Define material intelligent entities: Create material intelligent entities and define their properties, including type, quantity, and storage location; design material flow rules and automatically assign them to corresponding workstations when tasks are executed; Establish decision logic: Use conditional statements and rules to set up interactions and decisions between agents; when there are multiple tasks to be processed, give priority to tasks with higher priority; if the transport vehicle is idle and there are materials to be transported, automatically select the nearest workstation for transportation; Simulate behavioral interactions: Use AnyLogic events and triggers to set time-driven behaviors, including periodic checks for free transport vehicles or workstations; use messaging to enable different agents to notify each other of their state changes; Step 6: Use external algorithms to calculate production logistics logic: Integrate Python algorithms to handle complex production logistics logic, and gradually improve the accuracy of the model through iterative development and testing. At the same time, use the API interface to connect the external algorithm to AnyLogic to ensure that real-time data can be fed back to the algorithm. Step 7. Set up data collection: Define data collection rules in the intelligent agent and record key indicators, including the manufacturing time of a single vehicle, the collection of auxiliary materials for body loading, the processing time and proportion of each link, inventory bar charts and workshop dynamic Gantt charts, to facilitate subsequent analysis and optimization; use charts or report functions to display simulation results and performance indicators.

[0007] The present invention is based on a real smart workshop, adopts a modeling method that combines discrete events and agents, and adopts modular design in step 1 to solve the problem of low operating efficiency of large-scale complex models; in step 5, the behaviors and attributes of different agents are independently defined to facilitate the use of external algorithms to accept large data sets and achieve higher accuracy.

[0008] In the present invention, the simulation object is the new energy vehicle assembly workshop, which is a new type of "intelligent factory" that fully combines the lean production concept and realizes closed-loop integration in product testing, quality inspection and analysis, production logistics and other links with the production process. The present invention makes scheduling decisions and optimizations on this basis, which can be used as a standardized sample of smart factories to provide guidance for workshop planning and design, and production scheduling optimization. The present invention realizes the following two functions: 1) Visual analysis: Through the visualization function of AnyLogic, the operation of workshop logistics is displayed in a graphical or dynamic simulation manner. This enables users to more intuitively understand the operation and bottlenecks of the workshop logistics system, as well as the impact of various decisions on system performance. 2) Dynamic production scheduling optimization: The present invention can monitor production progress and resource usage in real time, and automatically adjust production scheduling through data analysis and intelligent algorithms, optimize job sequence and resource allocation, so as to improve overall production efficiency and reduce production costs.

[0009] At the same time, the present invention has two advantages: 1. The present invention constructs a replica of an intelligent manufacturing factory: the model of the present invention is based on a real intelligent workshop, transitions from a single logistics decision to the establishment of a logistics system, realizes the coordinated transportation of SPS distribution, sorted parts distribution, and manual transportation, and uses Java language programming and database interfaces to meet the refinement and customization of functions based on the basic model, achieving high accuracy, high reliability, and high accessibility.

[0010] 2. The present invention designs interactive visual operations of a two-dimensional interface and a three-dimensional window, allowing users to view the basic information of any transport vehicle, any material, and any workstation during the model operation at any time and at will, including the transport vehicle number, speed, transport object, destination, idle time ratio; material number and type; workstation status, processing time, etc. DETAILED DESCRIPTION

[0011] The following examples further illustrate the present invention.

[0012] The AnyLogic-based intelligent workshop production logistics modeling and simulation method includes the following steps: Step 1. Open AnyLogic simulation software to build a basic factory environment model. For large-scale and complex models, a modular design method is adopted to make it easy to expand and update. It includes three parts: two-dimensional workshop layout, three-dimensional physical model, and production logistics simulation logic chain; Step 2: Collect data to facilitate model construction, where the workshop layout CAD drawing is used for two-dimensional workshop layout construction; the production equipment model drawing and AGV model drawing are used for three-dimensional physical model construction; and the production flow chart, process flow chart and BOM list are used for the production logistics simulation logic chain; Step 3: Build a 2D workshop layout: Create a new model in AnyLogic, set the model properties, and select the system operation unit as seconds; Select the drawing view: In the model interface, click the drawing view Drawing View to create a visual space for the workshop layout; Add basic layout elements: Select the Shapes tool from the toolbox and use basic shapes such as rectangles and circles to add walls, machines, and workstations of the workshop; Layout based on data: Based on the shop floor data collected in step 2, adjust the size and location of machines and workstations to ensure that the layout meets actual needs; Connect workstations: Use the Connector tool to represent the flow path between workstations and save the layout diagram; Step 4: Build a 3D physical model: In AnyLogic, create a new 3D View; select Add View in the Model Tree and then select the 3D View option; Set up the 3D environment: In the 3D view, set up the model's environment, including workshop wall obstacles and lighting; according to the data in step 2, adjust the size and position of each shape to ensure that they conform to the actual workshop layout; Add 3D objects: Select 3D objects from the toolbox to add equipment, workstations, machines, transportation tools and shelves in the workshop; specific models can be added to the scene by importing external 3D model files in OBJ format through the import function; Adjust object properties: set properties for each 3D object; Create a visual interface: Add user interface elements, including buttons, sliders, and charts, to the model for interaction and data display; use the UI Designer to lay out these elements, ensure that they are consistent with the 3D model, and save the model; Step 5: Construct the production logistics simulation logic chain, including: Create agent objects: In the model tree, right-click Agent and select New Agent to create different agent tasks, transport vehicles, workstations, and materials; Define the task agent: Create properties for the task agent, including task type, priority, and required time; define the life cycle of the task, including receiving, executing, and completing states; use a statechart to describe the different states of the task; Design behavioral rules: After receiving a task, select an idle workstation to process it and update the task status to in progress; Define the transporter agent: Define the movement properties for the transporter agent, including speed, load, and path; use the path tool to create the transporter's driving route and set transportation rules in the properties, including automatically selecting the best path to avoid obstacles; Design behavioral rules: automatically receive tasks within a specified time, transport materials along a predetermined route, and notify the workstation when arriving at the workstation; Define workstation agents: Set processing capacity, working hours, and task queues for workstation agents; define workstation status including idle, busy, and maintenance; use state diagrams to manage workstation workflows, including the process of receiving and completing tasks; Design behavioral rules: When a task is received, update the status to busy, and mark the task as completed after completion; Define material intelligent entities: Create material intelligent entities and define their properties, including type, quantity, and storage location; design material flow rules and automatically assign them to corresponding workstations when tasks are executed; Establish decision logic: Use conditional statements and rules to set up interactions and decisions between agents; when there are multiple tasks to be processed, give priority to tasks with higher priority; if the transport vehicle is idle and there are materials to be transported, automatically select the nearest workstation for transportation; Simulate behavioral interactions: Use AnyLogic events and triggers to set time-driven behaviors, including periodic checks for free transport vehicles or workstations; use messaging to enable different agents to notify each other of their state changes; Step 6: Use external algorithms to calculate production logistics logic: Integrate Python algorithms to handle complex production logistics logic, and gradually improve the accuracy of the model through iterative development and testing. At the same time, use the API interface to connect the external algorithm to AnyLogic to ensure that real-time data can be fed back to the algorithm. Step 7. Set up data collection: Define data collection rules in the intelligent agent and record key indicators, including the manufacturing time of a single vehicle, the collection of auxiliary materials for body loading, the processing time and proportion of each link, inventory bar charts and workshop dynamic Gantt charts, to facilitate subsequent analysis and optimization; use charts or report functions to display simulation results and performance indicators.

[0013] In this embodiment, the corresponding production process parameters in the workshop model are designed as follows: The workshop model is divided into four areas according to the factory layout, namely storage area, production area, testing area and rework area.

[0014] (1) The storage area includes the unloading dock, auxiliary material storage shelves, manual sorting area, and AGV charging area.

[0015] (2) The production area includes 4 floor interior installation lines, 1 door subassembly conveyor line, 4 chassis installation lines, 8 overhead circulating conveyor chains, 2 finishing lines, approximately 270 to 280 production stations, and a variety of advanced production equipment.

[0016] (3) The inspection area includes 1 fully automatic integrated vehicle inspection line, 1 rain test line, and 1 submission line.

[0017] (4) The repair area includes parking spaces and a number of advanced repair equipment.

[0018] Workshop production and processing parameters: The production line speed is set to 1m / s, and the conveyor line speed is set to 0.5m / s. The no-load running speed of the avg trolley is the same as the full-load running speed and is 1m / s. The transportation tasks of the agv trolley are designed and assigned by the model building algorithm. The material buffer area capacity at the production line side is 3.

[0019] In this embodiment, when running AnyLogic, the setting interface is first entered, and the user sets the number of orders, conveyor belt running speed, and agv running speed within the specified threshold. Click "Run" to enter the two-dimensional interface of the new energy vehicle assembly workshop. The body resources use the inject function call and reach the online point. The fine sorting area has four selection lines, and the task priority is sorted according to the weight. A fine sorting area intelligent body car is transported to the door removal station one by one.

[0020] At the door removal station, the split control controls the splitting of the body intelligent body into one body and two rows of doors, which are then lifted by a cantilever crane (moveByCrane control) and transported to the interior processing line and the door processing line respectively. The production line speed changes according to order requirements, and auxiliary materials are consumed in the processing of the interior line. This additional task is the responsibility of the AGV trolley in the storage area, which monitors the material consumption along the interior line. When it is less than the set value, auxiliary material demand and auxiliary material distribution tasks are generated and stored in the coll task set. The model sets up an alarm system. When the production line does not match the quantity of auxiliary materials and cannot operate normally, the production line stops and sends an alarm signal to the console until the abnormal situation is resolved.

[0021] Pallet racks (palletRack setting) are set up in the storage area, each with three layers and 10 cells. When the inventory in the storage area is less than a certain set value, the forklift unloads the goods from the external truck and places them on the rack in order, waiting for the AGV transporter to transport them away. The material distribution tasks of the four interior lines are divided into regions according to the storage location, and the numbered materials are transported separately according to the line requirements.

[0022] The interior lines are connected by gantry cranes (moveByCrane control), and the interior lines are connected to the overhead conveyor line by elevators (lift control). The machine lifting / lowering speed is set to 0.5m / s. The line side inventory capacity is set by queue control and delay control, and the range is about 3 to 5.

[0023] The probability of vehicle body failure is set as a random probability event, which can be checked on the inspection line. Once a problem occurs, workers will use trailers to transport the vehicle body back and forth to the repair area. Vehicles that are not found to be abnormal are transported to the OK line by an aerial conveyor belt and are finished off the line.

[0024] The material distribution mode of the entire workshop strictly simulates the real situation. The model establishes a double-layer space, with the first layer for production and the second layer for transportation. The layout retains the entire process flow, and reserves a dynamically adjustable material transportation strategy. The interior line adopts SPS single-vehicle delivery, the AGV island adopts sorted piece transportation, and the others adopt manual transportation. Special materials such as tires are transported by aerial spiral transport lines. This model gives the three transportation methods internal logical settings respectively, and meets the coordinated optimization of material distribution strategies under different distribution methods.

[0025] The preferred embodiments of the present invention are described in detail above. The embodiments described in the present invention are merely descriptions of the preferred embodiments of the present invention, and are not intended to limit the concept and scope of the present invention. The various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction, and such combinations, as long as they do not violate the concept of the present invention, should also be regarded as the contents disclosed in the present disclosure. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0026] The present invention is not limited to the specific details of the above-mentioned embodiments. Within the technical concept of the present invention and without departing from the design concept of the present invention, various modifications and improvements made to the technical solution of the present invention by technical personnel in this field should fall within the protection scope of the present invention. The technical contents for which protection is sought in the present invention have been fully recorded in the claims.

Claims

1. The AnyLogic-based intelligent workshop production logistics modeling and simulation method is characterized by: The following steps are involved: Step 1. Open AnyLogic simulation software to build a basic factory environment model. For large-scale and complex models, a modular design method is adopted to make it easy to expand and update. It includes three parts: two-dimensional workshop layout, three-dimensional physical model, and production logistics simulation logic chain; Step 2: Collect data to facilitate model building, where the workshop layout CAD drawing is used for two-dimensional workshop layout construction; production equipment model drawing and AGV model drawing are used for three-dimensional physical model construction; and production flow chart, process flow chart and BOM list are used for production logistics simulation logic chain; Step 3: Build a 2D workshop layout: Create a new model in AnyLogic, set the model properties, and select the system operation unit as seconds; Select the drawing view: In the model interface, click the drawing view Drawing View to create a visual space for the workshop layout; Add basic layout elements: Select the Shapes tool from the toolbox and use basic shapes such as rectangles and circles to add walls, machines, and workstations of the workshop. Layout based on data: Based on the shop floor data collected in step 2, adjust the size and location of machines and workstations to ensure that the layout meets actual needs; Connect workstations: Use the Connector tool to represent the flow path between workstations and save the layout diagram; Step 4: Build a 3D physical model: In AnyLogic, create a new 3D view; select Add View in the model tree and then select the 3D View option; Set up the 3D environment: In the 3D view, set up the model environment, including workshop wall obstacles and lighting; According to the data in step 2, adjust the size and position of each shape to ensure that they conform to the layout of the actual workshop; Add 3D objects: Select 3D objects from the toolbox to add equipment, workstations, machines, transportation tools and shelves in the workshop; specific models can be added to the scene by importing external 3D model files in OBJ format through the import function; Adjust object properties: set properties for each 3D object; Create a visual interface: Add user interface elements, including buttons, sliders, and charts, to the model for interaction and data display; use the UI Designer to lay out these elements, ensure that they are consistent with the 3D model, and save the model; Step 5: Construct the production logistics simulation logic chain, including: Create agent objects: In the model tree, right-click Agent and select New Agent to create different agent tasks, transport vehicles, workstations, and materials; Define the task agent: Create properties for the task agent, including task type, priority, and required time; define the life cycle of the task, including receiving, executing, and completing states; use a statechart to describe the different states of the task; Design behavioral rules: After receiving a task, select an idle workstation to process it and update the task status to in progress; Define the transporter agent: Define the movement properties for the transporter agent, including speed, load, and path; use the path tool to create the transporter's driving route and set transportation rules in the properties, including automatically selecting the best path to avoid obstacles; Design behavioral rules: automatically receive tasks within a specified time, transport materials along a predetermined route, and notify the workstation when arriving at the workstation; Define workstation agents: Set processing capacity, working hours, and task queues for workstation agents; define workstation status including idle, busy, and maintenance; use state diagrams to manage workstation workflows, including the process of receiving and completing tasks; Design behavioral rules: When a task is received, update the status to busy, and mark the task as completed after completion; Define material intelligent entities: Create material intelligent entities and define their properties, including type, quantity, and storage location; design material flow rules and automatically assign them to corresponding workstations when tasks are executed; Establish decision logic: Use conditional statements and rules to set up interactions and decisions between agents; when there are multiple tasks to be processed, give priority to tasks with higher priority; if the transport vehicle is idle and there are materials to be transported, automatically select the nearest workstation for transportation; Simulate behavioral interactions: Use AnyLogic events and triggers to set time-driven behaviors, including periodic checks for free transport vehicles or workstations; use messaging to enable different agents to notify each other of their state changes; Step 6: Use external algorithms to calculate production logistics logic: Integrate Python algorithms to handle complex production logistics logic, and gradually improve the accuracy of the model through iterative development and testing. At the same time, use the API interface to connect the external algorithm to AnyLogic to ensure that real-time data can be fed back to the algorithm. Step 7. Set up data collection: Define data collection rules in the intelligent agent and record key indicators, including the manufacturing time of a single vehicle, the collection of auxiliary materials for body loading, the processing time and proportion of each link, inventory bar charts and workshop dynamic Gantt charts, to facilitate subsequent analysis and optimization; use charts or report functions to display simulation results and performance indicators.