OHT trolley pick-and-place scheduling optimization method, device and equipment and medium

Abstract

The invention discloses an OHT trolley pick-and-place scheduling optimization method and device, equipment and a medium, and the method comprises the steps: obtaining the historical carrying task data of OHT trolleys, building an OHT trolley queuing model according to the historical carrying task data, carrying out the optimization according to the OHT trolley queuing model with the minimum average queuing tasks in unit time as an optimization target, and carrying out the optimization according to the optimization result. The task scheduling of the OHT trolley is optimized, scheduling optimization is performed on the task of the OHT trolley through the queuing model, the task queuing time of the OHT trolley is effectively shortened, and the occurrence probability of the line congestion condition is reduced.

Description

Technical Field

[0001] The present invention relates to the field of automated handling task scheduling, and in particular, to an optimization method, device, equipment and medium for the pick-up and placement scheduling of OHT vehicles. Background Art

[0002] The main reason for the design and application of the AMHS (Automated Material Handling System) in semiconductor wafer foundries is that the semiconductor process requires 400 - 500 steps. The current common layout of the AMHS system is as follows: Storage units (such as stockers, OHBs) are distributed in the central aisle, with Interbay tracks above. On both sides of the central aisle are the production areas of related processes, forming each Intrabay. The Intrabay tracks are above the production machines in each bay. Both Interbay and Intrabay are one-way loop routes, ultimately forming the overall connection of the AMHS system tracks in the entire FAB, enabling the transfer of production materials between bays, between tools and bays, and between tools and tools. Even though the AMHS system between tools can greatly improve the transfer efficiency to a large extent, it also has certain bottlenecks, and intelligent transfer is proposed. As the wafers are transported, they will travel to various corners of the wafer fab. However, sometimes due to process differences, there will be a large number of wafers in a certain production area during a certain period of time, which is likely to cause blockages, seriously affecting the normal handling of wafers. The increase in handling time due to congestion and queuing will reduce the handling efficiency of OHT (Over Head Hoist Transport) vehicles, and thus greatly increase the Wafer Cycle Time. Summary of the Invention

[0003] In view of this, the present invention provides an optimization method for the pick-up and placement scheduling of OHT vehicles, which solves the technical problem in the prior art that the handling time increases due to congestion and queuing, reducing the handling efficiency of OHT vehicles.

[0004] According to the first aspect of the present invention, an optimization method for the pick-up and placement scheduling of OHT vehicles is provided, including:

[0005] Obtaining historical handling task data of the OHT vehicle;

[0006] Establishing an OHT vehicle queuing model according to the historical handling task data;

[0007] Optimizing according to the OHT vehicle queuing model with the goal of minimizing the average queuing tasks per unit time;

[0008] Performing optimization settings on the task scheduling of the OHT vehicle according to the optimization result.

[0009] According to a second aspect of the present invention, there is provided an optimization device for the picking and placing scheduling of an OHT cart, comprising:

[0010] An acquisition module, configured to acquire historical handling task data of the OHT cart;

[0011] An analysis module, configured to establish an OHT cart queuing model according to the historical handling task data;

[0012] An optimization module, configured to optimize according to the OHT cart queuing model with the goal of minimizing the average queuing tasks per unit time;

[0013] A control module, configured to perform optimization settings on the task scheduling of the OHT cart according to the optimization result.

[0014] According to a third aspect of the present invention, there is provided a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned OHT cart picking and placing scheduling optimization method are implemented.

[0015] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned OHT cart picking and placing scheduling optimization method are implemented.

[0016] By means of the above technical solutions, an OHT cart picking and placing scheduling optimization method, device, equipment, and medium provided by the present invention acquire historical handling task data of the OHT cart, establish an OHT cart queuing model according to the historical handling task data, optimize according to the OHT cart queuing model with the goal of minimizing the average queuing tasks per unit time, and perform optimization settings on the task scheduling of the OHT cart according to the optimization result. The task of the OHT cart is scheduled and optimized by the queuing model, effectively reducing the task queuing time of the OHT cart and reducing the occurrence probability of line congestion.

[0017] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, and to be implemented in accordance with the content of the specification, and in order to make the above and other purposes, features, and advantages of the present invention more obvious and understandable, the following specifically describes the embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0019] Figure 1 The figure shows a schematic diagram of the application scenario of the AMHS system provided in the embodiment of the present invention;

[0020] Figure 2 The figure shows a schematic flowchart of a method for optimizing the loading and unloading scheduling of an OHT cart provided in the embodiment of the present invention;

[0021] Figure 3 The figure shows a schematic structural diagram of an apparatus for optimizing the loading and unloading scheduling of an OHT cart provided in the embodiment of the present invention. Detailed implementation manners

[0022] The following will refer to the accompanying drawings and describe in detail the specific implementation manners of the present invention in combination with embodiments. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0023] A method for optimizing the loading and unloading scheduling of an OHT cart provided in the embodiment of the present invention can be applied to an AMHS system scenario such as Figure 1 In the AMHS system scenario, it includes an inter-bay loop inter-aisle system, an intra-bay loop in-aisle system (chip manufacturing area, and there are multiple processing devices, i.e., machines, in the chip manufacturing area), an OHT cart (the white frame is in an idle state, and the black frame is in a task-loaded state), an OHB (Over Head Buffer for storing FOUP) is arranged on the track of the inter-aisle system. The OHT cart obtains the FOUP from the OHB and then unloads it at the Port of a certain processing device in the in-aisle system. During this process, congestion may occur when the OHT cart obtains the FOUP from the OHB and when it is transported to and unloaded at the Port of a certain processing device. It is necessary to reduce the probability of congestion occurring while ensuring as high a utilization rate as possible based on a method for optimizing the loading and unloading scheduling of an OHT cart disclosed in the embodiment of the present invention. By obtaining the historical handling task data of the OHT cart, an OHT cart queuing model is established according to the historical handling task data. According to the OHT cart queuing model, optimization is carried out with the goal of minimizing the average queuing tasks per unit time. According to the optimization result, the task scheduling of the OHT cart is optimized. The queuing model schedules and optimizes the tasks of the OHT cart, effectively reducing the task queuing time of the OHT cart and reducing the probability of line congestion.

[0024] The execution process of the OHT (Over Head Hoist Transport) trolley handling task is as follows: The EAP receives the loading and unloading signals from the process equipment, transmits the FOUP handling task requirements to the MCS through the MES, and the MCS then immediately notifies the OCS. The OCS is responsible for scheduling the OHT to execute the specific handling task. An OHT trolley picking and placing scheduling optimization method provided by an embodiment of the present invention can be applied alone or comprehensively on the MES, MCS, and OCS systems. Among them, the functions and roles of each execution system are as follows:

[0025] EAP: It is short for Equipment Automation Programming, also known as "equipment automation programming", which is responsible for controlling semiconductor equipment for automated production, integrating with the MES system, verifying product information, automatically accounting, and at the same time collecting process data and equipment parameter data during the product production process, helping to improve the production efficiency of semiconductor factories, avoiding manual operation errors, and providing product yield.

[0026] MES: It is short for Manufacturing Execution System, also known as "production execution system", which provides manufacturing data management, production scheduling management, production dispatching management, inventory management, quality management, production process control, and underlying data integration and analysis for intelligent manufacturing factories, and is the brain of intelligent manufacturing factories.

[0027] MCS: It is short for Material Control System, also known as "material control system", which is responsible for the scheduling of production materials in the intelligent factory, monitors the status of all transmission equipment in the entire factory, selects the best path for material delivery, reduces material transfer time, and increases the efficiency of the factory floor and the availability of equipment.

[0028] OCS: It is short for OHT Control System, also known as "overhead crane control system", which is responsible for the traffic control of OHT in the intelligent factory, selects the optimal OHT to execute material delivery, selects the optimal path to schedule the OHT to travel, and maximizes the transportation capacity of the OHT.

[0029] OHT: It is short for Over head Hoist Transport, also known as "overhead crane", which refers to a device that can travel on an aerial track and can "directly" enter the loading and unloading ports of storage equipment or process equipment through a belt-driven hoisting mechanism, and is used for internal transportation in the process area, as well as for transportation between process areas or factories.

[0030] FOUP: It is short for Front Opening Unified Pod, also known as "front-opening wafer transfer box (wafer boat)", which is a container used in semiconductor manufacturing processes to protect, transport, and store wafers.

[0031] The scheduling optimization in the embodiments of the present invention includes adjusting the task issuance frequency of task MCS, adjusting the number of working OHT carts (dynamically adjusting between the number of idle OHT carts and the number of working OHT carts), and the average speed of OHT carts (corresponding to the average time for a single handling).

[0032] The present invention will be described in detail below through specific embodiments.

[0033] Embodiment 1

[0034] As Figure 2 shown, a method for optimizing the loading and unloading scheduling of OHT carts provided in the embodiments of the present invention includes:

[0035] Step 201, obtain the historical handling task data of the OHT cart;

[0036] Step 202, establish an OHT cart queuing model according to the historical handling task data;

[0037] Among them, in this step, an OHT cart queuing model can be established based on the M / M / 1 (M / M / s) queuing model. The conditions for the M / M / 1 queue and the M / M / s queue include that the length of the OHT cart task queue is not limited; the time intervals between the issuance of handling tasks and the handling time both follow an exponential distribution (simulating the task requests of the OHT cart as a Poisson flow with an average incidence rate of λ task . The number of service desks (the number of OHT carts) is 1 and s respectively, where s is a positive integer.

[0038] In Embodiment 1 of the present invention, the task requests of the OHT cart are simulated as a Poisson flow with an average incidence rate of λ t (t is the task type with values of 1 and 2. The first type of task is the task of the OHT cart loading wafers for handling, and the second type of task is the task of the OHT cart unloading wafers. The first type of task includes the OHT cart obtaining a FOUP from the OHB as task task1, which is the load task, or loading a FOUP from the Port of a certain processing device. The second type of task includes the OHT cart transporting the FOUP to the Port of a certain processing device for unloading or transporting it to the OHB for unloading the FOUP as task task2, which is the unload task). Correspondingly, the average task time is λ and Obtained by calculating historical handling task data; it is also possible to obtain the relationship between the process type, historical handling task data, and the number of tasks per unit time based on machine learning (such as neural networks) of historical handling task data.

[0039] Step 203: Optimize according to the OHT cart queuing model with the goal of minimizing the average queuing tasks per unit time.

[0040] Among them, in the embodiments of the present invention, the AMHS system is modeled based on queuing theory. The OHT can be regarded as a service desk, and the load and unload tasks that send requests to the OHT at any moment are respectively regarded as two types of customers, task (task = 1 or 2) tasks, which is equivalent to a queuing system with mixed-flow service. The model is an M / M / 1 or M / M / s queuing model, and then the following formula can be used for calculation:

[0041] The total incidence rate of load and unload tasks is: Among them, M is the number of load tasks, N is the number of unload tasks. Considering that the number of general load tasks and unload tasks is equal, the above formula can be simplified to

[0042] The average time of load and unload tasks is: Considering that the number of general load tasks and unload tasks is equal, that is, M = N, the above formula can be simplified to

[0043] Correspondingly, the utilization rate of the OHT cart is With the goal of optimizing the average queuing tasks per unit time as min(E task＝1,2 ), E task＝1,2 is the average queuing tasks per unit time (the total average queuing tasks of the first type of task and the second type of task, calculated by combining load tasks and unload tasks). In order to ensure that the utilization rate of the OHT cart is within a reasonable range, a limiting condition needs to be added That is, the utilization rate of the OHT cart must be optimized between 60% and 90%, that is

[0044] Optionally, considering the case where the task times of load tasks and unload tasks are approximately equal, that is, the processing equipment Port and OHB corresponding to each OHT cart to complete the tasks are fixed (that is, the processing equipment Port and OHB corresponding to load tasks and unload tasks do not belong to the situation of dynamic change), then the average queuing time T Q satisfies E′ task＝1The average number of tasks in the load task queue when the load task is issued to the OHT cart (i.e., multiple OHT carts are queuing to load wafers from the OHB or from the processing equipment port), E′ task＝2 The average number of tasks in the unload task queue when the unload task is issued to the OHT cart (i.e., multiple OHT carts are queuing to unload wafers from the OHB or from the processing equipment port). Based on Little's Law in queuing theory ("the number of items L in the queue" = "the arrival rate λ of new items" × "the average time W spent on tasks or items", i.e., L = λW), it is transformed

[0045] The objective function in the first embodiment of the present invention is simplified to The constraint conditions are

[0046] Step 204: According to the optimization result, optimize the task scheduling of the OHT cart

[0047] Among them, after the optimization result is calculated, the optimal utilization target value ρ opt can be obtained, the optimal task occurrence rate target value λ opt can be obtained, the optimal task number target value M opt (corresponding to the number of OHT carts in the working state), and the optimal number of OHT carts allocated to each OHB and Intrabay (taking Figure 1 the layout diagram as an example, a specific number of OHT carts are specified to be allocated to the system Intrabay and the adjacent OHB in a certain aisle, and the handling and movement are restricted within this range).

[0048] An OHT cart picking and placing scheduling optimization method provided by the present invention obtains the historical handling task data of the OHT cart, establishes an OHT cart queuing model according to the historical handling task data, optimizes with the goal of minimizing the average queuing tasks per unit time according to the OHT cart queuing model, and optimizes the task scheduling of the OHT cart according to the optimization result. The task of the OHT cart is scheduled and optimized by the queuing model, effectively reducing the task queuing time of the OHT cart and reducing the occurrence probability of line congestion

[0049] Embodiment 2

[0050] Compared with the first embodiment, the second embodiment of the present invention provides more scheduling strategies. It not only takes the minimum average queuing tasks per unit time as the optimization goal but also introduces the handling cost as the target optimization value. According to the OHT cart queuing model, the sum of the unit time costs of the OHT cart is calculated as the optimization goal. Among them, the minimum unit time cost of the OHT cart includes waiting time cost, OHT hardware cost, and OHT operation cost, and the optimal solution is obtained through intelligent optimization algorithms such as simulated annealing algorithm and ant colony algorithm. The second embodiment of the present invention gives a multi-OHT cart scheduling strategy based on genetic algorithm, including:

[0051] The objective function adopted in this embodiment is:

[0052]

[0053] where C is the total cost (unit: yuan / hour); m is the number of OHT carts in operation; C OHT is the input cost per unit time of a single OHT cart (quantifying the fixed loss and power loss of the cart), i is the serial number in the task queue (the serial number in the LILO queue is equivalent to the priority), there are n tasks queued in the task queue in total, n′ is the total number of tasks waiting per unit time, η i is the proportion of the task with serial number i in the total number of tasks, C i is the waiting cost per unit time of the task with serial number i (quantifying the cost loss of process manufacturing delay time), L a is the utilization rate of the OHT (assuming that the handling load of each OHT is approximately equal each time, because the wafer cassette specifications are the same, it can be considered approximately equal), C load is the OHT operation cost per unit time (quantifying the fixed loss and power loss of the cart), and α1, α2, α3 are weight coefficients. Among them, α1, α2, α3, C OHT , C i , η i can all be regarded as fixed values, and the decision variables are m, n′, and L a . Combining with the M / M / s / queuing theory model, m is equivalent to the number of service desks, n′ is the average queue length of the queuing system, and L a is the service intensity.

[0054] For the M / M / s queuing model, the service intensity where λ is the average number of tasks issued per unit time, μ is the average number of tasks that a single OHT cart can complete per unit time, L a is the utilization rate of the OHT, and its value ranges between 0 and 1, representing the busy degree of the OHT cart. 0 means idle and 1 means always busy. n′ and η i can be expressed as a function L(m, μ) of m and μ. Correspondingly, the objective function adopted in the embodiment is:

[0055]

[0056] The objective function is transformed into a problem of finding the optimal solution of f(m, μ). In the second embodiment of the present invention, a genetic algorithm with an elitism preservation strategy is used to find the optimal solution, that is, the number arrangement of OHT vehicles m under the condition of the maximum average number of tasks that a single OHT vehicle can complete per unit time. Since the average OHT service number is related to the track layout in the Intrabay, the performance of the OHT vehicle itself, the number of OHT vehicles, the specific tasks, and the scheduling strategy, the optimal μ is determined. MAX After that, the number of OHT vehicles and the corresponding control strategy are determined in reverse. In the algorithm solution, the objective function is directly used as the fitness function, and roulette wheel selection with an elitism preservation strategy is adopted to retain the best individual in each generation without allowing it to participate in roulette wheel selection, crossover, and mutation. After selection, replication is performed to keep the population size constant, and the worst individual in the offspring is replaced by the retained elite to achieve faster convergence. The single-point crossover method is adopted, and the crossover points are randomly paired and selected. Basic bit mutation is used, and the mutated gene positions are randomly specified. A relatively large crossover probability is adopted to ensure the emergence of new patterns with large fitness values, a relatively small mutation probability is used to prevent premature convergence and improve local search ability, and the preset number of iterations of the average fitness value remaining unchanged is used as the iteration termination condition.

[0057] One genetic algorithm iteration convergence is performed for each OHT number m. The best fitness value and the optimal individual, that is, the shortest time T for task completion, can be obtained in each iteration process. min and the allocation relationship between the specific tasks and the OHT, so as to obtain the T under different OHT numbers m. min and μ MAX relationship. Substituting the corresponding values into the objective function formula, the optimal scheduling cost C is finally obtained. It can be seen that when the number of OHTs is small, the task waiting cost is large; while when the number is large, the average service number decreases, and the input and operation costs increase. It can be seen that based on this genetic algorithm scheduling strategy, as the number of OHTs increases, the values of the average service number and the target cost first increase and then decrease, and the optimal OHT number and scheduling target are obtained, realizing the optimization of the OHT pickup and delivery scheduling in the AMHS system of the semiconductor FAB factory.

[0058] In the second embodiment of the present invention, based on the optimization of the total cost, the objective of the OHT vehicle is optimized, realizing a more global and comprehensive optimization, effectively improving the cost-effectiveness during the operation of the OHT vehicle, reducing the cost and resource waste in the production process while improving the utilization rate.

[0059] Furthermore, as Figure 1For the specific implementation of the method, in an embodiment of the present invention, an optimization device for the pickup and placement scheduling of an OHT cart is provided. As Figure 3 shown, the device includes:

[0060] An acquisition module 310, configured to acquire historical handling task data of the OHT cart;

[0061] An analysis module 320, configured to establish an OHT cart queuing model according to the historical handling task data;

[0062] An optimization module 330, configured to optimize according to the OHT cart queuing model with the goal of minimizing the average queuing tasks per unit time;

[0063] A control module 340, configured to perform an optimized setting on the task scheduling of the OHT cart according to the optimization result.

[0064] In an embodiment of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0065] Acquire historical handling task data of the OHT cart;

[0066] Establish an OHT cart queuing model according to the historical handling task data;

[0067] Optimize according to the OHT cart queuing model with the goal of minimizing the average queuing tasks per unit time;

[0068] Perform an optimized setting on the task scheduling of the OHT cart according to the optimization result.

[0069] In an embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0070] Acquire historical handling task data of the OHT cart;

[0071] Establish an OHT cart queuing model according to the historical handling task data;

[0072] Optimize according to the OHT cart queuing model with the goal of minimizing the average queuing tasks per unit time;

[0073] Perform an optimized setting on the task scheduling of the OHT cart according to the optimization result.

[0074] It should be noted that for the functions or steps that can be achieved by the above-mentioned computer-readable storage medium or computer device, reference can be made to the relevant descriptions on the server side and the client side in the foregoing method embodiments. To avoid repetition, they will not be described in detail here.

[0075] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided by the present invention can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0076] Those skilled in the art can clearly understand that for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0077] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or equivalently replace some of the technical features. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention and should all be included in the protection scope of the present invention.

Claims

1. An optimization method for the picking and placing scheduling of an OHT cart, characterized in that, Including: Obtain the historical handling task data of the OHT cart; Establish an OHT cart queuing model according to the historical handling task data; Optimize according to the OHT cart queuing model with the goal of minimizing the average queuing tasks per unit time; Optimize the task scheduling of the OHT cart according to the optimization result.

2. The OHT vehicle picking and placing scheduling optimization method according to claim 1, wherein The step of establishing an OHT cart queuing model according to the historical handling task data includes: Establish an OHT cart M / M / 1 or M / M / s queuing model according to the historical handling task data, where s is the number of OHT carts in the working state.

3. The OHT cart loading and unloading scheduling optimization method according to claim 1, wherein The step of optimizing according to the OHT cart queuing model with the goal of minimizing the average queuing tasks per unit time includes: According to the OHT cart queuing model, the minimum value of the average number of queuing tasks per unit time is min(E task＝1,2 ), where E task＝1,2 is the total average number of queuing tasks for the first type of task and the second type of task. The first type of task is the task of the OHT cart loading wafers for handling, and the second type of task is the task of the OHT cart unloading wafers; Set the optimized limit condition as where M is the number of tasks of the first type or the second type, and ρ task1 is the utilization rate of the OHT cart in each task of the first type, and ρ task2 is the utilization rate of the OHT cart in each task of the second type.

4. The OHT vehicle picking and placing scheduling optimization method according to claim 3, wherein, According to the OHT trolley queuing model, the minimum value of the average number of queuing tasks per unit time is min(E task＝1,2 ) and the steps are as follows: Calculate the minimum value of the objective function according to the OHT car queuing model, where ρ is the utilization rate of the OHT car, ρ is the utilization rate of the OHT car for completing the first type of task, and ρ task1 is the utilization rate of the OHT car for completing the second type of task. task2 ​ 5. The OHT cart loading and unloading scheduling optimization method according to claim 1, wherein The step of optimizing the task scheduling of the OHT cart according to the optimization result includes: Adjust the frequency of tasks sent to the OHT cart and / or adjust the number of OHT carts in the working state and / or the number of OHT carts allocated to each OHB and Intrabay according to the optimization result.

6. The OHT cart loading and unloading scheduling optimization method according to claim 1, wherein The step of optimizing according to the OHT cart queuing model with the goal of minimizing the average queuing tasks per unit time includes: Calculate the sum of the unit time costs of the OHT cart as the optimization goal according to the OHT cart queuing model, where the minimum unit time cost of the OHT cart includes waiting time cost, OHT hardware cost, and OHT operation cost.

7. The OHT vehicle picking and placing scheduling optimization method according to claim 6, characterized in that, Use the simulated annealing algorithm to calculate the optimal solution of the sum of the unit time costs of the OHT cart.

8. An OHT cart loading and unloading scheduling optimization device, characterized in that Including: An acquisition module for obtaining the historical handling task data of the OHT cart; An analysis module for establishing an OHT cart queuing model according to the historical handling task data; An optimization module for optimizing according to the OHT cart queuing model with the goal of minimizing the average queuing tasks per unit time; A control module for optimizing the task scheduling of the OHT cart according to the optimization result.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the OHT cart picking and placing scheduling optimization method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the OHT cart picking and placing scheduling optimization method according to any one of claims 1 to 7.

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