CN118151618B - Discrete manufacturing workshop scheduling control method, device, equipment and medium - Google Patents
Discrete manufacturing workshop scheduling control method, device, equipment and medium Download PDFInfo
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Abstract
The application relates to a method, a device, equipment and a medium for controlling dispatching of a discrete manufacturing workshop, wherein the method comprises the following steps: initializing each particle in the particle population based on a preset loading and unloading port optimization algorithm; calculating and determining an objective function value of an individual solution in the particle population based on the configuration cost, the transportation cost, the yield constraint condition and the production period constraint condition; when the preset iteration condition is reached, taking the particle with the smallest objective function value as the optimal selection strategy of the position and capacity of the feeding and discharging openings of the production units corresponding to the objective product; and determining the positions and the capacities of the upper and lower feed inlets of each production unit according to an optimal selection strategy, and determining the optimal scheduling sequence of the intelligent carrying trolley according to the positions and the capacities of the upper and lower feed inlets of each production unit based on a preset cooperative scheduling algorithm so as to complete the scheduling control of the discrete manufacturing workshops. The application can improve the rationality of the production line layout of the target product, and can greatly improve the production efficiency and save the production cost.
Description
Technical Field
The present application relates to the field of automation control, and in particular, to a discrete manufacturing shop scheduling control method, a corresponding apparatus, an electronic device, and a computer readable storage medium.
Background
With the continued development of modern manufacturing, discrete manufacturing systems (Discrete Manufacturing System, DMS) play a critical role in the manufacturing process. Advanced automation and informatization technologies are commonly adopted by manufacturing enterprises to form highly complex discrete manufacturing systems to assist in analysis in order to optimize the production flow, reduce the production risk and the production cost and improve the resource utilization rate.
At present, an innovative discrete manufacturing system simulation optimization platform capable of promoting the innovative development of manufacturing industry and improving the quality and efficiency of products is indispensable, and the existing discrete manufacturing system simulation platform has some limitations, including the problems that a simulation model is not perfect enough, a simulation result is not accurate enough, an operation interface is not user-friendly and the like, meanwhile, the simulation process has no multi-factor and omnibearing optimization function for the manufacturing system, and along with the continuous development and complicating of the manufacturing system, the problems in the manufacturing process of products are increasingly more, such as material shortage, unreasonable layout of a production line, equipment faults and the like are prominent, so that the problems of low production efficiency, high production cost and the like are caused.
In summary, the present inventors have made a corresponding search for solving the problems of low production efficiency and high production cost caused by the shortage of materials, unreasonable layout of the production line, equipment failure, etc. in the product manufacturing process of the discrete manufacturing workshops in the prior art.
Disclosure of Invention
The present application has been made to solve the above-mentioned problems, and an object of the present application is to provide a discrete manufacturing shop scheduling control method, a corresponding apparatus, an electronic device, and a computer-readable storage medium.
In order to meet the purposes of the application, the application adopts the following technical scheme:
a discrete manufacturing shop scheduling control method according to one of the objects of the present application comprises:
Initializing each particle in a particle population based on a preset feeding and discharging port optimization algorithm in response to a feeding and discharging port optimization instruction, wherein the particle population represents a selection strategy of the feeding and discharging port position and capacity of each production unit corresponding to a target product;
determining configuration cost of an upper feed opening and a lower feed opening of a production unit corresponding to the target product, transportation cost among the production units, yield constraint conditions of the production units and production cycle constraint conditions, and calculating and determining objective function values of individual solutions in the particle population based on the configuration cost, the transportation cost, the yield constraint conditions and the production cycle constraint conditions;
Based on the objective function value of the individual solutions, selecting the individual solutions with high probability to enter a next generation particle population, randomly generating a cross point for the selected individuals, and exchanging the two individuals at the cross point to generate new individuals;
Determining mutated new genes according to the new individuals by adopting genetic algorithm boundary mutation, adding the mutated new genes into the particle population, updating the speed and the position of each particle in the particle population, and taking the particle with the minimum objective function value as the optimal selection strategy of the position and the capacity of the feeding and discharging port of each production unit corresponding to the objective product when the preset iteration condition is reached;
responding to an instruction for dispatching the intelligent handling trolleys, determining a starting point and a finishing point corresponding to each intelligent handling trolley, determining the position and the capacity of the loading and unloading openings of each production unit according to the optimal selection strategy, and determining the optimal dispatching sequence of the intelligent handling trolleys according to the starting point and the finishing point corresponding to the intelligent handling trolleys and the position and the capacity of the loading and unloading openings of each production unit based on a preset collaborative dispatching algorithm so as to finish dispatching control of a discrete manufacturing workshop.
Optionally, the step of calculating and determining the objective function value of the individual solution in the particle population based on the configuration cost, the transportation cost, the yield constraint, and the production cycle constraint comprises:
Acquiring production operation time of an intelligent carrying trolley among production units in the production process of a target product, wherein the production operation time comprises one or more of parking lot waiting time, load transportation time, load congestion time, no-load walking time, no-load congestion time, loading time and unloading time;
and calculating and determining the objective function value of the individual solution in the particle population based on the production operation time, the configuration cost of the upper and lower feed inlets of the production units, the transportation cost among the production units, the yield constraint condition of the production units and the production period constraint condition.
Optionally, determining a mutated new gene according to the new individual by adopting genetic algorithm boundary mutation, and adding the mutated new gene into the particle population, including:
generating a random variation value at the boundary of the new individual by adopting genetic algorithm boundary variation;
And replacing the generated mutation value with the gene value at the boundary of the new individual to determine a mutated new gene, and adding the mutated new gene into the particle population.
Optionally, the step of determining the configuration cost of the loading and unloading port of the production unit of the target product includes:
determining the capacity and unit configuration cost of the loading and unloading ports of each production unit of the target product;
calculating and determining the corresponding configuration cost of each production unit based on the product of the capacity of the feeding opening and the unit configuration cost of each production unit;
And calculating and determining the configuration cost of the upper and lower feed inlets of the production units corresponding to the target product based on the sum of the configuration costs corresponding to the production units.
Optionally, the step of determining the transportation cost between the production units comprises:
determining the carrying distance, unit carrying cost and material carrying capacity among all production units in the production process of the target product;
Calculating and determining the transportation cost among the production units based on the product among the transportation distance among the production units, the unit transportation cost among the production units and the material transportation volume among the production units;
and calculating and determining the transportation cost between the production units corresponding to the target product based on the transportation cost between the production units.
Optionally, the step of determining the optimal scheduling sequence of the intelligent handling trolley based on a preset cooperative scheduling algorithm according to the starting point and the finishing point corresponding to the intelligent handling trolley and the positions and the capacities of the upper and lower feed inlets of the production units includes:
determining the positions and capacities of the feeding and discharging openings of all production units corresponding to the target product, and constructing an optimizing objective function of the collaborative scheduling algorithm based on the positions and capacities of the feeding and discharging openings of all production units, wherein the optimizing objective function is characterized by the minimum time consumed by an optimal path of each intelligent carrying trolley, and the collaborative scheduling algorithm is a genetic algorithm;
And calling a preset genetic algorithm, carrying out genetic optimization based on the optimizing objective function, and outputting the optimal scheduling sequence of the intelligent carrying trolley when the preset iteration times are reached through crossing and mutation.
Optionally, the optimization algorithm of the loading and unloading port is a genetic particle swarm mixing algorithm.
A discrete manufacturing shop scheduling control device according to another object of the present application comprises:
The initialization module is used for responding to the feeding port and discharging port optimization instruction, and initializing each particle in the particle population based on a preset feeding port and discharging port optimization algorithm, wherein the particle population represents a selection strategy of the feeding port and discharging port positions and the capacities of each production unit corresponding to a target product;
The objective function determining module is used for determining the configuration cost of the upper and lower feed inlets of the production units, the transportation cost among the production units, the yield constraint condition of the production units and the production period constraint condition corresponding to the target product, and calculating and determining the objective function value of the individual solution in the particle population based on the configuration cost, the transportation cost, the yield constraint condition and the production period constraint condition;
The new individual generation module is used for selecting an individual solution with high probability to enter a next generation particle population based on the objective function value of the individual solution, randomly generating an intersection point for the selected individual, and exchanging the two individuals at the intersection point to generate a new individual;
The optimal strategy generation module is set to determine a mutated new gene by adopting genetic algorithm boundary mutation according to the new individual, the mutated new gene is added into the particle population, the speed and the position of each particle in the particle population are updated, and when a preset iteration condition is reached, the particle with the minimum objective function value is used as an optimal selection strategy of the position and the capacity of the upper and lower feed inlets of each production unit corresponding to a target product;
The intelligent trolley scheduling module is configured to respond to an instruction for scheduling the intelligent trolley, determine a starting point and an ending point corresponding to each intelligent trolley, determine the position and the capacity of the feeding and discharging openings of each production unit according to the optimal selection strategy, and determine the optimal scheduling sequence of the intelligent trolley according to the starting point and the ending point corresponding to the intelligent trolley and the position and the capacity of the feeding and discharging openings of each production unit based on a preset cooperative scheduling algorithm so as to complete the scheduling control of a discrete manufacturing workshop.
An electronic device adapted for another object of the application comprises a central processor and a memory, said central processor being adapted to invoke the steps of running a computer program stored in said memory for performing the discrete manufacturing shop scheduling control method of the application.
A computer-readable storage medium adapted to another object of the present application stores, in the form of computer-readable instructions, a computer program implemented according to the discrete manufacturing shop scheduling control method, which when invoked by a computer, performs the steps comprised by the corresponding method.
Compared with the prior art, the application aims at solving the problems of low production efficiency, high production cost and the like caused by shortage of materials, unreasonable layout of a production line, equipment failure and the like in the manufacturing process of a product in a discrete manufacturing workshop in the prior art, and comprises the following beneficial effects:
Firstly, the application can obviously improve the positions and capacities of the feeding and discharging openings of each production unit in the product manufacturing process, thereby ensuring the rationality of the production line layout of the target product, greatly reducing the failure rate of equipment, ensuring the production safety of the target product to be orderly carried out, greatly improving the production efficiency and saving the production cost by improving the rationality of the production line layout of the target product, and further improving the benefit of enterprises;
Secondly, determining the optimal scheduling sequence of the intelligent carrying trolley based on the positions and the capacities of the feeding and discharging openings of all production units in an optimal layout strategy, wherein the optimal scheduling sequence can be automatically adjusted in a real-time self-adaptive manner according to the speed and the running path of the intelligent carrying trolley, so that the optimal scheduling sequence is realized, the intelligent carrying trolley is prevented from being in a congestion state, tasks are completed in the shortest time by outputting the optimal scheduling sequence of the intelligent carrying trolley, and the maximization of the overall efficiency is ensured;
Thirdly, by cooperatively scheduling the intelligent carrying trolley, not only is the efficient scheduling under the condition of single trolley considered, but also the complex scene of the cooperative operation of multiple trolleys is considered, so that the intelligent adaptation to different factories, warehouses and other scenes is realized, and a more efficient and intelligent automatic solution is brought to the manufacturing industry;
Fourth, according to real-time road conditions and task queue conditions, the running path and speed of the intelligent carrying trolley are dynamically adjusted and optimized, congestion is avoided, tasks are guaranteed to be completed timely, multi-trolley cooperative scheduling is achieved under the condition that multiple vehicles run simultaneously, collision and intersection are avoided, the running path is optimized to maximize the whole efficiency, and production efficiency can be greatly improved.
Drawings
The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of a method for controlling the dispatch of a discrete manufacturing shop in an embodiment of the application;
FIG. 2 is a schematic flow chart of determining the configuration cost of the loading and unloading ports of the production unit of the target product according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of determining transportation costs between production units according to an embodiment of the present application;
FIG. 4 is a flow chart of determining objective function values of individual solutions in a particle population according to an embodiment of the present application;
FIG. 5 is a schematic flow chart of a genetic algorithm boundary variation determination method for determining a mutated gene according to a new individual in the embodiment of the application;
FIG. 6 is a schematic flow chart of determining an optimal scheduling sequence of an intelligent transportation cart according to an embodiment of the present application;
FIG. 7 is a diagram of a plant model for multi-vehicle cooperative operation in an embodiment of the present application;
FIG. 8 is a schematic block diagram of a discrete manufacturing shop scheduling control device in an embodiment of the application;
Fig. 9 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the application.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. The term "and/or" as used herein includes all or any element and all combination of one or more of the associated listed items.
It will be understood by those skilled in the art that all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs unless defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
As used herein, "client," "terminal device," and "terminal device" are understood by those skilled in the art to include both devices that include only wireless signal receivers without transmitting capabilities and devices that include receiving and transmitting hardware capable of two-way communication over a two-way communication link. Such a device may include: a cellular or other communication device such as a personal computer, tablet, or the like, having a single-line display or a multi-line display or a cellular or other communication device without a multi-line display; PCS (Personal Communications Service, personal communications System) that may combine voice, data processing, facsimile and/or data communications capabilities; PDA (Personal DIGITAL ASSISTANT ) that may include a radio frequency receiver, pager, internet/intranet access, web browser, notepad, calendar and/or GPS (Global Positioning System ) receiver; a conventional laptop and/or palmtop computer or other appliance that has and/or includes a radio frequency receiver. As used herein, "client," "terminal device" may be portable, transportable, installed in a vehicle (aeronautical, maritime, and/or land-based), or adapted and/or configured to operate locally and/or in a distributed fashion, at any other location(s) on earth and/or in space. As used herein, a "client," "terminal device," or "terminal device" may also be a communication terminal, an internet terminal, or a music/video playing terminal, for example, may be a PDA, a MID (Mobile INTERNET DEVICE ), and/or a Mobile phone with a music/video playing function, or may also be a device such as a smart tv, a set top box, or the like.
The application refers to hardware such as a server, a client, a service node, and the like, which essentially is an electronic device with personal computer and other functions, and is a hardware device with necessary components disclosed by von neumann principles such as a central processing unit (including an arithmetic unit and a controller), a memory, an input device, an output device, and the like, wherein a computer program is stored in the memory, and the central processing unit calls the program stored in the memory to run, executes instructions in the program, and interacts with the input and output devices, thereby completing specific functions.
It should be noted that the concept of the present application, called "server", is equally applicable to the case of server clusters. The servers should be logically partitioned, physically separate from each other but interface-callable, or integrated into a physical computer or group of computers, according to network deployment principles understood by those skilled in the art. Those skilled in the art will appreciate this variation and should not be construed as limiting the implementation of the network deployment approach of the present application.
One or more technical features of the present application, unless specified in the clear, may be deployed either on a server for implementation and the client remotely invokes an online service interface provided by the acquisition server for implementation of the access, or may be deployed and run directly on the client for implementation of the access.
The neural network model cited or possibly cited in the application can be deployed on a remote server and can be used for implementing remote call on a client, or can be deployed on a client with sufficient equipment capability for direct call, unless specified by plaintext, and in some embodiments, when the neural network model runs on the client, the corresponding intelligence can be obtained through migration learning so as to reduce the requirement on the running resources of the hardware of the client and avoid excessively occupying the running resources of the hardware of the client.
The various data related to the present application, unless specified in the plain text, may be stored either remotely in a server or in a local terminal device, as long as it is suitable for being invoked by the technical solution of the present application.
Those skilled in the art will appreciate that: although the various methods of the present application are described based on the same concepts so as to be common to each other, the methods may be performed independently of each other unless specifically indicated otherwise. Similarly, for the various embodiments disclosed herein, all concepts described herein are presented based on the same general inventive concept, and thus, concepts described herein with respect to the same general inventive concept, and concepts that are merely convenient and appropriately modified, although different, should be interpreted as equivalents.
The various embodiments of the present application to be disclosed herein, unless the plain text indicates a mutually exclusive relationship with each other, the technical features related to the various embodiments may be cross-combined to flexibly construct a new embodiment as long as such combination does not depart from the inventive spirit of the present application and can satisfy the needs in the art or solve the deficiencies in the prior art. This variant will be known to the person skilled in the art.
The discrete manufacturing workshop scheduling control method can be based on a discrete manufacturing system simulation and optimization platform, the discrete manufacturing system simulation and optimization platform adopts a functional modularization design idea, the simulation platform adopts a functional modularization design through an efficient algorithm and an advanced simulation technology, and the simulation platform can realize other functional expansion by adopting a data transmission interface module, a raw material workshop workpiece production module, a task assignment module and driving task execution module, a production unit upper and lower feed opening layout module, an intelligent transport trolley (AGV trolley) path planning module, an intelligent transport trolley (AGV trolley) parking lot module, a process data management module, a statistics module and the like.
In some embodiments, the data transfer interface module enables the Matlab program to establish communications with the discrete manufacturing system simulation platform (Tecnomatix Plant Simulation) using TCP protocols and Socket sockets through specific IP addresses and network ports. The method has the function of realizing the two-way data transmission of byte data required in the simulation process through the network port, ensuring the realization of the simulation verification function and the process optimization function, and simultaneously realizing the expansion of a software interface to enable various required software to be accessed to a discrete manufacturing system simulation platform (Tecnomatix Plant Simulation).
The byte data sent to the discrete manufacturing system simulation platform (Tecnomatix Plant Simulation) by Matlab is provided with a starting instruction, initial layout configuration of a loading and unloading port (P/D port) of a production unit, quantity configuration of intelligent carrying small intelligent carrying dollies (AGV dollies), capacity configuration of the loading and unloading port (P/D port) of the production unit and new optimized layout configuration of the loading and unloading port (P/D port). In addition, other software can access the simulation verification and optimization functions of the simulation platform through Socket sockets.
The byte data sent to Matlab by the discrete manufacturing system simulation platform (Tecnomatix Plant Simulation) has simulation results, congestion rate data and layout configuration of a production unit loading and unloading port (P/D port). The simulation result comprises simulation start time, simulation end time, simulation time consumption, simulation process index data and objective function values. The objective function value comprises no-load transportation time of the intelligent transportation trolley (AGV trolley), feeding and discharging time consumption and congestion time of the intelligent transportation trolley (AGV trolley).
In some embodiments, the workpiece production module of the raw material workshop mainly has the functions of simulating raw materials randomly arriving at random and in an irregular quantity, and automatically classifying and preliminarily processing according to the types of the raw materials to generate semi-finished products. And simulating a material preliminary treatment workshop under a real scene, feeding the material into a workpiece buffer area for processing, then conveying the material to a loading buffer area, conveying a pallet from the pallet buffer area to the loading buffer area, conveying the workpiece and the pallet to a buffer area to be processed after the loading buffer area finishes loading, conveying the workpiece and the pallet to an outlet, and waiting for a corresponding intelligent carrying trolley (AGV trolley) to be conveyed to a production unit.
In some embodiments, the parking lot module of the intelligent transportation vehicle (AGV) comprises a parking space of the intelligent transportation vehicle (AGV) formed by a channel and a '匚' space, and the parking space can be increased or decreased according to actual conditions, so that the number of the intelligent transportation vehicles (AGV) and the dispatching rule of the intelligent transportation vehicles (AGV) are optimized. And setting a Task list Task of the parking lot, wherein the Task list Task is used for storing Task assignment and an assignment Task queue generated by a driving execution module. And setting an intelligent carrying trolley (AGV trolley) Detail table for storing idle intelligent carrying trolley (AGV trolley) vehicles and preparing for task allocation and driving an execution module. The intelligent handling trolley (AGV trolley) table is set and used for counting the states of all intelligent handling trolley (AGV trolley) vehicles, including waiting time, load transportation time, load congestion time, no-load walking time, no-load congestion time, unit feeding time and unit discharging time in a parking lot.
In some embodiments, the production unit loading and unloading port layout module sends Matlab to the production unit loading and unloading port (P/D port) initial layout scheme configuration or the production unit loading and unloading port (P/D port) new optimization layout scheme configuration of the discrete manufacturing system simulation platform (Tecnomatix Plant Simulation), and after analysis, the sensor is used for marking and layout of the loading and unloading port of the simulation optimization platform for positioning, timing and path planning of the intelligent transport trolley (AGV trolley).
The production unit has the following two conditions that the upper and lower material loading ports are required to be placed along the road, and if the production unit has only the upper and lower sides or the left and right sides, the production unit has only 6 upper and lower material loading port (P/D port) layout schemes, for example, the upper and lower side road conditions are that the D port is arranged below and the P port is arranged above respectively; the D port is arranged on the upper part, and the P port is arranged on the lower part; the D port is at the upper left and the P port is at the upper right; the D port is on the upper right, and the P port is on the upper left; the D port is at the lower left and the P port is at the lower right; the D port is at the lower right and the P port is at the lower left.
If the adjacent road sections of the production units are provided with unidirectional openings, 12 layout schemes of upper and lower material openings (P/D openings) are provided, for example, 匚 -shaped roads are distributed, besides the 6 types, D openings are arranged on the upper side, and the P openings are arranged on the left side; the port D is on the left, and the port P is on the upper; the D port is at the upper left, and the P port is at the lower left; the D port is at the lower left, and the P port is at the upper left; the D port is on the left, and the P port is on the lower; the D port is down and the P port is left. All layouts of all production cells need to be written in PosPDPoint in advance as variable fields.
For the initial layout scheme configuration of the loading and unloading port (P/D port): firstly, verifying whether a scheme is in a variable domain, and carrying out corresponding processing according to different categories. If the solution exceeds the variable domain, the objective function value is assigned to infinity, and information that the solution is not feasible is returned. And then creating corresponding loading and unloading ports (P/D ports) according to different categories. For a D-port sensor, the ID of the sensor, the position of the sensor on the path, and the specific coordinate position of the sensor are recorded. If the road where the sensor is located is a double channel, creating a conjugate sensor of another road; for a P-port sensor, the ID of the sensor, the position of the sensor on the path, and the specific coordinate position of the sensor are recorded. If the road on which the sensor is located is two channels, a conjugate sensor of another channel is created, and finally, the current unit feeding and discharging opening (P/D opening) scheme is recorded in the last row of the PosPDPoint table.
Configuration of a new optimized layout scheme for a loading and unloading port (P/D port): firstly, verifying whether a scheme is in a variable domain, and carrying out corresponding processing according to different categories. If the solution exceeds the variable domain, the objective function value is assigned to infinity, and information that the solution is not feasible is returned. If the new scheme is in the variable domain, deleting the sensor layout of the original production unit, creating a new sensor layout of a blanking port (D port) and a sensor layout of a loading port (P port), calculating the total number of the blanking ports (P/D ports) on the relevant road section, and recording the current scheme of the unit loading and blanking ports (P/D ports), wherein the total number of the two sensors in the buffer area of the feeding workshop is not calculated, and the total number of the sensors on the conjugate channel is calculated.
In some embodiments, in the Task assignment and driving Task execution module, when the starting materials randomly arriving at an irregular time and an irregular amount are initially classified and processed into semi-finished workpieces, a workpiece carrying Task allocation program is executed first, and the carrying starting position, the target position and the carrying Task plan are stored in a parking lot Task table Task according to the principle of 'first in first out' of a queue. And then executing a transportation Task program (TaskExe), distributing corresponding trolleys, and executing tasks of the Task list Task of the parking lot according to the principle of 'first-in first-out' of the queue.
For the task assignment generation section, the specific procedure is as follows:
If the event of carrying the semi-finished workpiece is triggered, generating the type, the number, the starting position and the destination address of the carrying workpiece into a carrying Task, and storing the carrying Task in a parking lot Task table Task according to the principle of 'first-in first-out' of a queue;
If the trigger is a discharging port (D port) leaving event corresponding to each generating unit, indicating that the unit is processed, receiving a tray of the previous generating unit, firstly analyzing processing information of the generating unit, generating the type, the number, the starting position and the destination address of the processed workpiece into a carrying Task, and storing the carrying Task in a parking lot Task table Task according to the principle of 'first-in first-out' of a queue; if the P-port tray entering event corresponding to each generating unit is triggered, the processing information of the production unit is analyzed, the type, the number, the starting position and the destination address of the processed workpieces are generated into a carrying Task, and the carrying Task is stored in a parking lot Task table Task according to the principle of 'first-in first-out' of a queue.
And executing a task part for driving an intelligent carrying trolley (AGV trolley):
The first situation is that for a Task not executed in a Task table Task of a parking lot, firstly, an intelligent carrying trolley (AGV trolley) in an idle state in the parking lot is required to be searched through traversal, then a Task instruction is distributed to the intelligent carrying trolley (AGV trolley), the running speed of the intelligent carrying trolley (AGV trolley) is initialized, the Task state of the intelligent carrying trolley (AGV trolley) in a table at the position of the parking lot is updated, and the Task is started to be executed, wherein the Task instruction refers to path pointing by taking a feed opening (P/D opening) on each production unit as a starting point or a destination point.
The second condition is that for the task in execution, related programs of the path planning module are automatically called under the action of the sensor, the task is continuously executed, and the task is returned to the parking lot after the task is executed. If no task exists, no operation is performed.
In some embodiments, a shop with multiple ring nested field arrangements may accommodate more production units within a limited field area to achieve greater productivity by an intelligent transport cart (AGV) path planning module. However, often because the work piece needs to be processed through a plurality of production units, the intelligent handling trolley (AGV trolley) of many delivery work pieces needs simultaneous operation, and unreasonable, the jam scheduling problem of intelligent handling trolley (AGV trolley) route planning can appear, leads to the whole production efficiency in workshop to reduce, causes the production bottleneck, and consequently the optimization of intelligent handling trolley (AGV trolley) route planning is vital.
The intelligent transport trolley (AGV trolley) path planning module is a key component of an automatic navigation vehicle system, and realizes efficient scheduling and path planning of single or multiple trolleys by combining specific sensors, task demands and real-time road condition information.
Under the condition of a single trolley, a sensor control program (SensorCtrl) can judge whether to stop at a corresponding production unit feeding and discharging opening (a feeding and discharging opening (P/D opening)) according to signals of a specific sensor, and then judge whether to carry out feeding or discharging operation according to carrying task information of an intelligent carrying trolley (AGV), wherein the feeding operation comprises two conditions:
Firstly, conveying raw material workpieces to each production unit on a production line or conveying the workpieces which are finished in a certain production unit to another production unit, wherein the production unit can send a feeding request to a parking lot module of an intelligent carrying trolley (AGV trolley), the intelligent carrying trolley (AGV trolley) leaves a parking lot after receiving a feeding Task instruction assigned by a parking lot Task table Task, and the raw material workpieces are conveyed to a raw material workpiece production workshop to load raw materials or to a feeding port of a specific production unit according to carrying Task information, and whether the raw materials or workpieces to be processed reach the corresponding feeding port is judged through a sensor control program (SensorCtrl) and are placed into the designated feeding port;
Secondly, in the production process, if the raw materials or the machined parts of a certain production unit are insufficient, a material supplementing task is sent to a parking lot module of an intelligent carrying trolley (AGV), after the intelligent carrying trolley (AGV) is assigned to execute the material supplementing task, the intelligent carrying trolley is sent to a raw material generating workshop or a material feeding port of a corresponding production unit according to carrying task information, whether the raw materials or the machined parts reach the material feeding port is judged through a sensor control program (SensorCtrl), and the raw materials or the machined parts to be machined are placed into the specified material feeding port.
The blanking operation comprises the following four conditions:
When the production line finishes the production of finished products, the finished product materials need to be discharged from the production line, the production unit sends a discharging task to an intelligent carrying trolley (AGV trolley) parking lot module, after the intelligent carrying trolley (AGV trolley) is assigned to execute the discharging task, the intelligent carrying trolley is sent to a discharging opening of the production unit according to carrying task information, whether the finished product materials reach the discharging opening of the production unit is judged through a sensor control program (SensorCtrl), and finished product workpieces are taken out from the discharging opening and conveyed to a designated position;
Secondly, when a certain number of semi-finished workpieces are reached or need to be emptied, the production unit sends a blanking task to a parking lot module of an intelligent carrying trolley (AGV trolley), after the intelligent carrying trolley (AGV trolley) is assigned to execute the corresponding task, the intelligent carrying trolley (AGV trolley) goes to a blanking port of the production unit according to carrying task information, whether the semi-finished workpieces reach the blanking port is judged through a sensor control program (SensorCtrl), the semi-finished workpieces are taken out from the blanking port of the production unit, and the semi-finished workpieces are conveyed to a production unit of a next production unit;
Thirdly, when the materials need to be reworked, the production unit can request a material reworking task to a parking lot module of an intelligent carrying trolley (AGV trolley), after the intelligent carrying trolley (AGV trolley) is assigned to execute a reworking material blanking instruction, the intelligent carrying trolley (AGV trolley) goes to a blanking port of the production unit according to the information of the assigned task, whether the material reaches the blanking port is judged through a sensor control program (SensorCtrl), and after the material reaches the blanking port, the intelligent carrying trolley (AGV trolley) can take the material out of the blanking port and re-carry the material to a proper production unit of the production unit;
Fourth, when need follow production line unloading with defective products or waste product, the production line can send the waste product unloading task to intelligent handling dolly (AGV dolly) parking area module, and after intelligent handling dolly (AGV dolly) was assigned waste product unloading task instruction, the waste product unloading mouth on the production line was gone forward to according to the information of assigned task, judges whether to reach the waste product unloading mouth through sensor control program (SensorCtrl), after reaching, intelligent handling dolly (AGV dolly) dolly can take out the waste product from the unloading mouth to carry specific place according to the information of assigned task.
The intelligent carrying trolley (AGV trolley) driving program (Drive) can Drive the intelligent carrying trolley to perform feeding or discharging operation, record feeding and discharging time length, idle time length and load time length of the intelligent carrying trolley (AGV trolley), wait for a certain feeding and discharging time interval or Drive the trolley to move along a pre-planned path according to a task completion state;
the intelligent carrier (AGV) path control program RSCtrl is responsible for analyzing road conditions in real time according to the destination assigned by the task, and selecting the fastest and nearest paths to ensure efficient completion of the task.
In the case that multiple trolleys are running simultaneously and congestion is likely to occur, the system automatically performs real-time self-adaptive adjustment of the speed and the running path of the intelligent carrying trolley (AGV trolley). Through real-time supervision vehicle position and road conditions, intelligent handling dolly (AGV dolly) route planning module can in time discern the region of congestion to for every car dynamic planning best route, accomplish the task in the shortest time, guarantee that overall efficiency is maximize.
The design of the path planning module not only considers the efficient scheduling under the single-vehicle condition, but also considers the complex scene of the cooperative operation of multiple vehicles, thereby realizing the intelligent adaptation to the scenes of different factories, warehouses and the like and bringing more efficient and intelligent automatic solutions for the logistics industry.
In some embodiments, in the process data management module, the module records the problems occurring in the simulation process and the state of the workshop, where the problems include the number of congestion times and congestion rate of each intelligent road handling trolley (AGV trolley), the number of entering times, and the change of the number of corresponding production units and loading and unloading ports (P/D ports) along with the update of the layout; the state of workshop includes the number of trays to be distributed of raw materials workshop discharge gate, the position of intelligent handling dolly (AGV dolly) in the parking area, the state of feed opening (P/D mouth) on the production unit, the task of waiting to distribute, each production unit real-time tray quantity. The process data management module sends related data to other software through a Socket interface, and the other software can perform corresponding optimization processing according to the data.
In some embodiments, in the statistics module, the calculated waiting time, load transportation time, load congestion time, no-load transportation time, no-load congestion time, loading time and unloading time of each intelligent transportation trolley (AGV) in a parking lot are calculated, so that the average waiting time, average load transportation time, average load congestion time, average no-load transportation time, average no-load congestion time, loading time and unloading time of each intelligent transportation trolley (AGV) in the parking lot, and the total average amount and average amount per day of each intelligent transportation trolley (AGV) are obtained, and the change of a variable domain, simulation start time, simulation consumption time and an objective function value are calculated, wherein the objective function value comprises the intelligent transportation trolley (AGV) transportation time, the intelligent transportation trolley (AGV) load transportation time, loading and unloading consumption time and the intelligent transportation trolley (AGV) congestion time. The statistics module returns information through the Socket interface, and other software can make corresponding optimization processing.
In some embodiments, the functionality and flexibility of the existing innovative discrete manufacturing system simulation and optimization platform can be further extended to meet changing needs and application scenarios. The scalability can be realized in the following way:
And other software is accessed to the platform, namely the accessibility of the platform is further expanded through the realized data transmission interface module, so that more types of software can interact with the simulation platform. This means that, not limited to Matlab and discrete manufacturing system simulation platform (Tecnomatix Plant Simulation), other production planning, logistics management or data analysis software can be accessed through Socket interfaces, thus realizing wider data sharing and system integration.
And secondly, other functional modules can be added to the platform according to actual needs, namely new functional modules are added to the platform so as to cope with the continuously developed manufacturing requirements and technical challenges. For example, the introduction of more advanced path planning algorithms or intelligent scheduling algorithms may be considered to optimize production flows and resource utilization. In addition, functional modules related to the emerging technologies such as the Internet of things and artificial intelligence can be added, so that the intelligent and self-adaptive capacity of the system can be further improved.
And thirdly, the parking space of the intelligent carrying trolley (AGV trolley) parking lot module is expandable, namely, the intelligent carrying trolley (AGV trolley) parking lot module is expanded, and the dynamic intelligent carrying trolley (AGV trolley) parking space management and optimization are supported. The method comprises the steps of flexibly increasing and decreasing the number of parking spaces of the intelligent transport trolley (AGV) according to actual demands, and optimizing scheduling rules and parking strategies of the intelligent transport trolley (AGV) according to real-time data and predictive analysis. Meanwhile, through the state quantity expansion of the intelligent carrying trolley (AGV trolley) real-time state display record table, the vehicle state of the intelligent carrying trolley (AGV trolley) is monitored more carefully and comprehensively, and therefore manageability and operation efficiency of the system are further improved.
With reference to the above exemplary scenario and referring to fig. 1, in one embodiment, the discrete manufacturing shop scheduling control method of the present application comprises:
step S10, responding to an upper and lower feed opening optimization instruction, and initializing each particle in a particle population based on a preset upper and lower feed opening optimization algorithm, wherein the particle population represents a selection strategy of the upper and lower feed opening position and capacity of each production unit corresponding to a target product;
The production unit loading and unloading port layout module in the discrete manufacturing system simulation and optimization platform can respond to loading and unloading port optimization instructions, each particle in a particle population is initialized based on a preset loading and unloading port optimization algorithm, the loading and unloading port optimization algorithm is a genetic particle swarm mixing algorithm, the particle population represents a selection strategy of loading and unloading port positions and capacities of each production unit corresponding to a target product, and it is easy to understand that the particles in the particle population are the loading and unloading port positions and capacities corresponding to each production unit.
Specifically, the positions and capacities of the loading and unloading ports of the respective production units in the discrete manufacturing workshops are optimized based on a genetic particle swarm hybrid algorithm, which takes the positions and Rong Liangkan of the loading and unloading ports (P/D ports) of the respective production units in the discrete manufacturing workshops as a genetic code, and then new solutions are continuously generated through operations such as crossing, mutation, selection, update speed, update position, capacity and the like until termination conditions are met or the maximum number of iterations is reached, so that the optimal loading and unloading port (P/D port) positions and capacity are found under consideration of the transportation costs and capacity configuration costs of the production units.
More specifically, the production unit loading and unloading port layout module in the discrete manufacturing system simulation and optimization platform may call a pseudo-monte carlo method in a genetic particle swarm mixing algorithm to initialize the particle swarm, and randomly generate a set of solutions by using the pseudo-monte carlo method, where each solution represents a position and a capacity of a loading and unloading port, and the position and the capacity of the loading and unloading port are as followsWherein, the method comprises the steps of, wherein,Represent the firstThe solution is that,Represent the firstIn the solution of the firstThe position and the capacity of the upper and lower feed inlets.
Step S20, determining configuration cost of a loading and unloading port of a production unit corresponding to the target product, transportation cost among the production units, yield constraint conditions of the production units and production cycle constraint conditions, and calculating and determining objective function values of individual solutions in the particle population based on the configuration cost, the transportation cost, the yield constraint conditions and the production cycle constraint conditions;
After each particle in a particle population is initialized based on a preset loading and unloading port optimization algorithm, determining the configuration cost of loading and unloading ports of production units corresponding to the target product, the transportation cost among the production units, the yield constraint condition of the production units and the production period constraint condition, and calculating and determining the objective function value of an individual solution in the particle population based on the configuration cost, the transportation cost, the yield constraint condition and the production period constraint condition;
In some embodiments, referring to fig. 2, the step of determining the configuration cost of the loading and unloading ports of the production unit of the target product includes:
Step S201, determining the capacity of the loading and unloading ports of each production unit of a target product and unit configuration cost;
step S203, calculating and determining the corresponding configuration cost of each production unit based on the product of the capacity of the feeding opening and the unit configuration cost of each production unit;
Step 205, calculating and determining the configuration cost of the upper and lower feed ports of the production units corresponding to the target product based on the sum of the configuration costs corresponding to the production units.
Specifically, the configuration cost of the loading and unloading ports of the production unit corresponding to the target product mainly depends on the number and capacity of the loading and unloading ports, and the number of the loading and unloading ports is assumed to beThe capacity of the feeding and discharging opening isThe unit configuration cost isThe configuration cost of the loading and unloading ports of the production units corresponding to the target products is as follows。
In some embodiments, referring to fig. 3, the step of determining the cost of transportation between production units includes:
Step S2001, determining a handling distance, a unit handling cost and a material handling volume between each production unit in the production process of the target product;
Step S2003, calculating and determining the transportation cost between the production units based on the product of the transportation distance between the production units, the unit transportation cost between the production units and the material transportation amount between the production units;
step S2005, calculating and determining the transportation cost between the production units corresponding to the target product based on the transportation cost between the production units.
Specifically, the transportation cost between the production units corresponding to the target product depends on the transportation distance and the transportation amount of the material, and the material is assumed to be transported from the production unitsCarrying to production unitsIs the distance of (2)The unit transportation cost between each production unit isThe material conveying capacity between each production unit isThe transportation cost is。
In some embodiments, the yield constraints of the production unit areWhereinIn order for the minimum throughput requirements to be met,To produce the unitIs a result of the production of (2);
the production cycle constraint conditions of the production units are that WhereinFor the production cycle of the production unit,Is the maximum production cycle;
the capacity constraint condition of the feeding and discharging port is as follows WhereinIs the firstThe capacity of the upper and lower feed inlets,For material from production unitsCarrying to production unitsIs a transport amount of the vehicle;
The objective function of the genetic particle swarm mixing algorithm comprises the transportation cost between production units corresponding to the target product, the configuration cost of the loading and unloading ports of the production units corresponding to the target product, the yield constraint condition of the production units, the production period constraint condition of the production units and the like, and the violation degree of each individual in the particle swarm to the constraint condition is included into the objective function to punish the individual violating the constraint condition, namely the objective function is expressed as follows:
，
Wherein, AndIs a penalty coefficient.
Calculating objective function values for each individual solution in a particle populationDegree of violation of yield constraints by each individualDegree of violation of cycle constraintsAccording to the objective function value of each individual solutionDegree of violation of yield constraints by each individualDegree of violation of cycle constraintsJudging whether each individual in the particle population violates the capacity constraint of the loading and unloading openings of each production unit, and if so, continuing to execute the next step.
Further, referring to fig. 4, the step of calculating and determining the objective function value of the individual solution in the particle population based on the configuration cost, the transportation cost, the yield constraint, and the production cycle constraint includes:
step S100, acquiring production operation time of an intelligent carrying trolley among production units in the production process of a target product, wherein the production operation time comprises one or more of parking lot waiting time, load transportation time, load congestion time, no-load walking time, no-load congestion time, loading time and unloading time;
step 200, calculating and determining objective function values of individual solutions in the particle population based on the production operation time, the configuration cost of the loading and unloading openings of the production units, the transportation cost among the production units, the yield constraint condition of the production units and the production cycle constraint condition.
Specifically, parking lot waiting time, load transportation time, load congestion time, no-load walking time, no-load congestion time, loading time, unloading time and the like of the intelligent carrying trolley among all production units are used as objective function values of individual solutions in the particle population, so that rationality of optimization of positions and capacities of loading and unloading openings corresponding to all production units of a target product can be remarkably improved, production efficiency of the target product can be greatly improved, and meanwhile production cost is greatly saved.
Step S30, based on the objective function value of the individual solution, selecting the individual solution with high probability to enter a next generation particle population, randomly generating an intersection for the selected individual, and exchanging the two individuals at the intersection to generate a new individual;
After determining the objective function value of the individual solution in the particle population, selecting the individual solution with larger probability to enter the next generation particle population based on the objective function value of the individual solution, randomly generating an intersection point for the selected individual, and exchanging the two individuals at the intersection point to generate a new individual;
Specifically, objective function values based on individual solutions And constraint conditions, adopting roulette to select, and enabling individual solutions with high selection probability to enter the next generation for cross operation.
，
Wherein the method comprises the steps ofIn order to select the probability of a probability,Is the firstThe objective function value of each solution,Is the firstThe objective function value of each solution,A small number to avoid a denominator of 0.
Further, a cross point is randomly generated for the selected individuals, and two individuals are exchanged at the cross point to generate new individuals, which is specifically expressed as follows:
，
Wherein, For the new solution that is generated after the crossover,Is the first solutionThe positions and the capacities of the upper and lower feed inlets,Is the second solutionThe position and the capacity of the upper and lower feed inlets.
Step S40, determining mutated new genes according to the new individuals by adopting genetic algorithm boundary mutation, adding the mutated new genes into the particle population, updating the speed and the position of each particle in the particle population, and taking the particle with the minimum objective function value as an optimal selection strategy of the position and the capacity of the feeding and discharging port of each production unit corresponding to the objective product when a preset iteration condition is reached;
In some embodiments, referring to fig. 5, determining mutated new genes from the new individuals using genetic algorithm boundary mutation, adding the mutated new genes to the population of particles comprises:
Step S401, generating a random variation value at the boundary of the new individual by adopting genetic algorithm boundary variation;
step S403, replacing the generated mutation value with the gene value at the boundary of the new individual to determine a mutated new gene, and adding the mutated new gene into the particle population.
And determining a mutated new gene according to the mutation of the new individual by adopting a genetic algorithm boundary, generating a random mutation value at the boundary of the new individual by adopting the genetic algorithm boundary mutation, replacing the generated mutation value with the gene value at the boundary of the new individual to determine the mutated new gene, and adding the mutated new gene into the particle population to improve population diversity and convergence rate.
，
For the new value that is generated after the mutation,Is the firstSolution No.The positions of the upper and lower feed inlets and the lower limit of the capacity,Is the firstSolution No.The positions of the upper and lower feed inlets and the upper limit of the capacity.
Further, the speed of each solution is updated, and the speed of each solution is updated according to the particle swarm algorithmIt is represented as follows:
，
Wherein the super parameter As the weight of the inertia is given,AndIn order for the learning factor to be a function of,AndIn the form of a random number,Is the current firstThe solution is that,For individualsThe optimal position and capacity in the current iteration,Is the best position and capacity of the population in the current iteration.
The location and capacity of each set of solutions is updated, which is expressed as follows:
，
Wherein, Optimizing updated first for particle swarmThe group solution is carried out by the method,To the first before updateThe group solution is carried out by the method,A velocity vector of the set of solutions optimized for the population of particles.
Repeating the steps until the maximum iteration times or the minimum objective function value is reached, and taking the particles with the minimum objective function value as the optimal selection strategy of the positions and the capacities of the feeding and discharging openings of the production units corresponding to the objective product.
And S50, responding to an instruction for dispatching the intelligent handling trolleys, determining a starting point and an ending point corresponding to each intelligent handling trolley, determining the position and the capacity of a loading and unloading opening of each production unit according to the optimal selection strategy, and determining the optimal dispatching sequence of the intelligent handling trolleys according to the starting point and the ending point corresponding to the intelligent handling trolleys and the position and the capacity of the loading and unloading opening of each production unit based on a preset collaborative dispatching algorithm so as to finish dispatching control of a discrete manufacturing workshop.
After taking the particles with the minimum objective function value as an optimal selection strategy of the positions and capacities of the loading and unloading openings of all production units corresponding to the target product, the discrete manufacturing simulation and optimization platform can respond to an instruction for scheduling the intelligent transport trolley to determine the corresponding starting point and the corresponding end point of each intelligent transport trolley, determine the positions and capacities of the loading and unloading openings of all production units according to the optimal selection strategy, and determine the optimal scheduling sequence of the intelligent transport trolley according to the corresponding starting point and the corresponding end point of each intelligent transport trolley and the positions and capacities of the loading and unloading openings of all production units based on a preset collaborative scheduling algorithm so as to complete scheduling control of a discrete manufacturing workshop, wherein the collaborative scheduling algorithm can be a genetic algorithm.
Further, referring to fig. 6, the step of determining the optimal scheduling sequence of the intelligent transportation cart based on a preset cooperative scheduling algorithm according to the start point and the end point corresponding to the intelligent transportation cart and the positions and capacities of the loading and unloading openings of the production units includes:
Step S501, determining the positions and capacities of the feeding and discharging openings of all production units corresponding to the target product, and constructing an optimizing objective function of the collaborative scheduling algorithm based on the positions and capacities of the feeding and discharging openings of all production units, wherein the optimizing objective function is characterized by the minimum time consumed by an optimal path of each intelligent carrying trolley, and the collaborative scheduling algorithm is a genetic algorithm;
and S503, calling a preset genetic algorithm, carrying out genetic optimization based on the optimizing objective function, and outputting the optimal scheduling sequence of the intelligent carrying trolley when the preset iteration times are reached through crossing and mutation.
Specifically, in the production process of target products, each intelligent handling trolley (AGV) carries out loading and unloading among production units, and under the condition that a plurality of trolleys run simultaneously and congestion is likely to occur, the position and the capacity of the upper and lower feed inlets of each production unit are determined according to the optimal selection strategy by determining the corresponding starting point and the corresponding end point of each intelligent handling trolley, the optimizing objective function of the collaborative scheduling algorithm is constructed based on the position and the capacity of the upper and lower feed inlets of each production unit, the optimizing objective function is characterized by the minimum time consumed by the optimal path of each intelligent handling trolley, the discrete manufacturing simulation and optimizing platform can call a preset genetic algorithm, genetic optimizing is carried out based on the optimizing objective function, and after crossing and mutation, when the preset iteration times are reached, the optimal scheduling sequence of the intelligent handling trolley is output, the speed and the running path of the intelligent handling trolley can be automatically and adaptively adjusted in real time, the optimal scheduling sequence is realized, the congestion state of the intelligent handling trolley is avoided, the optimal scheduling task is completed in the shortest time, and the whole optimal scheduling efficiency is ensured.
In some embodiments, the collaborative scheduling algorithm further comprises:
Referring to fig. 7, a workshop model diagram is shown in fig. 7, 23 intelligent transport trolley (AGV trolley) tracks are laid in the model diagram, and a single track is defined as Aggregation ofRepresenting a path taken by an intelligent transport cart (AGV cart) receiving an assigned task to complete the task;
after the intelligent transport trolley (AGV trolley) receives the task, firstly, determining a departure track Assume that the track where the intelligent transport cart (AGV cart) needs to reach three working points is、、Returning to the track of the parking lot；
By passing through、If it appearsAndNot on condition, skipSelection ofAs the next track until it appearsAnd so on to determine arrival、、Because the workshop site is in a multi-loop arrangement mode, the same working point can have multiple arrival modes, and the algorithm can generate all path schemes conforming to the assignment tasks according to the mode；
Definition of the definitionFor the total distance of a single path scheme,For the length of a single track,And trackOne-to-one correspondence in which the track is initiallyCorresponding toThe final track isCorresponding toThe total distance is calculated by，The optimal path is selected to save the moving time of the intelligent carrying trolley (AGV trolley) and ensure the maximization of the overall benefit.
In the embodiment, through the cooperative scheduling of the intelligent carrying trolley, not only the efficient scheduling under the single-vehicle condition is considered, but also the complex scene of the cooperative operation of multiple vehicles is considered, the intelligent adaptation to different factories, warehouses and other scenes is realized, and a more efficient and intelligent automatic solution is brought to the logistics industry.
In some embodiments, the number of intelligent transfer dollies (AGV dollies) and the intelligent transfer dollies (AGV dollies) scheduling rules: in the discrete manufacturing system simulation platform (Tecnomatix Plant Simulation), optimization of the number of intelligent handling dollies (AGV dollies) is one of the keys to ensure efficient operation of the production process. The simulation data analysis is carried out by utilizing the data recorded by the process data management module, the simulation data analysis comprises indexes such as waiting time, load transportation time, load congestion time, no-load walking time, no-load congestion time, unit feeding time, unit discharging time and the like of each intelligent carrying trolley (AGV), the demand prediction is carried out according to factory production plans and material supply conditions so as to determine the demand quantity of the intelligent carrying trolley (AGV), then the operation conditions of the intelligent carrying trolleys (AGV) with different quantities are simulated and verified through simulation, the influence of the intelligent carrying trolleys (AGV) on production efficiency, the utilization rate of the intelligent carrying trolleys (AGV) and the like is evaluated, and the quantity of the intelligent carrying trolleys (AGV) with highest production efficiency is selected as an optimization target.
One transport task scheduled by an intelligent transport cart (AGV cart) may be denoted { begin, finish, t }, where begin is the loading point, finish is the unloading point, and t is the arrival time of the transport task. In terms of scheduling, there are three rules, a most recent vehicle priority (NVF) rule, a most recent task priority (NMF) rule, and an earliest task priority (EMF) rule.
If an intelligent transport trolley (AGV trolley) is in an idle state, calculating the path length l of a departure point (begin) by using a recent task priority (NMF) rule, and judging and selecting l_min. When a new task appears in the system, all AGV trolley states are traversed, an idle AGV AGV is screened out, then the distance l_n (n=1, 2 …) from the idle AGV trolley to a new task point is calculated, a circulation algorithm is entered, the comparison is repeated with l_1 as a starting point to l_n, the minimum distance l_min is obtained, l_min is output, then the new task is assigned to the trolley, and the l_min represents the minimum distance from the idle AGV trolley to the new task point. When the arrival time of a certain generated transportation task is later than the current system time, the task assignment module drives the task execution module to operate, and the system time is advanced until the arrival time of the transportation task. When a task arrives, if an idle intelligent transport trolley (AGV trolley) exists, selecting the intelligent transport trolley (AGV trolley) by adopting a recent vehicle priority (NVF) rule;
if the intelligent transport trolley (AGV trolley) is executing other transport tasks, the tasks are cached in a task pool, and a task execution module is driven to propel simulation until an idle intelligent transport trolley (AGV trolley) appears in the system. During which incoming transport tasks are also cached in the task pool.
When an intelligent transport cart (AGV cart) is idle, a specified most recent task priority (NMF) rule or earliest task priority (EMF) is used to select a transport task. In solving the multi-vehicle cooperation aspect, adopting a conflict solution type strategy: when the path conflict occurs, a corresponding processing scheme is tried according to the type of the conflict, if the conflict is judged to be unable to be resolved, the corresponding intelligent carrying trolley (AGV trolley) is terminated; if so, the next path is selected to continue operation.
Optimization of the scheduling rules of intelligent handling dollies (AGV dollies) is a key to ensure smooth operation of the intelligent handling dollies (AGV dollies) in the production process. According to the real-time road conditions and the task queue conditions, the running path and speed of an intelligent transport trolley (AGV trolley) are dynamically adjusted and optimized, congestion is avoided, tasks are guaranteed to be completed timely, multi-trolley cooperative scheduling is achieved under the condition that multiple vehicles run simultaneously, collision and intersection are avoided, and the running path is optimized to maximize the overall efficiency.
In some embodiments, the optimization of the capacity of the loading port/unloading port can be achieved by transmitting constraint conditions and objective functions to other software through the data transmission interface module, optimizing the capacity by using other algorithms such as artificial intelligence algorithm and the like in other software, evaluating the optimization result, checking whether the optimized capacity setting meets the production requirement, and comparing the optimized capacity setting with the original setting to verify the optimization effect.
As can be seen from the foregoing embodiments, compared with the prior art, the present application aims at the problems of low production efficiency and high production cost caused by shortage of materials, unreasonable layout of production lines, equipment failure, etc. in the product manufacturing process of the discrete manufacturing workshops in the prior art, and includes, but is not limited to, the following beneficial effects:
Firstly, the application can obviously improve the positions and capacities of the feeding and discharging openings of each production unit in the product manufacturing process, thereby ensuring the rationality of the production line layout of the target product, greatly reducing the failure rate of equipment, ensuring the production safety of the target product to be orderly carried out, greatly improving the production efficiency and saving the production cost by improving the rationality of the production line layout of the target product, and further improving the benefit of enterprises;
Secondly, determining the optimal scheduling sequence of the intelligent carrying trolley based on the positions and the capacities of the feeding and discharging openings of all production units in an optimal layout strategy, wherein the optimal scheduling sequence can be automatically adjusted in a real-time self-adaptive manner according to the speed and the running path of the intelligent carrying trolley, so that the optimal scheduling sequence is realized, the intelligent carrying trolley is prevented from being in a congestion state, tasks are completed in the shortest time by outputting the optimal scheduling sequence of the intelligent carrying trolley, and the maximization of the overall efficiency is ensured;
Thirdly, by cooperatively scheduling the intelligent carrying trolley, not only is the efficient scheduling under the condition of single trolley considered, but also the complex scene of the cooperative operation of multiple trolleys is considered, so that the intelligent adaptation to different factories, warehouses and other scenes is realized, and a more efficient and intelligent automatic solution is brought to the manufacturing industry;
Fourth, according to real-time road conditions and task queue conditions, the running path and speed of the intelligent carrying trolley are dynamically adjusted and optimized, congestion is avoided, tasks are guaranteed to be completed timely, multi-trolley cooperative scheduling is achieved under the condition that multiple vehicles run simultaneously, collision and intersection are avoided, the running path is optimized to maximize the whole efficiency, and production efficiency can be greatly improved.
Referring to fig. 8, a discrete manufacturing shop scheduling control device provided in accordance with one of the objects of the present application includes an initialization module 1100, an objective function determination module 1200, a new individual generation module 1300, an optimal strategy generation module 1400, and an intelligent car scheduling module 1500. The initialization module 1100 is configured to respond to the loading and unloading port optimization instruction, and initialize each particle in the particle population based on a preset loading and unloading port optimization algorithm, wherein the particle population represents a selection strategy of loading and unloading port positions and capacities of each production unit corresponding to a target product; an objective function determining module 1200 configured to determine a configuration cost of a loading and unloading port of a production unit, a transportation cost between production units, a yield constraint condition of a production unit, and a production cycle constraint condition corresponding to the target product, and calculate and determine an objective function value of an individual solution in the particle population based on the configuration cost, the transportation cost, the yield constraint condition, and the production cycle constraint condition; a new individual generation module 1300 configured to select an individual solution with a larger probability to enter a next generation particle population based on the objective function value of the individual solution, randomly generate an intersection for the selected individual, and exchange the two individuals at the intersection to generate a new individual; the optimal strategy generating module 1400 is configured to determine a mutated new gene according to the mutation of the new individual by adopting a genetic algorithm boundary, add the mutated new gene into the particle population, update the speed and the position of each particle in the particle population, and when a preset iteration condition is reached, take the particle with the minimum objective function value as an optimal selection strategy of the position and the capacity of the loading and unloading mouth of each production unit corresponding to the target product; the intelligent trolley scheduling module 1500 is configured to determine a start point and an end point corresponding to each intelligent trolley in response to an instruction for scheduling the intelligent trolley, determine a position and a capacity of a loading opening and a unloading opening of each production unit according to the optimal selection strategy, and determine an optimal scheduling sequence of the intelligent trolley according to the start point and the end point corresponding to the intelligent trolley and the position and the capacity of the loading and unloading openings of each production unit based on a preset collaborative scheduling algorithm, so as to complete scheduling control of a discrete manufacturing workshop.
On the basis of any embodiment of the present application, please refer to fig. 9, another embodiment of the present application further provides an electronic device, which may be implemented by a computer device, and as shown in fig. 9, the internal structure of the computer device is schematically shown. The computer device includes a processor, a computer readable storage medium, a memory, and a network interface connected by a system bus. The computer readable storage medium of the computer device stores an operating system, a database and computer readable instructions, the database can store a control information sequence, and the computer readable instructions, when executed by a processor, can enable the processor to realize a discrete manufacturing workshop scheduling control method. The processor of the computer device is used to provide computing and control capabilities, supporting the operation of the entire computer device. The memory of the computer device may have stored therein computer readable instructions that, when executed by the processor, cause the processor to perform the discrete manufacturing shop scheduling control method of the present application. The network interface of the computer device is for communicating with a terminal connection. It will be appreciated by persons skilled in the art that the architecture shown in fig. 9 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
The processor in this embodiment is configured to execute specific functions of each module and its sub-module in fig. 8, and the memory stores program codes and various data required for executing the above modules or sub-modules. The network interface is used for data transmission between the user terminal or the server. The memory in this embodiment stores program codes and data required for executing all modules/sub-modules in the discrete manufacturing shop scheduling control device of the present application, and the server can call the program codes and data of the server to execute the functions of all sub-modules.
The present application also provides a storage medium storing computer readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the discrete manufacturing shop scheduling control method according to any of the embodiments of the present application.
The present application also provides a computer program product comprising computer programs/instructions which when executed by one or more processors implement the steps of the discrete manufacturing shop scheduling control method of any of the embodiments of the present application.
Those skilled in the art will appreciate that all or part of the processes implementing the methods of the above embodiments of the present application may be implemented by a computer program for instructing relevant hardware, where the computer program may be stored on a computer readable storage medium, where the program, when executed, may include processes implementing the embodiments of the methods described above. The storage medium may be a computer readable storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (RandomAccess Memory, RAM).
The foregoing is only a partial embodiment of the present application, and it should be noted that it will be apparent to those skilled in the art that modifications and adaptations can be made without departing from the principles of the present application, and such modifications and adaptations are intended to be comprehended within the scope of the present application.
In summary, the application can obviously improve the positions and capacities of the loading and unloading openings of each production unit in the product manufacturing process, thereby greatly reducing the failure rate of equipment, ensuring the production safety and orderly of the target product, greatly improving the production efficiency, saving the production cost and improving the benefit of enterprises.
Claims (10)
1.A method for controlling schedule of a discrete manufacturing facility, comprising:
Initializing each particle in a particle population based on a preset feeding and discharging port optimization algorithm in response to a feeding and discharging port optimization instruction, wherein the particle population represents a selection strategy of the feeding and discharging port position and capacity of each production unit corresponding to a target product;
determining configuration cost of an upper feed opening and a lower feed opening of a production unit corresponding to the target product, transportation cost among the production units, yield constraint conditions of the production units and production cycle constraint conditions, and calculating and determining objective function values of individual solutions in the particle population based on the configuration cost, the transportation cost, the yield constraint conditions and the production cycle constraint conditions;
Based on the objective function value of the individual solutions, selecting the individual solutions with high probability to enter a next generation particle population, randomly generating a cross point for the selected individuals, and exchanging the two individuals at the cross point to generate new individuals;
Determining mutated new genes according to the new individuals by adopting genetic algorithm boundary mutation, adding the mutated new genes into the particle population, updating the speed and the position of each particle in the particle population, and taking the particle with the minimum objective function value as the optimal selection strategy of the position and the capacity of the feeding and discharging port of each production unit corresponding to the objective product when the preset iteration condition is reached;
responding to an instruction for dispatching the intelligent handling trolleys, determining a starting point and a finishing point corresponding to each intelligent handling trolley, determining the position and the capacity of the loading and unloading openings of each production unit according to the optimal selection strategy, and determining the optimal dispatching sequence of the intelligent handling trolleys according to the starting point and the finishing point corresponding to the intelligent handling trolleys and the position and the capacity of the loading and unloading openings of each production unit based on a preset collaborative dispatching algorithm so as to finish dispatching control of a discrete manufacturing workshop.
2. The discrete manufacturing shop scheduling control method according to claim 1, wherein the step of computing an objective function value for determining an individual solution in the population of particles based on the configuration cost, transportation cost, yield constraint, and production cycle constraint, comprises:
Acquiring production operation time of an intelligent carrying trolley among production units in the production process of a target product, wherein the production operation time comprises one or more of parking lot waiting time, load transportation time, load congestion time, no-load walking time, no-load congestion time, loading time and unloading time;
and calculating and determining the objective function value of the individual solution in the particle population based on the production operation time, the configuration cost of the upper and lower feed inlets of the production units, the transportation cost among the production units, the yield constraint condition of the production units and the production period constraint condition.
3. The method of claim 1, wherein determining mutated new genes from the new individuals using genetic algorithm boundary mutation, and adding the mutated new genes to the population of particles comprises:
generating a random variation value at the boundary of the new individual by adopting genetic algorithm boundary variation;
And replacing the generated mutation value with the gene value at the boundary of the new individual to determine a mutated new gene, and adding the mutated new gene into the particle population.
4. The discrete manufacturing shop scheduling control method according to claim 1, wherein the step of determining the configuration cost of the loading and unloading ports of the production unit of the target product comprises:
determining the capacity and unit configuration cost of the loading and unloading ports of each production unit of the target product;
calculating and determining the corresponding configuration cost of each production unit based on the product of the capacity of the feeding opening and the unit configuration cost of each production unit;
And calculating and determining the configuration cost of the upper and lower feed inlets of the production units corresponding to the target product based on the sum of the configuration costs corresponding to the production units.
5. The discrete manufacturing shop scheduling control method according to claim 1, wherein the step of determining the transportation costs between production units comprises:
determining the carrying distance, unit carrying cost and material carrying capacity among all production units in the production process of the target product;
Calculating and determining the transportation cost among the production units based on the product among the transportation distance among the production units, the unit transportation cost among the production units and the material transportation volume among the production units;
and calculating and determining the transportation cost between the production units corresponding to the target product based on the transportation cost between the production units.
6. The method according to claim 1, wherein the step of determining the optimal scheduling order of the intelligent transportation vehicles according to the start point and the end point corresponding to the intelligent transportation vehicles and the positions and capacities of the loading and unloading openings of the respective production units based on a preset cooperative scheduling algorithm comprises:
determining the positions and capacities of the feeding and discharging openings of all production units corresponding to the target product, and constructing an optimizing objective function of the collaborative scheduling algorithm based on the positions and capacities of the feeding and discharging openings of all production units, wherein the optimizing objective function is characterized by the minimum time consumed by an optimal path of each intelligent carrying trolley, and the collaborative scheduling algorithm is a genetic algorithm;
And calling a preset genetic algorithm, carrying out genetic optimization based on the optimizing objective function, and outputting the optimal scheduling sequence of the intelligent carrying trolley when the preset iteration times are reached through crossing and mutation.
7. The discrete manufacturing shop scheduling control method according to claim 1, wherein the loading and unloading port optimization algorithm is a genetic particle swarm mixing algorithm.
8. A discrete manufacturing shop scheduling control device, comprising:
The initialization module is used for responding to the feeding port and discharging port optimization instruction, and initializing each particle in the particle population based on a preset feeding port and discharging port optimization algorithm, wherein the particle population represents a selection strategy of the feeding port and discharging port positions and the capacities of each production unit corresponding to a target product;
The objective function determining module is used for determining the configuration cost of the upper and lower feed inlets of the production units, the transportation cost among the production units, the yield constraint condition of the production units and the production period constraint condition corresponding to the target product, and calculating and determining the objective function value of the individual solution in the particle population based on the configuration cost, the transportation cost, the yield constraint condition and the production period constraint condition;
The new individual generation module is used for selecting an individual solution with high probability to enter a next generation particle population based on the objective function value of the individual solution, randomly generating an intersection point for the selected individual, and exchanging the two individuals at the intersection point to generate a new individual;
The optimal strategy generation module is set to determine a mutated new gene by adopting genetic algorithm boundary mutation according to the new individual, the mutated new gene is added into the particle population, the speed and the position of each particle in the particle population are updated, and when a preset iteration condition is reached, the particle with the minimum objective function value is used as an optimal selection strategy of the position and the capacity of the upper and lower feed inlets of each production unit corresponding to a target product;
The intelligent trolley scheduling module is configured to respond to an instruction for scheduling the intelligent trolley, determine a starting point and an ending point corresponding to each intelligent trolley, determine the position and the capacity of the feeding and discharging openings of each production unit according to the optimal selection strategy, and determine the optimal scheduling sequence of the intelligent trolley according to the starting point and the ending point corresponding to the intelligent trolley and the position and the capacity of the feeding and discharging openings of each production unit based on a preset cooperative scheduling algorithm so as to complete the scheduling control of a discrete manufacturing workshop.
9. An electronic device comprising a central processor and a memory, characterized in that the central processor is arranged to invoke a computer program stored in the memory for performing the steps of the method according to any of claims 1 to 7.
10. A computer-readable storage medium, characterized in that it stores in the form of computer-readable instructions a computer program implemented according to the method of any one of claims 1 to 7, which, when invoked by a computer, performs the steps comprised by the corresponding method.
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