Demand scheduling method for smart storage

Abstract

The invention discloses a demand scheduling method for smart warehousing, and relates to the technical field of task scheduling. The method comprises the steps of receiving a warehouse-in and warehouse-out task sequence; supplementing the tasks in the warehouse-in and warehouse-out task sequence; in the warehouse-in task subsequence and the warehouse-out task subsequence of the supplemented warehouse-in and warehouse-out task sequence, the warehouse-in tasks and the warehouse-out tasks corresponding to the same sorting position are combined, and a warehouse-in and warehouse-out task combination corresponding to the supplemented warehouse-in and warehouse-out task sequence is obtained; initializing a population; optimizing the individuals in the population based on a genetic algorithm to obtain an optimal individual with the shortest completion duration of the corresponding task, and taking the in-warehouse and out-warehouse task combination corresponding to the optimal individual as an optimal in-warehouse and out-warehouse task combination; and carrying out warehouse-in and warehouse-out task scheduling based on the warehouse-in and warehouse-out task sequence corresponding to the optimal warehouse-in and warehouse-out task combination. According to the demand scheduling method for intelligent warehousing, the total time of batch warehouse-in and warehouse-out operation of the warehouse can be effectively shortened, and the warehousing and transportation efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of task scheduling, and in particular relates to a demand scheduling method for smart warehousing. Background Art

[0002] Smart warehousing is an important part of modern logistics. It uses advanced technologies such as the Internet of Things, big data, and artificial intelligence to achieve digitalization, intelligence, and efficiency in warehousing operations. In the smart warehousing system, inbound and outbound task scheduling is one of the core links, which is directly related to the warehouse's operational efficiency and customer satisfaction.

[0003] For the scheduling of inbound and outbound tasks, the warehouse management system or operators usually send the batch name of the goods to the upper computer of the stacker, and the upper computer sends the inbound and outbound instructions to the stacker in sequence, and the stacker performs the corresponding inbound and outbound tasks in sequence based on the received instructions. However, different task execution orders will lead to inconsistent idle time of the stacker during the execution of inbound and outbound tasks. This method often results in too long idle time, resulting in low storage and transportation efficiency.

[0004] Therefore, how to provide an effective solution to improve warehousing and transportation efficiency has become a difficult problem to be solved urgently in the prior art. Summary of the invention

[0005] The purpose of the present invention is to provide a demand scheduling method for smart warehousing to solve the above-mentioned problems existing in the prior art.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a demand scheduling method for smart warehousing, comprising: Receiving a warehouse entry and exit task sequence, wherein the warehouse entry and exit task sequence includes at least one warehouse entry task and at least one warehouse exit task; Completing the tasks in the inbound and outbound task sequence, wherein the number of inbound tasks and outbound tasks in the completed inbound and outbound task sequence is consistent, and the storage locations corresponding to the completed tasks correspond to the reference positions of the stacker; Combine the inbound task subsequence and the outbound task subsequence of the completed inbound and outbound task sequence, corresponding to the same sorting position, to obtain an inbound and outbound task combination corresponding to the completed inbound and outbound task sequence; Initialize a population, wherein the population includes a plurality of individuals corresponding to a plurality of inbound and outbound task combinations; Based on the genetic algorithm, the individuals in the population are optimized to obtain the optimal individual with the shortest corresponding task completion time, and the inbound and outbound task combination corresponding to the optimal individual is used as the optimal inbound and outbound task combination; Inbound and outbound tasks are scheduled based on the inbound and outbound task sequence corresponding to the optimal inbound and outbound task combination.

[0007] Based on the above disclosed content, the present invention receives an inbound and outbound task sequence, wherein the inbound and outbound task sequence includes at least one inbound task and at least one outbound task, completes the tasks in the inbound and outbound task sequence, wherein the number of inbound tasks and outbound tasks in the completed inbound and outbound task sequence is consistent, and the storage location corresponding to the completed task corresponds to the reference position of the stacker, and combines the inbound task subsequence and the outbound task subsequence of the completed inbound and outbound task sequence, corresponding to the same sorting position, to obtain an inbound and outbound task combination corresponding to the completed inbound and outbound task sequence, and then initializes a population, wherein the population includes multiple individuals corresponding one-to-one to multiple inbound and outbound task combinations, and then optimizes the individuals in the population based on a genetic algorithm to obtain the optimal individual with the shortest completion time of the corresponding task, and uses the inbound and outbound task combination corresponding to the optimal individual as the optimal inbound and outbound task combination, and finally performs inbound and outbound task scheduling based on the inbound and outbound task sequence corresponding to the optimal inbound and outbound task combination. In this way, the genetic algorithm can be used to find the combination of inbound and outbound tasks with the shortest task completion time as much as possible for inbound and outbound task scheduling, thereby effectively reducing the total time of batch inbound and outbound operations in the warehouse and improving warehousing and transportation efficiency.

[0008] In a possible design, the optimization of individuals in the population based on a genetic algorithm to obtain the optimal individual with the shortest completion time for the corresponding task includes: Calculate the task completion time of the warehouse entry and exit task combination corresponding to each individual in the multiple individuals; Based on the task completion time of the inbound and outbound task combination corresponding to each individual, determine the fitness value of each individual; Selecting the multiple individuals based on the fitness value of each individual; Perform crossover and mutation operations on the selected individuals to iteratively update the population; When the iteration termination condition is reached or the number of iterations reaches the preset number of iterations, the individual with the shortest task completion time in the latest population is taken as the optimal individual.

[0009] In a possible design, when selecting the multiple individuals based on the fitness value of each individual, the probability of an individual being selected is proportional to the fitness value of the individual.

[0010] In a possible design, the fitness value corresponding to any one of the multiple individuals is ,in , represents the fitness value corresponding to the i-th individual, C represents the preset threshold, It represents the task completion time of the i-th individual for the combination of inbound and outbound tasks. It represents the task completion time of the Kth group of in-and-out tasks in the in-and-out task combination corresponding to the i-th individual, and n represents the number of groups of in-and-out tasks in the in-and-out task combination corresponding to the i-th individual.

[0011] In a possible design, the calculating of the task completion time of the warehouse entry and exit task combination corresponding to each individual in the plurality of individuals includes: Based on the inbound and outbound task combinations corresponding to each individual, determine the stacker operation trajectory corresponding to each individual; Based on the length of the stacker crane operation track corresponding to each individual, the task completion time of the inbound and outbound task combination corresponding to each individual is determined.

[0012] In a possible design, mutation operations are performed on selected individuals, including: The task sequence of the inbound task subsequence and the outbound task subsequence in the inbound and outbound task combination corresponding to the selected individual is adjusted according to the same task sequence adjustment rule.

[0013] In a possible design, the iteration termination condition is that there are individuals in the latest population whose corresponding fitness values ​​are lower than a preset fitness value.

[0014] In a second aspect, the present invention provides a demand scheduling device for smart warehousing, comprising: A receiving unit, used for receiving a warehouse entry and exit task sequence, wherein the warehouse entry and exit task sequence includes at least one warehouse entry task and at least one warehouse exit task; A completing unit, used for completing the tasks in the inbound and outbound task sequence, wherein the number of inbound tasks and outbound tasks in the completed inbound and outbound task sequence is consistent, and the storage location corresponding to the completed task corresponds to the reference position of the stacker; A combining unit is used to combine the inbound task subsequence and the outbound task subsequence of the completed inbound and outbound task sequence, corresponding to the same sorting position, to obtain an inbound and outbound task combination corresponding to the completed inbound and outbound task sequence; An initialization unit, used to initialize a population, wherein the population includes a plurality of individuals corresponding to a plurality of inbound and outbound task combinations; An optimization unit is used to optimize the individuals in the population based on a genetic algorithm to obtain the best individual with the shortest corresponding task completion time, and use the inbound and outbound task combination corresponding to the best individual as the best inbound and outbound task combination; The task scheduling unit is used to schedule the inbound and outbound tasks based on the inbound and outbound task sequence corresponding to the optimal inbound and outbound task combination.

[0015] In a third aspect, the present invention provides an electronic device comprising a memory, a processor and a transceiver which are communicatively connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program to execute the demand scheduling method for smart warehousing as described in the first aspect or any possible design of the first aspect.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed on a computer, executes the demand scheduling method for smart warehousing described in the first aspect or any possible design of the first aspect.

[0017] In a fifth aspect, the present invention provides a computer program product comprising instructions, which, when executed on a computer, causes the computer to execute the demand scheduling method for smart warehousing as described in the first aspect or any possible design of the first aspect.

[0018] Beneficial effects: The present invention receives an inbound and outbound task sequence, wherein the inbound and outbound task sequence includes at least one inbound task and at least one outbound task, completes the tasks in the inbound and outbound task sequence, wherein the number of inbound tasks and outbound tasks in the completed inbound and outbound task sequence is consistent, and the storage location corresponding to the completed task corresponds to the reference position of the stacker, and combines the inbound task subsequence and the outbound task subsequence of the completed inbound and outbound task sequence, corresponding to the same sorting position, to obtain an inbound and outbound task combination corresponding to the completed inbound and outbound task sequence, and then initializes a population, wherein the population includes a plurality of individuals corresponding to a plurality of inbound and outbound task combinations one by one, and then optimizes the individuals in the population based on a genetic algorithm to obtain an optimal individual with the shortest completion time of the corresponding task, and uses the inbound and outbound task combination corresponding to the optimal individual as the optimal inbound and outbound task combination, and finally performs inbound and outbound task scheduling based on the inbound and outbound task sequence corresponding to the optimal inbound and outbound task combination. In this way, the genetic algorithm can be used to find the combination of inbound and outbound tasks with the shortest task completion time as much as possible for inbound and outbound task scheduling, thereby effectively reducing the total time of batch inbound and outbound operations in the warehouse, improving warehousing and transportation efficiency, and facilitating practical application and promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of a demand scheduling method for smart warehousing provided in an embodiment of the present application; Figure 2 A schematic block diagram of a demand scheduling device for smart warehousing provided in an embodiment of the present application; Figure 3 A schematic block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0021] It should be understood that although the terms first, second, etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another unit. For example, a first unit can be referred to as a second unit, and similarly, a second unit can be referred to as a first unit without departing from the scope of the exemplary embodiments of the present invention.

[0022] It should be understood that the term "and / or" that may appear in this article is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can represent three situations: A exists alone, B exists alone, and A and B exist at the same time. The term " / and" that may appear in this article describes another type of association object relationship, indicating that two relationships may exist. For example, A / and B can represent two situations: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this article generally indicates that the previous and next associated objects are in an "or" relationship.

[0023] In order to improve the efficiency of warehousing and transportation, an embodiment of the present application provides a demand scheduling method for smart warehousing, which can effectively reduce the total time of batch warehousing and outbound operations in the warehouse and improve warehousing and transportation efficiency.

[0024] The demand scheduling method for smart warehousing provided in the embodiment of the present application can be applied to a stacker or a host computer for controlling a stacker. It can be understood that the execution subject does not constitute a limitation on the embodiment of the present application.

[0025] The demand scheduling method for smart warehousing provided in an embodiment of the present application will be described in detail below.

[0026] like Figure 1 As shown, it is a flowchart of the demand scheduling method for smart warehousing provided in the first aspect of the embodiment of the present application. The demand scheduling method for smart warehousing may include but is not limited to the following steps S101-S106.

[0027] Step S101: Receive the inbound and outbound task sequence.

[0028] The inbound and outbound task sequence includes at least one inbound task and at least one outbound task.

[0029] In an embodiment of the present application, when scheduling inbound and outbound tasks through smart warehousing, an operator or a warehouse management system may send an inbound and outbound task sequence to a stacker or a host computer of the stacker, and the inbound and outbound task sequence includes at least one inbound task and at least one outbound task.

[0030] Step S102: Complete the tasks in the inbound and outbound task sequence.

[0031] After receiving the inbound and outbound task sequence, the tasks in the inbound and outbound task sequence can be completed so that the number of inbound tasks and outbound tasks in the inbound and outbound task sequence is consistent, and the storage location corresponding to the completed task corresponds to the reference position of the stacker (a preset position to which the stacker needs to return when it is not working or before the next operation).

[0032] It is understandable that if the number of inbound tasks and outbound tasks in the inbound and outbound task sequence is the same, there is no need to complete the inbound and outbound task sequence.

[0033] Step S103: Combine the inbound tasks and outbound tasks corresponding to the same sorting position in the inbound task subsequence and the outbound task subsequence of the completed inbound and outbound task sequence to obtain an inbound and outbound task combination corresponding to the completed inbound and outbound task sequence.

[0034] In the embodiment of the present application, the tasks in the completed inbound and outbound task sequence can be divided into inbound task subsequences and outbound task subsequences according to inbound tasks and outbound tasks.

[0035] For example, the completed inbound and outbound task sequence includes inbound task R1, outbound task C1, inbound task R2, inbound task R3, outbound task C2 and outbound task C3. Inbound task R1, inbound task R2 and inbound task R3 can be divided into the inbound task subsequence, and outbound task C1, outbound task C2 and outbound task C3 can be divided into the outbound task subsequence.

[0036] When combining inbound tasks and outbound tasks, inbound tasks and outbound tasks corresponding to the same sorting position can be combined. Still based on the above example, assuming that the inbound task subsequence is (R1, R2, R3) and the outbound task subsequence is (C1, C2, C3), then the inbound task R1 in the inbound task subsequence can be combined with the outbound task C1 in the outbound task subsequence, the inbound task R2 in the inbound task subsequence can be combined with the outbound task C2 in the outbound task subsequence, and the inbound task R3 in the inbound task subsequence can be combined with the outbound task C3 in the outbound task subsequence. The resulting task combination can be expressed as [(R1, C1) (R2, C2) (R3, C3)]. In the embodiment of the present application, for ease of understanding, the combined inbound and outbound tasks are referred to as a group of inbound and outbound tasks, that is, (R1, C1) can be referred to as a group of inbound and outbound tasks, (R2, C2) can be referred to as a group of inbound and outbound tasks, and (R3, C3) can also be referred to as a group of inbound and outbound tasks.

[0037] Step S104: Initialize a population, which includes a plurality of individuals corresponding to a plurality of inbound and outbound task combinations.

[0038] In the embodiment of the present application, a plurality of inbound and outbound task combinations can be obtained by adjusting the order of the inbound tasks in the inbound task subsequence and / or the order of the outbound tasks in the outbound task subsequence, and each inbound and outbound task combination is regarded as an individual in the population.

[0039] Step S105. Based on the genetic algorithm, the individuals in the population are optimized to obtain the optimal individual with the shortest corresponding task completion time, and the inbound and outbound task combination corresponding to the optimal individual is used as the optimal inbound and outbound task combination.

[0040] Specifically, step S105 may include but is not limited to the following steps S1051-S1055.

[0041] Step S1051. Calculate the task completion time of the warehouse entry and exit task combination corresponding to each individual among the multiple individuals.

[0042] When calculating the task completion time of the in-and-out task combination corresponding to each individual, the stacker operation trajectory corresponding to each individual can be determined based on the in-and-out task combination corresponding to each individual, and then the task completion time of the in-and-out task combination corresponding to each individual can be determined based on the length of the stacker operation trajectory corresponding to each individual.

[0043] Step S1052: Determine the fitness value of each individual based on the task completion time of the inbound and outbound task combination corresponding to each individual.

[0044] Among them, the fitness value corresponding to any individual among the multiple individuals is ,in , represents the fitness value corresponding to the i-th individual, C represents the preset threshold, It represents the task completion time of the i-th individual for the combination of inbound and outbound tasks. It represents the task completion time of the Kth group of in-warehouse and out-of-warehouse tasks (a group of in-warehouse and out-of-warehouse tasks includes an in-warehouse task and a corresponding out-of-warehouse task) in the in-warehouse and out-of-warehouse task combination corresponding to the i-th individual, and n represents the number of groups of in-warehouse and out-of-warehouse tasks in the in-warehouse and out-of-warehouse task combination corresponding to the i-th individual.

[0045] Step S1053: Select the multiple individuals based on the fitness value of each individual.

[0046] When selecting the multiple individuals, the probability of an individual being selected is proportional to the fitness value of the individual, that is, the greater the fitness value of the individual, the greater the probability of being selected, and vice versa.

[0047] Step S1054. Perform crossover and mutation operations on the selected individuals to iteratively update the population.

[0048] Specifically, when the mutation operation is performed on the selected individual, the task order of the inbound task subsequence and the outbound task subsequence in the inbound and outbound task combination corresponding to the selected individual can be adjusted according to the same task order adjustment rule.

[0049] For example, for an incoming task subsequence, if the order of the second incoming task and the third incoming task in the incoming task subsequence is adjusted, the order of the second outgoing task and the third outgoing task in the outgoing task subsequence is adjusted accordingly.

[0050] Step S1055. When the iteration termination condition is reached or the number of iterations reaches the preset number of iterations, the individual with the shortest task completion time in the latest population is taken as the optimal individual.

[0051] The iteration termination condition may be that there are individuals in the latest population whose corresponding fitness values ​​are lower than a preset fitness value.

[0052] Step S106: Based on the inbound and outbound task sequence corresponding to the optimal inbound and outbound task combination, inbound and outbound task scheduling is performed.

[0053] That is, the inbound and outbound tasks are scheduled according to the inbound and outbound task sequence recorded in the inbound and outbound task sequence corresponding to the optimal inbound and outbound task combination.

[0054] The demand scheduling method for smart warehousing provided by the present invention receives an inbound and outbound task sequence, wherein the inbound and outbound task sequence includes at least one inbound task and at least one outbound task, completes the tasks in the inbound and outbound task sequence, wherein the number of inbound tasks and outbound tasks in the completed inbound and outbound task sequence is consistent, and the storage location corresponding to the completed task corresponds to the reference position of the stacker, and the inbound task subsequence of the completed inbound and outbound task sequence and the outbound task subsequence, corresponding to the same sorting position, are combined to obtain an inbound and outbound task combination corresponding to the completed inbound and outbound task sequence, and then a population is initialized, wherein the population includes a plurality of individuals corresponding to a plurality of inbound and outbound task combinations one by one, and then the individuals in the population are optimized based on a genetic algorithm to obtain an optimal individual with the shortest completion time of the corresponding task, and the inbound and outbound task combination corresponding to the optimal individual is used as the optimal inbound and outbound task combination, and finally, based on the inbound and outbound task sequence corresponding to the optimal inbound and outbound task combination, inbound and outbound task scheduling is performed. In this way, the genetic algorithm can be used to find the combination of inbound and outbound tasks with the shortest task completion time as much as possible for inbound and outbound task scheduling, thereby effectively reducing the total time of batch inbound and outbound operations in the warehouse, improving warehousing and transportation efficiency, and facilitating practical application and promotion.

[0055] See also Figure 2 The embodiment of the present application provides a demand scheduling device for smart warehousing, and the demand scheduling device for smart warehousing includes: A receiving unit, used for receiving a warehouse entry and exit task sequence, wherein the warehouse entry and exit task sequence includes at least one warehouse entry task and at least one warehouse exit task; A completing unit, used for completing the tasks in the inbound and outbound task sequence, wherein the number of inbound tasks and outbound tasks in the completed inbound and outbound task sequence is consistent, and the storage location corresponding to the completed task corresponds to the reference position of the stacker; A combining unit is used to combine the inbound task subsequence and the outbound task subsequence of the completed inbound and outbound task sequence, corresponding to the same sorting position, to obtain an inbound and outbound task combination corresponding to the completed inbound and outbound task sequence; An initialization unit, used to initialize a population, wherein the population includes a plurality of individuals corresponding to a plurality of inbound and outbound task combinations; An optimization unit is used to optimize the individuals in the population based on a genetic algorithm to obtain the best individual with the shortest corresponding task completion time, and use the inbound and outbound task combination corresponding to the best individual as the best inbound and outbound task combination; The task scheduling unit is used to schedule the inbound and outbound tasks based on the inbound and outbound task sequence corresponding to the optimal inbound and outbound task combination.

[0056] The working process, working details and technical effects of the demand scheduling device for smart warehousing provided in the second aspect of this embodiment can be found in the first aspect of the embodiment and will not be repeated here.

[0057] like Figure 3 As shown, the third aspect of an embodiment of the present application provides an electronic device, comprising a memory, a processor and a transceiver that are communicatively connected in sequence, wherein the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer program to execute the demand scheduling method for smart warehousing as described in the first aspect of the embodiment.

[0058] For specific examples, the memory may include but is not limited to random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out memory (FIFO) and / or first-in-last-out memory (FILO), etc.; the processor may be but is not limited to a microprocessor of the STM32F105 series, an ARM (Advanced RISC-Machines), an X86 or other architecture processor, or a processor with an integrated NPU (neural-network processing units); the transceiver may be but is not limited to a WiFi (Wireless Fidelity) wireless transceiver, a Bluetooth wireless transceiver, a General Packet Radio Service (GPRS) wireless transceiver, a ZigBee protocol (a low-power local area network protocol based on the IEEE802.15.4 standard, ZigBee) wireless transceiver, a 3G transceiver, a 4G transceiver and / or a 5G transceiver, etc.

[0059] The fourth aspect of this embodiment provides a computer-readable storage medium storing instructions for the demand scheduling method for smart warehousing described in the first aspect of the embodiment, that is, the computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the demand scheduling method for smart warehousing described in the first aspect is executed. The computer-readable storage medium refers to a carrier for storing data, which may include but is not limited to a floppy disk, an optical disk, a hard disk, a flash memory, a USB flash drive, and / or a memory stick, etc., and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0060] The fifth aspect of this embodiment provides a computer program product comprising instructions, which, when executed on a computer, causes the computer to execute the demand scheduling method for smart warehousing as described in the first aspect of the embodiment, wherein the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0061] It should be understood that certain details are provided in the following description to facilitate a complete understanding of the example embodiments. However, it should be understood by those of ordinary skill in the art that the example embodiments can be implemented without these certain details. For example, the system can be shown in a block diagram to avoid obscuring the example with unnecessary details. In other examples, well-known processes, structures, and techniques may not be shown in unnecessary detail to avoid obscuring the example embodiments.

[0062] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A demand scheduling method for smart warehousing, characterized in that: include: Receiving a warehouse entry and exit task sequence, wherein the warehouse entry and exit task sequence includes at least one warehouse entry task and at least one warehouse exit task; Completing the tasks in the inbound and outbound task sequence, wherein the number of inbound tasks and outbound tasks in the completed inbound and outbound task sequence is consistent, and the storage locations corresponding to the completed tasks correspond to the reference positions of the stacker; Combine the inbound task subsequence and the outbound task subsequence of the completed inbound and outbound task sequence, corresponding to the same sorting position, to obtain an inbound and outbound task combination corresponding to the completed inbound and outbound task sequence; Initialize a population, wherein the population includes a plurality of individuals corresponding to a plurality of inbound and outbound task combinations; Based on the genetic algorithm, the individuals in the population are optimized to obtain the optimal individual with the shortest corresponding task completion time, and the inbound and outbound task combination corresponding to the optimal individual is used as the optimal inbound and outbound task combination; Inbound and outbound tasks are scheduled based on the inbound and outbound task sequence corresponding to the optimal inbound and outbound task combination.

2. The demand scheduling method for smart warehousing according to claim 1 is characterized in that: The optimizing of the individuals in the population based on the genetic algorithm to obtain the optimal individual with the shortest completion time of the corresponding task includes: Calculate the task completion time of the warehouse entry and exit task combination corresponding to each individual in the multiple individuals; Based on the task completion time of the inbound and outbound task combination corresponding to each individual, determine the fitness value of each individual; Selecting the multiple individuals based on the fitness value of each individual; Perform crossover and mutation operations on the selected individuals to iteratively update the population; When the iteration termination condition is reached or the number of iterations reaches the preset number of iterations, the individual with the shortest task completion time in the latest population is taken as the optimal individual.

3. The demand scheduling method for smart warehousing according to claim 2 is characterized in that: When selecting the multiple individuals based on the fitness value of each individual, the probability of the individual being selected is proportional to the fitness value of the individual.

4. The demand scheduling method for smart warehousing according to claim 2 is characterized in that: The fitness value corresponding to any individual among the multiple individuals is ,in , represents the fitness value corresponding to the i-th individual, C represents the preset threshold, It represents the task completion time of the i-th individual for the combination of inbound and outbound tasks. It represents the task completion time of the Kth group of in-and-out tasks in the in-and-out task combination corresponding to the i-th individual, and n represents the number of groups of in-and-out tasks in the in-and-out task combination corresponding to the i-th individual.

5. The demand scheduling method for smart warehousing according to claim 2 is characterized in that: The calculating of the task completion time of the warehouse entry and exit task combination corresponding to each individual in the plurality of individuals includes: Based on the inbound and outbound task combinations corresponding to each individual, determine the stacker operation trajectory corresponding to each individual; Based on the length of the stacker crane operation track corresponding to each individual, the task completion time of the inbound and outbound task combination corresponding to each individual is determined.

6. The demand scheduling method for smart warehousing according to claim 2 is characterized in that: Perform mutation operations on the selected individuals, including: The task sequence of the inbound task subsequence and the outbound task subsequence in the inbound and outbound task combination corresponding to the selected individual is adjusted according to the same task sequence adjustment rule.

7. The demand scheduling method for smart warehousing according to claim 2 is characterized in that: The iteration termination condition is that there are individuals in the latest population whose corresponding fitness values ​​are lower than the preset fitness values.

8. A demand scheduling device for smart warehousing, characterized in that: include: A receiving unit, used for receiving a warehouse entry and exit task sequence, wherein the warehouse entry and exit task sequence includes at least one warehouse entry task and at least one warehouse exit task; A completing unit, used for completing the tasks in the inbound and outbound task sequence, wherein the number of inbound tasks and outbound tasks in the completed inbound and outbound task sequence is consistent, and the storage location corresponding to the completed task corresponds to the reference position of the stacker; A combining unit is used to combine the inbound task subsequence and the outbound task subsequence of the completed inbound and outbound task sequence, corresponding to the same sorting position, to obtain an inbound and outbound task combination corresponding to the completed inbound and outbound task sequence; An initialization unit, used to initialize a population, wherein the population includes a plurality of individuals corresponding to a plurality of inbound and outbound task combinations; An optimization unit is used to optimize the individuals in the population based on a genetic algorithm to obtain the best individual with the shortest corresponding task completion time, and use the inbound and outbound task combination corresponding to the best individual as the best inbound and outbound task combination; The task scheduling unit is used to schedule the inbound and outbound tasks based on the inbound and outbound task sequence corresponding to the optimal inbound and outbound task combination.

9. An electronic device, characterized in that: It includes a memory, a processor and a transceiver which are communicatively connected in sequence, wherein the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer program to execute the demand scheduling method for smart warehousing as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or the instruction is executed by a computer, the computer program or the instruction implements the demand scheduling method for smart warehousing as described in any one of claims 1 to 7.

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