Linear reciprocating type RGV cooperative scheduling method, equipment and medium

Abstract

The embodiment of the invention discloses a linear reciprocating type RGV collaborative scheduling method, equipment and a medium, belongs to the technical field of physical scheduling, and solves the problems that the collaborative operation capability of an existing RGV system is insufficient, and the production efficiency and the resource utilization level are difficult to improve. Comprising the following steps: in response to a transportation demand initiated by a front end, collecting state information of each facility equipment corresponding to an RGV system in a workshop; based on the transportation demand and state information, solving the RGV collaborative scheduling optimization model through a hybrid heuristic algorithm to obtain a target task scheduling scheme; and sending a scheduling instruction to a task executor required by the task based on the target task scheduling scheme so as to control the task executor to complete the transportation task.

Description

Technical Field

[0001] This application relates to the field of logistics scheduling technology, and in particular to a linear reciprocating RGV collaborative scheduling method, equipment and medium. Background Technology

[0002] In current industrial practice, the core of a Warehouse Control System (WCS) lies in its powerful path planning and equipment scheduling capabilities. However, due to the diversity and complexity of workshop resources, data collection and usage become difficult, making it challenging for WCS systems to coordinate and cooperate with Warehouse Management Systems (WMS). Therefore, in practical applications, various resource information is often manually entered, leading to a cumbersome process and a high error rate. Conversely, if WMS information is not used and equipment monitoring is ignored, relying solely on manual experience for decision-making becomes difficult to adapt to complex production environments during busy periods, easily resulting in low resource utilization, resource conflicts, severe production rhythm disruptions, and reduced workshop efficiency.

[0003] Especially in workshops, material loading and unloading tasks are often not prioritized at the same level. To improve machine efficiency, it's advisable to complete some unloading tasks, such as transferring materials to RGV (Rail Guided Vehicle) stations or buffer areas, and then immediately execute loading tasks. However, this approach may lead to problems such as RGV station occupancy shortages, insufficient buffer area capacity, and pallet flow congestion, which in turn hinders loading efficiency and creates a counterproductive dilemma. Moreover, workshop logistics demands fluctuate, with loading and unloading task requests often exhibiting unbalanced peak and trough characteristics. Especially in scenarios with overlapping equipment processing cycles and bulk material arrivals, logistics pressure is further exacerbated, resulting in insufficient collaborative operation capabilities of existing RGV systems and difficulty in improving production efficiency and resource utilization. Summary of the Invention

[0004] This application provides a linear reciprocating RGV collaborative scheduling method, device, and medium to solve the following technical problem: the existing RGV system has insufficient collaborative operation capability, making it difficult to improve production efficiency and resource utilization.

[0005] The embodiments of this application adopt the following technical solutions: This application provides a linear reciprocating RGV collaborative scheduling method. It includes: responding to transportation demands initiated by the front end by collecting status information of each facility and equipment corresponding to the RGV system within the workshop; based on the transportation demands and status information, solving the RGV collaborative scheduling optimization model using a hybrid heuristic algorithm to obtain a target task scheduling scheme; and based on the target task scheduling scheme, sending scheduling instructions to the task executors required by the task to control the task executors to complete the transportation task.

[0006] In one implementation of this application, in response to a transportation request initiated by the front end, the status information of each facility and equipment corresponding to the RGV system in the workshop is collected. Specifically, this includes: responding to the transportation request and obtaining transportation task information; wherein, the transportation task information is triggered by at least one of the following: a new task is initiated, an existing task is canceled, or a task is rescheduled; through a pre-installed software system, the current status information of the facilities and equipment corresponding to the RGV system in the workshop is collected and integrated in real time; wherein, the current status information includes at least one of the following: the status and location of the pallet, the status and location of the material, the platform occupancy status, the buffer area occupancy status, the location and load status of the RGV vehicle, and the attribute information of the newly added transportation task; the integrated status information and transportation task information are stored in a database for RGV optimized scheduling.

[0007] In one implementation of this application, before solving the RGV collaborative scheduling optimization model using a hybrid heuristic algorithm based on transportation demand and status information, the method further includes: constructing an RGV collaborative scheduling optimization model based on pre-set RGV collaborative scheduling constraints and minimizing the task time of the transportation task as the optimization objective; wherein the pre-set RGV collaborative scheduling constraints include at least one of the following: capacity of each facility and equipment, RGV capacity, RGV collision status, pallet availability, and buffer availability.

[0008] In one implementation of this application, a hybrid heuristic algorithm is used to solve the RGV collaborative scheduling optimization model. Specifically, this includes: encoding the solution individuals corresponding to the target task scheduling scheme in three layers; wherein, the first layer encodes the execution order of all task operations, the second layer encodes the service station corresponding to each operation, and the third layer encodes the RGV workstation where each operation is executed; the population individuals composed of the three layers of encoding are optimized and updated by a strategy that combines adaptive large-domain search operators with crossover and mutation operators of genetic algorithms; and the RGV collaborative scheduling optimization model is solved through encoding and optimization updates.

[0009] In one implementation of this application, before performing three-layer encoding on the solution individuals corresponding to the target task scheduling scheme, the method further includes: during the initialization process, calling a preset operation set; wherein the preset operation set includes task operations with completed predecessor operations; and based on the preset operation set and the real-time status information of the current workshop facilities and equipment, randomly generating initial solution individuals that meet resource constraints to construct the initial population of the algorithm.

[0010] In one implementation of this application, a strategy combining adaptive large-domain search operators with crossover and mutation operators of genetic algorithms is used to optimize and update individuals in a population consisting of three layers of encoding. Specifically, this includes: randomly selecting a portion of individuals from the parent population to form a first subset, and the remaining individuals to form a second subset; processing the first subset using an adaptive large-domain search operator according to a preset probability distribution, and retaining the optimized individuals in the offspring population; sequentially performing genetic algorithm operations and repair operations on the second subset to form a third subset; applying a tabu search algorithm to solve for local optimization of individuals in the third subset that meet the triggering conditions; merging the individuals obtained after processing with the adaptive large-domain search operator with the individuals obtained after processing with the tabu search algorithm to obtain a new generation population; repeating the optimization process until the algorithm termination condition is met.

[0011] In one implementation of this application, based on the target task scheduling scheme, a scheduling instruction is sent to the task executor required by the task. Specifically, this includes: parsing the target task scheduling scheme and generating the scheduling instruction; sending the scheduling instruction to the corresponding task executor in real time through a communication network composed of wireless access points; wherein the task executor includes at least one of RGV vehicles, on-line and off-line roller conveyors, and buffer area equipment; and pushing task information requiring manual operation to the terminal devices of relevant operators to drive the collaborative operation of logistics execution units in the workshop.

[0012] In one implementation of this application, after obtaining the target task scheduling scheme, the method further includes: parsing the target task into a transfer task of a preset operation sequence; wherein, the transfer task includes at least one of the following: online task, offline task, rework task, and rework task; the online task is the task of transporting from the online roller conveyor station to the processing machine; the offline task is the task of transporting from the processing machine to the offline roller conveyor station; the rework task is the task of transporting from the processing machine to the rework station; and the rework task is the task of transporting from the rework station to the processing machine.

[0013] This application provides a linear reciprocating RGV collaborative scheduling device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: respond to transportation demands initiated by the front end and collect status information of each facility and equipment corresponding to the RGV system in the workshop; based on the transportation demands and status information, solve the RGV collaborative scheduling optimization model through a hybrid heuristic algorithm to obtain a target task scheduling scheme; and based on the target task scheduling scheme, send scheduling instructions to the task executors required by the task to control the task executors to complete the transportation task.

[0014] This application provides a non-volatile computer storage medium storing computer-executable instructions. The computer-executable instructions are configured to: respond to a transportation request initiated by the front end, collect status information of each facility and equipment corresponding to the RGV system in the workshop; based on the transportation request and status information, solve the RGV collaborative scheduling optimization model through a hybrid heuristic algorithm to obtain a target task scheduling scheme; and based on the target task scheduling scheme, send scheduling instructions to the task executors required by the task to control the task executors to complete the transportation task.

[0015] The at least one technical solution adopted in this application embodiment can achieve the following beneficial effects: This application embodiment responds to front-end customer needs and collects the status information of each resource entity in the back-end. Then, it realizes resource scheduling and path planning through a scheduling hybrid heuristic algorithm, and communicates the algorithm results with each facility and equipment to issue instructions, thereby realizing intelligent and automated decision-making in the workshop. The calculation results can quickly provide the scheduling sequence of each operation, the specific operation platform and workstation allocation, and specific obstacle avoidance measures, and automatically distribute them to each facility and equipment to ensure efficient and coordinated operation, thereby improving the enterprise's production efficiency. Attached Figure Description To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 A flowchart of a linear reciprocating RGV cooperative scheduling method provided in this application embodiment; Figure 2 A flowchart of a hybrid heuristic algorithm provided in this application embodiment; Figure 3 A workshop layout diagram provided for an embodiment of this application; Figure 4 A visualization of an instance scheduling scheme provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a linear reciprocating RGV collaborative scheduling device provided in an embodiment of this application. Attached image description: 200: Linear reciprocating RGV collaborative scheduling device; 201: Processor; 202: Memory. Detailed Implementation

[0017] This application provides a linear reciprocating RGV collaborative scheduling method, device, and medium.

[0018] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0019] Figure 1 A flowchart of a linear reciprocating RGV cooperative scheduling method provided in this application embodiment is shown below. Figure 1 As shown, the linear reciprocating RGV cooperative scheduling method includes the following steps: S101. Responding to the transportation request initiated by the front end, collect the status information of each facility and equipment corresponding to the RGV system in the workshop.

[0020] In one implementation of this application, in response to transportation demand, transportation task information is obtained; wherein, the transportation task information is triggered by at least one of the following: initiation of a new task, cancellation of an existing task, and rescheduling of a task. Through a pre-installed software system, the current status information of the facilities and equipment corresponding to the RGV system within the workshop is collected and integrated in real time; wherein, the current status information includes at least one of the following: the status and location of the pallet, the status and location of the material, the platform occupancy status, the buffer area occupancy status, the location and load status of the RGV vehicle, and the attribute information of the newly added transportation task. The integrated status information and transportation task information are stored in a database for RGV optimized scheduling.

[0021] Specifically, the pre-built software system for responding to human-computer interaction and equipment information collection in this embodiment is developed in Python. Its front-end interface uses the Jinja2 template engine to build HTML web pages, performs visual rendering using CSS (Cascading Style Sheets) files, and handles user interaction and requests using JavaScript files. The back-end uses the Flask framework to handle routing, view functions, and database operations, ensuring efficient system operation. Furthermore, this pre-built software system integrates libraries such as Snap to enable communication with workshop facilities and task assignment, and calls scheduling algorithms for optimization.

[0022] Furthermore, after the user scans the material barcode with a PDA (handheld terminal), the pre-installed software system will automatically identify the relevant material information. After the user confirms the material to be transported and its current location on the front-end interface, the system will collect all status information within the workshop at that moment and upload the status information and transfer task information to the database.

[0023] Furthermore, in this embodiment, the operations leading to task rescheduling include at least the initiation of a new task, the cancellation of an existing transportation task, and the initiation of a rescheduling request. The information collected by the system includes, but is not limited to, information on pallets, materials, platforms, buffer zones, I / O stations, vehicles, and the status and location of newly added tasks.

[0024] S102. Based on transportation demand and status information, the RGV cooperative scheduling optimization model is solved using a hybrid heuristic algorithm to obtain the target task scheduling scheme.

[0025] In one implementation of this application, an RGV collaborative scheduling optimization model is constructed based on pre-set RGV collaborative scheduling constraints and with minimizing the task time of the transportation task as the optimization objective; wherein, the pre-set RGV collaborative scheduling constraints include at least one of the following: capacity of each facility and equipment, RGV capacity, RGV collision status, pallet availability, and buffer availability.

[0026] Specifically, the optimization objective of the model in this embodiment is to minimize the maximum completion time of all transportation tasks, i.e., to minimize the time when the last operation ends. This objective can improve overall production efficiency and shorten the total time of the task flow. Based on the analysis of workshop resources, various physical constraints are transformed into mathematical constraints. The defined objective function is combined with all constraints to form a structured mathematical programming model. This model describes the problem of finding the optimal scheduling scheme to minimize the maximum completion time while satisfying all resource constraints and safety rules.

[0027] Furthermore, the linear reciprocating 2-RGV scheduling path optimization method model considering resource constraints in this embodiment is as follows: The objective function is to minimize the maximum operation completion time. ; In the formula, This represents the completion time of the j-th operation in the i-th task; ; In the formula, For 0-1 variables, if the task i Operation j At workstation v If completed, the value is 1; otherwise, it is 0. The constraint means that the next unloading operation will be performed at the same RGV station where the material was loaded. ; This constraint indicates that, for the task i Operation j In general, it will only be served by one RGV workstation. This constraint indicates that the load on the online roller conveyor, machine, and buffer area does not exceed 1 at any given time. ; This constraint indicates that, for the offline point, its load at any given time does not exceed 3; ; For each workstation, the load at any given time does not exceed 1;

[0028] All operations of a task conform to the predecessor and successor constraints.

[0029]

[0030] ; For the workstation executing the fourth operation of a rework task or the second operation of a rework task, this workstation The next operation must be the next operation of this task; ; When returning to the production line, the first-in-first-out tray should be used; ; When you select a location to transfer or place a tray, that location is empty; ; Obstacle avoidance constraints, operation Two cars respectively , In execution, the former is executed before the latter, and destination exist destination The right side or the destination overlaps, vehicle There is enough time to move to a safe location; ; ; Obstacle avoidance constraints, operation Two cars respectively , implement, Compare Execute first, and destination exist destination The right side or the destination overlaps, vehicle There is enough time to move to a safe location; ; The end time of each operation is greater than or equal to the start time of the operation plus the loading and unloading time;

[0031] ; For operations involving binding and unbinding material pallets, allow sufficient time for the pallets to be bound and unbound.

[0032] ; ; For each operation, its start time is greater than or equal to the completion time of the previous operation of this vehicle plus the displacement time; ; ; The two constraints above indicate that if a task does not have a transfer point, the actual loading and unloading time will be assigned to the virtual time. Proposed transit operation.

[0033] In one implementation of this application, during initialization, a preset operation set is invoked; wherein the preset operation set includes task operations whose predecessor operations have been completed. Based on the preset operation set and the real-time status information of the current workshop facilities and equipment, initial solution individuals that meet resource constraints are randomly generated to construct the initial population of the algorithm.

[0034] Specifically, in the embodiments of this application, when applying a hybrid algorithm that combines genetic, tabu search, and adaptive large-domain search algorithms, a set of arrangable operations is set during the initial generation initialization process. The set of arrangable operations includes tasks whose predecessor operations have been completed. Based on this, individuals that meet the resource constraints are randomly generated.

[0035] In one implementation of this application, the solution individuals corresponding to the target task scheduling scheme are encoded in three layers. The first layer encodes the execution order of all task operations, the second layer encodes the service stations corresponding to each operation, and the third layer encodes the RGV workstations where each operation is executed. The population individuals composed of these three layers of encoding are optimized and updated using a strategy that combines adaptive large-domain search operators with crossover and mutation operators from a genetic algorithm. Through encoding and optimization updates, the RGV collaborative scheduling optimization model is solved.

[0036] Specifically, when applying a hybrid algorithm that combines genetics, tabu search, and adaptive large-domain search, the individual is encoded as follows: the chromosome has three layers. The first layer represents the execution order of the six operations of all tasks, with the particle length being 6 * the number of tasks. The second layer represents the station position corresponding to each operation. The third layer represents the RGV station where each operation is executed.

[0037] In one implementation of this application, a subset of individuals is randomly selected from the parent population to form a first subset, and the remaining individuals form a second subset. For the first subset, an adaptive large-neighbor search operator is selected according to a preset probability distribution for processing, and the optimized individuals are retained in the offspring population. For the second subset, genetic algorithm operations and repair operations are sequentially applied to form a third subset. For individuals in the third subset that meet the triggering conditions, a tabu search algorithm is applied for local optimization. The individuals obtained after processing with the adaptive large-neighbor search operator are merged with the individuals obtained after processing with the tabu search algorithm to obtain a new generation population. The optimization process is repeated until the algorithm termination condition is met.

[0038] Specifically, this embodiment of the application, based on the state of workshop facilities and equipment, invokes a hybrid heuristic algorithm of genetic-tautology-adaptive large-domain search to globally optimize the solution space and obtain a globally satisfactory solution. The globally satisfactory solution obtained by invoking the genetic-tautology-adaptive large-domain search algorithm based on the state of workshop facilities and equipment includes the optimized scheduling order of each task operation, workstation and RGV station allocation, and obstacle avoidance strategies. When applying the genetic-tautology-adaptive large-domain search algorithm, there are two methods for population updating: one is to randomly sample a portion of the solutions from the parent generation, select an adaptive large-domain operator according to a certain probability, and retain the optimized individuals in the offspring; the second is to perform crossover and mutation using the genetic algorithm on all remaining parent generations, and then repair them to produce an equal number of offspring. Finally, a tabu search is performed on all individuals in this portion of the offspring that trigger tabu search.

[0039] Specifically, at the start of each generation of optimization, the current parent population is first randomly divided into two parts. A certain proportion of individuals are randomly selected to form the first subset, and the remaining individuals automatically form the second subset. This random division provides different sample bases for subsequent search strategies, ensuring search diversity. For each individual in the first subset, the algorithm dynamically calculates the selection probability based on the historical performance of a set of predefined destruction and repair operators. Then, according to this probability distribution, a destruction operator and a repair operator are randomly selected to destroy and then repair the current individual, generating a new solution. This optimized new individual is directly retained for subsequent generation population construction. For all individuals in the second subset, standard genetic algorithm operations are uniformly applied. First, a crossover operation is performed, randomly selecting individuals to pair and exchange partial chromosome segments. Then, a mutation operation is performed, randomly changing the values ​​of some genes in the individuals with a small probability. Since crossover and mutation may produce infeasible solutions that violate resource constraints, a repair operation must be performed immediately, adjusting gene values ​​through heuristic rules to ensure that all newly generated individuals are feasible, thus forming the third subset. Traversing the third subset generated by the genetic algorithm, for individuals that meet specific triggering conditions, a tabu search algorithm is initiated for deep local optimization. Tabu search performs a meticulous search within the individual's neighborhood, avoiding loops through a tabu list, thereby finding a better local solution and replacing the original individual with this optimized solution. The individuals obtained after processing with the adaptive large-neighbor search operator are merged with those obtained after processing with the tabu search algorithm. The merged population becomes the newly generated offspring population. This completes one full population update iteration. The process of population partitioning, dual-path optimization, local reinforcement, and merging is repeated until the preset algorithm termination condition is met. At this point, the algorithm stops and outputs the best solution from each generation as the final scheduling scheme.

[0040] Figure 2A flowchart of a hybrid heuristic algorithm provided for embodiments of this application is shown below. Figure 2 As shown, an initial population is generated based on the problem constraints, followed by an iterative loop driven by adaptive large-domain search. In each iteration, the algorithm first adaptively selects and applies the appropriate operators to process the current solution to generate a new solution based on the historical performance of a set of predefined destruction and repair operators (the operator library integrates crossover and mutation operations of genetic algorithms as specific repair methods), and then determines whether to accept the new solution. After accepting the new solution, the process triggers two branches in parallel: one branch immediately initiates tabu search for deep local mining of the current high-quality individuals, and the other branch judges the conditions to decide whether to execute the population-level update operations of the genetic algorithm (including selection, crossover, and mutation). Finally, the individuals optimized by adaptive large-domain search, strengthened by tabu search, and evolved by genetic algorithm are merged and selected to form a new generation of population. This process is repeated until the termination conditions such as the maximum number of iterations or convergence accuracy are met, and a globally satisfactory scheduling scheme is output as the final solution.

[0041] S103. Based on the target task scheduling scheme, send scheduling instructions to the task executors required by the task to control the task executors to complete the transportation task.

[0042] In one implementation of this application, after obtaining the target task scheduling scheme, the target task is parsed into a transfer task with a preset operation sequence; wherein, the transfer task includes at least one of the following: an online task, an offline task, a rework task, and a rework task. An online task is a task transporting goods from the online roller conveyor station to the processing machine; an offline task is a task transporting goods from the processing machine to the offline roller conveyor station; a rework task is a task transporting goods from the processing machine to the rework station; and a rework task is a task transporting goods from the rework station to the processing machine.

[0043] Specifically, the types of tasks in the embodiments of this application There are four types in total, namely online tasks. SX Offline task XX Repair task FX Rework task FG Among them, the online task refers to the task starting from the online roller conveyor (the platform assembly point is...). SS Transported to the machine (the machine collection is) MA Offline tasks ( XX From the machine to the off-line location (the platform is where the equipment is transported). XS ); Repair tasks, from transporting the machine to the repair station (platform assembly point) FXRework tasks involve transporting materials from the repair area to the machine. Each task has six operations. Taking the online task as an example, the six operations are: picking up a pallet from a station, unloading the pallet on the online roller conveyor (waiting for the material pallets to be bundled), loading the material pallet assembly on the online roller conveyor, temporarily storing the material pallet assembly in the buffer area, and transporting the material pallet assembly to the machine for processing. Offline tasks are slightly different. The first four operations involve transporting the material pallet assembly (which may be transferred in the buffer area) to the offline point. The last two operations involve obtaining the empty pallet after unbundling the assembly at the offline point and placing the empty pallet in a certain location. The same logic applies to repair, rework, and offline tasks. These are fixed parameters; if the operation... j If it is an RGV loading operation, then It is 1 if it is true, otherwise it is -1.

[0044] The parameters are shown in Table 1:

[0045] Table 1 Decision variables: Discrete decision variables fall into three categories. For 0-1 variables, if the task i Operation j (i.e., operation) In the task p Operation q Before, the variable is 1; otherwise, it is 0. For variables of 0-1, if the operation By vehicle v Execute, value is 1, otherwise 0; a certain operation On the platform Job, Decision Variables For variables of 0-1, if the operation On the platform d If completed, the value is 1; otherwise, it is 0. Continuous decision variables fall into two categories. Indicates operation The moment when execution begins Indicates operation The moment when execution ends.

[0046] The variables are shown in Table 2:

[0047] Table 2 Figure 3 A workshop layout diagram provided for an embodiment of this application, such as Figure 3As shown, S1 and S2 are two access stations, E1 and E2 are two exit stations, and E3 is a repair station. RGV1 is to the left of RGV2. The left RGV has two workstations with a total maximum load of 2, while the right RGV has one workstation with a maximum load of 1. Both RGVs are always on the track, which is long enough to allow all RGVs to move from S1 to E3.

[0048] Figure 4 A visualization of an instance scheduling scheme provided in this application embodiment, such as... Figure 4 As shown, the horizontal axis represents time, and the vertical axis lists all resources that need to be scheduled, including two RGVs, various stations, and buffer zones. Different colored or patterned bars represent different task operations. The starting position and length of each bar on the time axis correspond to its start time and duration, respectively, while the position of the bar on the vertical axis indicates the resource executing that operation. This diagram clearly shows the following information: the task execution order and idle time of each RGV; the occupancy status and service sequence of each station or buffer zone; the temporal and spatial coordination and parallelism of different task types; and the total completion time of the entire scheduling scheme.

[0049] In one implementation of this application, the target task scheduling scheme is parsed to generate scheduling instructions. These instructions are then transmitted in real-time to the corresponding task executors via a communication network composed of wireless access points. Each task executor includes at least one of an RGV vehicle, on / off roller conveyors, and buffer zone equipment. Furthermore, task information requiring manual operation is pushed to the terminal devices of relevant personnel to drive the collaborative operation of logistics execution units within the workshop.

[0050] Specifically, the system backend uses a task scheduling scheme and wireless access points for communication to transmit instructions to the RGV, the on-line and off-line roller conveyors, the buffer area, and the workers who need manual operation, so as to realize intelligent decision-making and automated operation in the workshop and improve production efficiency and response speed.

[0051] Specifically, after the system backend obtains the target task scheduling scheme calculated by the genetic-taboo-adaptive large-domain search hybrid heuristic algorithm, it first parses it. The scheduling scheme includes information such as the task ID, operation type, planned start time, designated RGV workstation, and service station for each operation. The parsing engine converts this information into standardized control instructions that can be recognized and executed by various task executors according to predefined instruction templates. After generating standardized instructions, the system backend sends the instructions to the corresponding automated task executors in real time through a communication network composed of wireless access points deployed in the workshop. Instructions for controlling RGV vehicles are sent to the on-board controller; instructions for controlling the start, stop, and steering of the roller conveyor are sent to the roller conveyor PLC; instructions for controlling the lifting and transfer of the buffer area are sent to the corresponding control unit. For manual operation links in the scheduling scheme, corresponding task work order information is generated synchronously and pushed to the smart terminal devices carried by relevant operators through the enterprise intranet or wireless network, informing them of the work content, location, standard operating procedures, and precautions, guiding them to complete collaborative tasks efficiently and accurately. Once all instructions and information are delivered, the logistics execution units within the workshop are activated to work collaboratively: RGVs travel and operate according to the instructed paths, roller conveyors start and stop conveying materials according to instructions, buffer areas store or release materials according to instructions, and workers operate according to terminal information.

[0052] Figure 5 This is a schematic diagram of the structure of a linear reciprocating RGV collaborative scheduling device provided in an embodiment of this application. Figure 5 As shown, the linear reciprocating RGV collaborative scheduling device 200 includes: at least one processor 201; and a memory 202 communicatively connected to the at least one processor 201. The memory 202 stores instructions executable by the at least one processor 201. These instructions, when executed by the at least one processor 201, enable the at least one processor 201 to: respond to transportation demands initiated by the front end and collect status information of each facility and equipment corresponding to the RGV system within the workshop; based on the transportation demands and status information, solve the RGV collaborative scheduling optimization model using a hybrid heuristic algorithm to obtain a target task scheduling scheme; and based on the target task scheduling scheme, send scheduling instructions to the task executors required by the task to control the task executors to complete the transportation task.

[0053] This application provides a non-volatile computer storage medium storing computer-executable instructions. The computer-executable instructions are configured to: respond to a transportation request initiated by the front end, collect status information of each facility and equipment corresponding to the RGV system in the workshop; based on the transportation request and status information, solve the RGV collaborative scheduling optimization model through a hybrid heuristic algorithm to obtain a target task scheduling scheme; and based on the target task scheduling scheme, send scheduling instructions to the task executors required by the task to control the task executors to complete the transportation task.

[0054] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0055] The above descriptions are merely embodiments of this application and are not intended to limit the scope of this application. For those skilled in the art, various modifications and variations can be made to the embodiments of this application. These modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions in the embodiments of this application.

Claims

1. A linear reciprocating RGV cooperative scheduling method, characterized in that, The method includes: In response to the transportation requests initiated by the front end, the status information of each facility and equipment corresponding to the RGV system in the workshop is collected; Based on the transportation demand and the status information, the RGV cooperative scheduling optimization model is solved using a hybrid heuristic algorithm to obtain the target task scheduling scheme; Based on the target task scheduling scheme, scheduling instructions are sent to the task executors required by the task to control the task executors to complete the transportation task.

2. The linear reciprocating RGV cooperative scheduling method according to claim 1, characterized in that, The response to the transportation request initiated by the front end collects the status information of each facility and equipment corresponding to the RGV system in the workshop, specifically including: In response to the transportation demand, transportation task information is obtained; wherein, the transportation task information is triggered by at least one of the following: new task initiation, existing task cancellation, and task rescheduling; The pre-installed software system collects and integrates the current status information of the facilities and equipment corresponding to the RGV system in the workshop in real time. The current status information includes at least one of the following: the status and location of the pallet, the status and location of the material, the platform occupancy status, the buffer area occupancy status, the location and load status of the RGV vehicle, and the attribute information of the newly added transportation task. The integrated status information and transportation task information are stored in the database for RGV optimized scheduling.

3. The linear reciprocating RGV cooperative scheduling method according to claim 1, characterized in that, Before solving the RGV cooperative scheduling optimization model using a hybrid heuristic algorithm based on the transportation demand and the status information, the method further includes: Based on the pre-set RGV cooperative scheduling constraints and taking minimizing the task time of the transportation task as the optimization objective, the RGV cooperative scheduling optimization model is constructed. The pre-set RGV collaborative scheduling constraints include at least one of the following: capacity of each facility and equipment, RGV capacity, RGV collision status, tray availability, and buffer availability.

4. The linear reciprocating RGV cooperative scheduling method according to claim 1, characterized in that, The solution to the RGV cooperative scheduling optimization model using a hybrid heuristic algorithm specifically includes: The solution individuals corresponding to the target task scheduling scheme are encoded in three layers; wherein, the first layer encodes the execution order of all task operations, the second layer encodes the service station corresponding to each operation, and the third layer encodes the RGV workstation where each operation is executed. By combining adaptive large-domain search operators with crossover and mutation operators of genetic algorithms, the population individuals composed of the three-layer encoding are optimized and updated. The RGV cooperative scheduling optimization model is solved through encoding and optimization updates.

5. The linear reciprocating RGV cooperative scheduling method according to claim 4, characterized in that, Before performing three-layer encoding on the solution individuals corresponding to the target task scheduling scheme, the method further includes: During initialization, a preset set of operations is invoked; wherein, the preset set of operations includes task operations whose predecessor operations have been completed. Based on the preset operation set and the real-time status information of the current workshop facilities and equipment, initial solution individuals that meet resource constraints are randomly generated to construct the initial population of the algorithm.

6. The linear reciprocating RGV cooperative scheduling method according to claim 4, characterized in that, The strategy of combining adaptive large-domain search operators with crossover and mutation operators of genetic algorithms to optimize and update the population individuals composed of the three-layer encoding specifically includes: Randomly select some individuals from the parent population to form the first subset, and then select the remaining individuals to form the second subset. For the first subset, an adaptive large-domain search operator is selected according to a preset probability distribution for processing, and the optimized individuals are retained in the offspring population; The second subset is then processed sequentially using a genetic algorithm and a repair operation to form the third subset. For individuals in the third subset that meet the triggering conditions, a tabu search algorithm is applied for local optimization. The individuals obtained after processing with the adaptive large-domain search operator are merged with the individuals obtained after processing with the tabu search algorithm to obtain a new generation of population. Repeat the optimization process until the algorithm's termination condition is met.

7. The linear reciprocating RGV cooperative scheduling method according to claim 1, characterized in that, The step of sending scheduling instructions to the task executors required by the task based on the target task scheduling scheme specifically includes: The target task scheduling scheme is parsed to generate the scheduling instruction; The scheduling instructions are sent to the corresponding task executors in real time through a communication network composed of wireless access points; wherein the task executors include at least one of RGV vehicles, on-line and off-line roller conveyors, and buffer area equipment. In addition, task information requiring manual operation is pushed to the terminal devices of relevant operators to drive the collaborative operation of logistics execution units within the workshop.

8. The linear reciprocating RGV cooperative scheduling method according to claim 1, characterized in that, After obtaining the target task scheduling scheme, the method further includes: The target task is parsed into a transfer task with a preset operation sequence; wherein, the transfer task includes at least one of the following: online task, offline task, rework task, and rework task; The online task refers to the task of transporting goods from the online roller conveyor platform to the processing machine. The off-line task is the task of transporting the processing machine to the off-line roller conveyor station. The rework task is the task of transporting the equipment from the processing machine to the rework location. The rework task is the task of transporting goods from the repair station to the processing machine.

9. A linear reciprocating RGV collaborative scheduling device, characterized in that, The device includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the device is triggered to perform the method described in any one of claims 1-8.

10. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are capable of performing the method described in any one of claims 1-8.

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