A multi-objective picking scheduling method, storage medium and electronic device

Abstract

A multi-objective picking scheduling method, storage medium and electronic device, which include the following steps: combining any two orders in the order pool to be picked to generate an order combination; calculating the similarity of the order combination; based on the similarity of the order combination, screening out several order combinations as the order combinations to be matched; combining each order combination to be matched with other orders again to generate a new order combination, and calculating its multi-objective decision value, and taking the new order combination with the maximum multi-objective decision value as the optimal order combination; adopting the above technical solution to give priority to order combinations with similar paths during the order consolidation process, comprehensively considering the similarity and disruption degree of the order combination, so as to achieve a well-balanced order combination method, avoiding the negative impact on the feasibility, efficiency or effect of subsequent consolidation of multiple orders caused by solely focusing on similarity, contributing to the reasonable allocation and utilization of resources, and providing reliable decision support.

Description

Technical Field

[0001] The present application relates to the technical field of warehousing logistics, and particularly relates to a multi-objective picking scheduling method, a storage medium, and an electronic device. Background Art

[0002] With the rapid development of the e-commerce industry, the warehousing logistics industry is facing challenges such as an increasing number of orders and continuously improving customer requirements for delivery timeliness. In warehousing operations, the picking link is a very crucial one, and its efficiency directly affects the speed and cost of the entire logistics distribution. In the traditional picking mode, the picking method of one picker for one order is often adopted. In this way, the picker only processes one order each time. Even if the order has few items, the picker still has to run back and forth in the warehouse to find the locations of each item, resulting in a large amount of time wasted on the walking distance and low picking efficiency.

[0003] To significantly improve the picking efficiency, it is extremely urgent to deeply explore the feasibility of multi-objective picking scheduling. That is, combining the orders to be picked, and minimizing the total order delay time is the core point to improve customer service satisfaction, enhance the overall operation efficiency of the warehouse, and reduce logistics costs. However, the current multi-objective picking scheduling schemes usually only limit to combining multiple orders with high current priorities according to the similarity of the order paths to form an order combination with the "minimum distance", and as much as possible to increase the picker full rate (the ratio of the number of orders borne by the picker to the workload approaching a relatively saturated state through reasonable order allocation), thereby improving the picking efficiency. But it lacks considering from a global perspective the negative impacts of the current order combination behavior on the feasibility, efficiency, or effect of combining multiple orders subsequently. As a result, the currently calculated order combination with the "minimum distance" destroys the subsequent optimal order combination, reducing the number of orders that can be combined and processed subsequently, or making the processing of subsequent orders more complex and costly. Summary of the Invention

[0004] In view of the above problems, the present application provides a multi-objective picking scheduling method to solve the problem that the existing multi-objective picking scheduling only combines orders according to the similarity of the order paths and ignores the impact on subsequent orders.

[0005] To achieve the above object, the inventor provides a multi-objective picking scheduling method, which includes the following steps:

[0006] Combining any two orders in the order pool to be picked to generate an order combination;

[0007] Calculating the similarity of the order combination;

[0008] Based on the similarity of the order combination, screening out several order combinations as the order combinations to be matched;

[0009] Re - combine each order combination to be matched with other orders to generate new order combinations, calculate their multi - objective decision values, and take the new order combination with the maximum multi - objective decision value as the optimal order combination.

[0010] Further, the similarity of the order combinations is calculated by calculating the similarity between two orders based on the path nodes of the picking paths of the orders.

[0011] Further, the step of screening out several order combinations as the order combinations to be matched based on the similarity of the order combinations includes arranging the order combinations in descending order of their similarity, and selecting the first order combinations as the order combinations to be matched; where is a positive integer.

[0012] Further, the step of arranging the order combinations in descending order of their similarity and selecting the first order combinations as the order combinations to be matched includes constructing an order combination similarity matrix and selecting the first as the order combinations to be matched.

[0013] Further, in the step of re - combining each order combination to be matched with other orders to generate new order combinations, calculating their multi - objective decision values, and taking the new order combination with the maximum multi - objective decision value as the optimal order combination, the following steps are included:

[0014] Obtain the number of picking orders in the current batch ; where is a positive integer, and > 2;

[0015] For each order combination to be matched, perform the following steps:

[0016] Count the number of orders in the order combination to be matched ;

[0017] If , then re - combine each order to be matched with other orders to generate new order combinations;

[0018] Calculate the similarity of the new order combinations;

[0019] Based on the similarity of the new order combinations, screen out several new order combinations and calculate the disruption degree of the new order combinations;

[0020] Calculate the multi - objective decision value of the new order combination according to the corresponding similarity and disruption degree in the new order combination;

[0021] Based on the multi - objective decision value of the new order combinations, screen out the first A new order combination is used as the order combination to be matched; repeat the above steps until ;

[0022] The order combination with the maximum multi-objective decision value is the optimal order combination.

[0023] Further, in the step of calculating the disruption degree of the new order combination,

[0024] The disruption degree of the new order combination = the number of order combinations with the first preset number of other orders / the total number of the first preset number of order combinations.

[0025] Further, in the step of calculating the multi-objective decision value of the new order combination according to the corresponding similarity and disruption degree in the new order combination, the multi-objective decision value

[0026]

[0027] where, represents the similarity, represents the disruption degree, represents the weight coefficient of the similarity, represents the weight coefficient of the disruption degree.

[0028] Further, the weight coefficient of the similarity and the weight coefficient of the disruption degree are obtained by proportional conversion of the mean, maximum or minimum value of the similarity and the disruption degree.

[0029] A storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the multi-objective picking scheduling method are implemented.

[0030] An electronic device includes a memory and a processor, and a computer program is stored on the memory. When the computer program is executed by the processor, the steps of the multi-objective picking scheduling method are implemented.

[0031] Different from the prior art, the above technical solution provides a key basis for subsequent order consolidation decisions by calculating the similarity between different order combinations and screening out the order combinations to be matched, so that order combinations with similar paths are preferentially considered during the order consolidation process, thereby effectively reducing the walking distance of pickers in the warehouse, reducing the time cost, and improving the picking efficiency; then combining the order combinations to be matched with other orders again, calculating the multi-objective decision value, and comprehensively considering the similarity and disruption degree of the order combinations to achieve a well-balanced order combination method, avoiding the negative impact on the feasibility, efficiency or effect of subsequent order consolidation of a single focus on similarity, helping to realize the reasonable allocation and utilization of resources, and providing reliable decision support.

[0032] The above description of the invention content is only an overview of the technical solution of this application. In order to enable those of ordinary skill in the art to more clearly understand the technical solution of this application, and thus be able to implement it based on the content described in the specification and the accompanying drawings, and in order to make the above objects, other objects, features, and advantages of this application more easily understood, the following is described in conjunction with the specific embodiments of this application and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings are only used to illustrate the principles, implementation methods, applications, features, effects, etc. of the specific embodiments of the present invention and other related contents, and should not be considered as a limitation to this application.

[0034] In the accompanying drawings of the specification:

[0035] Figure 1 It is a schematic flowchart of the multi-object picking scheduling method described in the specific embodiment. SPECIFIC EMBODIMENTS

[0036] In order to elaborate in detail the possible application scenarios, technical principles, specific implementable solutions, achievable purposes and effects, etc. of this application, the following is described in detail in conjunction with the specific examples listed and the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of this application, and therefore are only used as examples and cannot be used to limit the protection scope of this application.

[0037] Referring to "embodiments" herein means that the specific features, structures, or characteristics described in connection with the embodiments may be included in at least one embodiment of this application. The term "embodiment" that appears in various positions in the specification does not necessarily refer to the same embodiment, nor is it particularly limited to its independence or relevance to other embodiments. In principle, in this application, as long as there is no technical contradiction or conflict, the technical features mentioned in each embodiment can be combined in any way to form corresponding implementable technical solutions.

[0038] Unless otherwise defined, the meanings of the technical terms used herein are the same as those commonly understood by those of ordinary skill in the technical field to which this application belongs; the use of the relevant terms herein is only for describing specific embodiments and is not intended to limit this application.

[0039] In the description of this application, the term "and / or" is an expression used to describe the logical relationship between objects, indicating that three relationships can exist. For example, A and / or B means: there is A, there is B, and there is both A and B at the same time. In addition, the character " / " in this article generally represents an "or" logical relationship between the associated objects before and after.

[0040] In this application, terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual quantitative, primary-secondary or sequential relationship between these entities or operations.

[0041] Without further limitation, in this application, the open-ended expressions such as "including", "comprising", "having" or other similar expressions used in a statement are intended to cover non-exclusive inclusion. These expressions do not exclude the possibility that there may be additional elements in the process, method or product including the recited elements, so that the process, method or product including a series of elements may include not only those defined elements, but also other elements not explicitly listed, or elements inherent to such process, method or product.

[0042] Similar to the understanding in the Examination Guidelines, in this application, expressions such as "greater than", "less than", "exceeding" are understood not to include the recited number; expressions such as "above", "below", "within" are understood to include the recited number. In addition, in the description of the embodiments of this application, the meaning of "a plurality of" is two or more (including two). Similar expressions related to "plurality", such as "multiple groups", "multiple times", etc., are understood in the same way, unless otherwise specifically defined.

[0043] In the description of the embodiments of this application, the spatially related expressions used, such as "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "perpendicular", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the specific embodiment or the drawing. This is only for the convenience of describing the specific embodiments of this application or facilitating the understanding of the reader, and does not indicate or imply that the device or component referred to must have a specific position, a specific orientation, or be constructed or operated in a specific orientation. Therefore, it should not be construed as a limitation to the embodiments of this application.

[0044] See Figure 1As shown, a multi-objective picking scheduling method is applied in the warehousing and logistics environment. It aims to provide comprehensive and accurate information for order consolidation decisions based on the comprehensive consideration of multiple related orders, so as to achieve efficient and intelligent picking operations. It mainly provides a key basis for subsequent order consolidation decisions by calculating the similarity between different order combinations and screening out the order combinations to be matched, so that order combinations with similar paths are given priority during the order consolidation process, thereby effectively reducing the walking distance of pickers in the warehouse, reducing time costs, and improving picking efficiency. Then, the order combinations to be matched are combined with other orders again, and the multi-objective decision value is calculated, taking into account both the similarity and disruption degree of the order combinations, to achieve a well-balanced order combination method, avoiding the negative impact on the feasibility, efficiency, or effectiveness of subsequent order consolidation by solely focusing on similarity, contributing to the rational allocation and utilization of resources, providing reliable decision support, making the order consolidation decision more scientific and accurate, and thus strongly promoting the development of the warehousing and logistics industry towards intelligence and high efficiency.

[0045] Combined with Figure 1 . The following provides an implementation manner of the multi-objective picking scheduling method, which includes the following steps:

[0046] S1. Combine any two orders in the order pool to be picked to generate an order combination;

[0047] S2. Calculate the similarity of the order combination;

[0048] S3. Based on the similarity of the order combination, screen out several order combinations as the order combinations to be matched;

[0049] S4. Combine each order combination to be matched with other orders again to generate a new order combination, calculate its multi-objective decision value, and take the new order combination with the maximum multi-objective decision value as the optimal order combination.

[0050] The above-mentioned order pool to be picked refers to the set of orders that have received order requests, entered the warehousing picking link, and have not yet undergone actual picking operations; the above-mentioned orders include, but are not limited to, order numbers, customer information, product lists (including product names, specifications, quantities, etc.), estimated delivery times, etc. Combining any two orders in the order pool to be picked to generate order combinations, you can use nested loops to traverse all the orders in the order pool. The outer loop selects the first order, and the inner loop selects the second order, and combines the two orders together. Use nested loops to traverse all the orders in the order pool. The outer loop selects the first order, and the inner loop selects the second order, and combines the two orders together. To avoid duplicate combinations (for example, the combination of order A and order B is the same as the combination of order B and order A), the inner loop starts from the next position of the current index of the outer loop, and stores the generated order combinations in the database for further analysis and processing later; the above-generated order combinations can be stored in the form of a list or a matrix.

[0051] The similarity of the above-mentioned order combinations refers to the path similarity of the orders within the order combination. Each order has a specific picking path due to the storage location of the products in the order in the warehouse. By calculating the similarity of the order combinations, the path similarity of the orders in the order combination is obtained. If the path similarities of two order combinations are relatively high, it means that there is a large overlapping part in their picking paths in the warehouse, providing a key basis for subsequent order consolidation decisions. Enabling us to give priority to those order combinations with similar paths during the order consolidation process, thereby effectively reducing the walking distance of pickers in the warehouse, reducing time costs, and improving picking efficiency. The similarity of order combinations can be calculated based on the similarity of picking path directions, the similarity of picking path nodes, etc. The following will further illustrate based on the similarity of path nodes, etc. The calculation of the similarity of order combinations is to calculate the similarity between two orders according to the path nodes of the picking paths of the orders, that is, abstract the order paths into node sequences, and measure the similarity by comparing the overlapping degree of the node sets; specifically, represent the picking path of each order as a node sequence, and the nodes can be shelves, storage locations, etc. in the warehouse. For example, the path of order A is , and the path of order B is ; find the intersection and union of the two order path node sets. The intersection is , and the union is ; then the similarity is:

[0052]

[0053] where ∣A∩B∣ represents the number of path nodes in the intersection, and ∣A∪B∣ represents the number of path nodes in the union. For orders A and B, the similarity is 0.33.

[0054] The above similarity based on order combinations is used to screen out several order combinations as the order combinations to be matched, providing a key basis for subsequent order consolidation decisions, so that order combinations with similar paths are given priority in the order consolidation process. In some embodiments, a similarity threshold is preset, and the calculated similarity of the order combinations is compared with the similarity threshold, and the order combinations with a similarity greater than the similarity threshold are used as the order combinations to be matched. In some embodiments, the order combinations are arranged according to the magnitude of their similarities; the first order combinations are selected as the order combinations to be matched; where is a positive integer; regardless of the specific numerical distribution of the similarities, a fixed number of order combinations can be selected as the order combinations to be matched according to actual needs, accurately controlling the number of order combinations participating in subsequent processing, making the entire order processing process more stable and predictable. Specifically, an order combination similarity matrix can be constructed, and the calculated similarities of the order combinations are filled in correspondingly, and the first are selected as the order combinations to be matched. The order combination similarity matrix can intuitively display the similarity relationships between all order combinations in a tabular form. Through the matrix, it can be quickly understood which order combinations have high similarities, and the overall data structure is clear at a glance, facilitating the comparison and sorting of the similarities of each order combination. The first order combinations are selected according to the magnitude of the similarities. This screening method is simple and direct, and can accurately obtain the most similar order combinations.

[0055] The above step of combining each order combination to be matched with other orders again to generate new order combinations, calculating their multi-objective decision values, and taking the new order combination with the maximum multi-objective decision value as the optimal order combination includes the following steps:

[0056] Obtain the number of picking orders in the current batch ; where is a positive integer, and > 2;

[0057] For each order combination to be matched, perform the following steps:

[0058] Count the number of orders in the order combination to be matched ;

[0059] If , then combine each order to be matched with other orders again to generate new order combinations;

[0060] Calculate the similarity of the new order combinations;

[0061] Based on the similarity of the new order combinations, screen out several new order combinations and calculate the disruption degree of the new order combinations;

[0062] Calculate the multi-objective decision value of the new order combination according to the corresponding similarity and disruption degree in the new order combination;

[0063] Based on the new order combination according to the multi-objective decision value, screen out several new order combinations as the order combinations to be matched; Repeat the above steps until ;

[0064] The order combination with the maximum multi-objective decision value is the optimal order combination.

[0065] The quantity of the current batch of picking orders refers to the quantity of orders borne by the current picker. During the order consolidation process, the quantity of the current batch of picking orders continuously plays a guiding role to make the order quantity within the final order combination equal to the quantity of orders borne by the current picker, and improve the picker's full dispatch rate as much as possible. If the order quantity within the order combination to be matched is much lower than the quantity of the current batch of picking orders , it means that order consolidation can continue, and each order combination to be matched can be combined with other orders again.

[0066] The above calculation of the similarity of the new order combination is the same as the above calculation of the similarity of the order combination. The above calculation of the similarity of the new order combination can also be obtained by calculating based on the similarity of the picking path direction, the similarity of the picking path nodes, etc. Based on the similarity of the new order combination, screening out several new order combinations to calculate the disruption degree of the new order combination is to give priority to those order combinations with similar paths during the subsequent continuous order consolidation process, so as to improve the picking efficiency. Similarly, in some embodiments, a similarity threshold is preset, and the calculated similarity of the new order combination is compared with the similarity threshold, and the disruption degree of the new order combination greater than the similarity threshold is calculated. In some embodiments, the new order combinations are arranged in ascending order of their similarity; the disruption degree of the first few new order combinations is calculated; Regardless of the specific numerical distribution of the similarity, the number of order combinations participating in the subsequent processing can be accurately controlled, making the entire order processing process more stable and predictable.

[0067] The above calculation of the disruption degree of the new order combination refers to adding other orders to a to-be-matched order combination, and the negative impact degree of the other orders on the subsequent order combination consolidation. Specifically, the disruption degree of the new order combination = the number of order combinations with the first preset number of the other orders / the total number of the first preset number of order combinations.

[0068] The above target decision value is a quantitative index for comprehensively evaluating the pros and cons of order combinations. In an order combination, it is a value calculated based on the similarity and disruption degree of the order combination, integrating information of different objectives into a single value. The higher the value, the better the order combination after comprehensively considering all objectives. For example, when calculating the multi-objective decision value of an order combination, if a certain order combination has a higher similarity and a lower disruption degree, then its multi-objective decision value will be relatively large, indicating that this order combination is an ideal solution after comprehensively considering factors such as similarity and disruption degree.

[0069] The step of calculating the multi-objective decision value of the new order combination according to the corresponding similarity and disruption degree in the new order combination can be calculated by methods such as weighted summation method, analytic hierarchy process, and ideal solution method (TOPSIS method). In some embodiments, the multi-objective decision value

[0070]

[0071] Among them, represents the similarity, represents the disruption degree, represents the weight coefficient of the similarity, represents the weight coefficient of the disruption degree.

[0072] The weight coefficient of the similarity and the weight coefficient of the disruption degree are obtained by proportional conversion based on the mean value, maximum value or minimum value ratio of the similarity and the disruption degree.

[0073] The following further illustrates the present application by taking the proportional conversion based on the mean value as an example.

[0074] Calculate the ratio of the mean value of the similarity to the mean value of the disruption degree

[0075]

[0076] Then the weight coefficient of the similarity

[0077]

[0078] Then the weight coefficient of the disruption degree

[0079]

[0080] The step of screening out several new order combinations as the order combinations to be matched according to the multi-objective decision value based on the new order combinations provides a key basis for subsequent order consolidation decisions, so that order combinations with higher multi-objective decision values are given priority in the order consolidation process. Similarly, in some embodiments, a target decision threshold is preset, the target decision value of the calculated order combination is compared with the target decision threshold, and the order combinations greater than the target decision threshold are used as the order combinations to be matched. In some embodiments, the order combinations are arranged according to the magnitude of their target decision values; the first several order combinations are selected as the order combinations to be matched; regardless of the specific numerical distribution of the target decision values, a fixed number of order combinations can be selected as the order combinations to be matched according to actual needs, accurately controlling the number of order combinations participating in subsequent processing, making the entire order processing process more stable and predictable.

[0081] To further illustrate the present application, the following provides a specific implementation manner of a multi-objective picking scheduling method. The number of picking orders in the current batch is 3, and an order combination similarity matrix is constructed according to the orders in the order pool to be picked, as shown in Table 1.

[0082] Table 1

[0083] o1 o2 o3 o4 o5 o6 o7 o8 o9 o0 o1 1 0.69 0.28 0.85 0.3 0.18 0.92 0.72 0.69 0.80 o2 0.69 1 0.21 0.59 0.11 0.52 0.03 0.57 0.24 0.11 o3 0.28 0.21 1 0.49 0.24 0.54 0.61 0.6 0.15 0.02 o4 0.85 0.59 0.49 1 0.90 0.59 0.78 0.25 0.84 0.09 o5 0.3 0.11 0.24 0.90 1 0.7 0.18 0.36 0.47 0.22 o6 0.18 0.52 0.54 0.59 0.7 1 0.86 0.2 0.33 0.93 o7 0.92 0.03 0.61 0.78 0.18 0.86 1 0.34 0.41 0.76 o8 0.72 0.57 0.6 0.25 0.36 0.2 0.34 1 0.29 0.39 o9 0.69 0.24 0.15 0 84 0.47 0.33 0.41 0.28 1 0 o0 0.80 0.11 0.02 0.09 0.22 0.93 0.76 0.39 0 1

[0084] Among them, o0 - o9 represent the numbers of different orders, and the order combinations are arranged as

[0085] (o6, o0), (o1, o7), (o4, o5), (o6, o7), (o9, o4),...;

[0086] Assume = 3, then the first 3 order combinations are selected as the order combinations to be matched, that is, the order combinations to be matched are: (o6, o0), (o1, o7), (o4, o5);

[0087] Based on the similarity of the new order combinations, several new order combinations are screened out as follows:

[0088] (o6, o0, o7), (o6, o0, o1), (o6, o0, o7)

[0089] (o1, o7, o6), (o1, o7, o4), (o1, o7, o0)

[0090] (o4, o5, o1), (o4, o5, o9), (o4, o5, o0).

[0091] Among them, the similarity of the new order combinations:

[0092] (o6, o0, o7) = 1.79

[0093] (o6, o0, o1) = 1.73

[0094] (o6, o0, o7) = 1.49

[0095] (o1, o7, o6) = 1.78

[0096] (o1, o7, o4) = 1.77

[0097] (o1, o7, o0) = 1.72

[0098] (o4, o5, o1) = 1.75

[0099] (o4, o5, o9) = 1.74

[0100] (o4, o5, o0) = 1.68.

[0101] Take (o6, o0, o7) as an example to calculate the disruption degree of the new order combination, that is, the proportion of order o7 in the previous preset number of order combinations.

[0102] Assume that the previous preset number of order combinations is:

[0103] (o6, o0), (o1, o7), (o4, o5), (o6, o7), (o9, o4);

[0104] Then

[0105] (o6, o0, o7) = 2 / 5

[0106] (o6, o0, o1) = 1 / 5

[0107] (o6, o0, o7) = 2 / 5

[0108] (o1, o7, o6) = 2 / 5

[0109] (o1, o7, o4) = 2 / 5

[0110] (o1, o7, o0) = 1 / 5

[0111] (o4, o5, o1) = 1 / 5

[0112] (o4, o5, o9)= 1 / 5

[0113] (o4, o5, o0)= 2 / 5。

[0114] Subsequently, only by setting parameters can the corresponding multi-objective decision values be obtained. The order combination with the largest target decision value is the optimal order combination, which will not be elaborated here.

[0115] The processor described in the embodiments of the present application can be implemented by hardware, firmware, software, or a combination thereof, and can use at least one of circuits, single or multiple application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), central processing units (CPUs), controllers, microcontrollers, and microprocessors. It also includes other physical, biological, or chemical structures that can achieve functions similar to or equivalent to those of the above-listed processors, such as biological neurons, quantum computing units, DNA computing units, etc., so that the processor can execute some steps, all steps, or any combination of the steps mentioned in the computer programs or methods involved in the various embodiments of the present application.

[0116] The computer programs involved in the embodiments can be stored in a computer device-readable storage medium, which includes but is not limited to magnetic disks, magnetic tapes, magnetic cards, floppy disks, flash memories, optical discs, optical cards, read-only memories (ROMs), random access memories (RAMs), erasable programmable ROMs (EPROMs), and electrically erasable programmable ROMs (EEPROMs), etc., and also includes other biological, physical, or chemical structures that can achieve functions similar to or equivalent to those of the above-listed storage media, such as units with information storage capabilities like DNA, RNA, proteins, etc. In a specific embodiment, the storage medium involved can be one of the above medium types or a combination of the above medium types. In different embodiments, the computer programs involved in the embodiments can be stored centrally in a single medium or distributed among multiple media. The memory containing the computer device-readable storage medium can be a non-volatile memory or a random access memory. These computer device-readable storage media can be built into the device or can be connected to the device involved in the embodiment as an external device or a part of an external device. In some embodiments, the memory with the computer device-readable storage medium is deployed locally; in other embodiments, a scheme of deploying the memory away from the processor can also be adopted, such as a network-attached memory accessed via an RF circuit or an external port and a communication network, where the communication network can be the Internet, one or more intranets, local area networks (LANs), wide area wireless networks (WLANs), storage area networks (SANs), etc., or an appropriate combination thereof, as long as the computer device can access the memory. In addition, the computer programs involved in the embodiments can be stored in plaintext / ciphertext form or can be designed as training data and be integrated and reorganized and implicitly stored in the parameter states of a deep neural network or other machine learning models through model training.

[0117] Finally, it should be noted that although the above embodiments have been described in the text and drawings of the specification of this application, the patent protection scope of this application cannot be limited thereby. Any technical solutions obtained by equivalent structure or equivalent process substitution or modification based on the substantial concept of this application and using the content recorded in the text and drawings of the specification of this application, as well as directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, etc., are all included in the patent protection scope of this application.

Claims

1. A multi-objective picking scheduling method, characterized in that: The following steps are involved: Combine any two orders in the order pool to be picked to generate an order combination; Calculate the similarity of order combinations; Based on the similarity of the order combinations, a plurality of order combinations are screened out as order combinations to be matched; the step of screening out a plurality of order combinations as order combinations to be matched based on the similarity of the order combinations comprises arranging the order combinations from large to small according to their similarity; Combine each order combination to be matched with other orders to generate a new order combination, and calculate its multi-objective decision value. The new order combination with the maximum multi-objective decision value is taken as the optimal order combination. The step of combining each order combination to be matched with other orders again to generate a new order combination, calculating its multi-objective decision value, and taking the new order combination with the maximum multi-objective decision value as the optimal order combination includes the following steps: Get the current batch picking order quantity ;in, is a positive integer, and >2; For each order combination to be matched, perform the following steps: Count the number of orders in the order combination to be matched ; like , then each order combination to be matched is combined with other orders to generate a new order combination; Calculate the similarity of new order combinations; Based on the similarity of the new order combination, several new order combinations are screened out to calculate the destructiveness of the new order combination; in the step of calculating the destructiveness of the new order combination, The degree of destruction of the new order combination = the number of order combinations with the previously preset number of other orders / the total number of order combinations with the previously preset number; According to the corresponding similarity and damage degree in the new order combination, the multi-objective decision value of the new order combination is calculated; Based on the new order combination, the multi-objective decision values ​​are arranged from large to small, and the top A new order combination is used as a new order combination to be matched; repeat the above steps until ; The new order combination with the maximum value of the multi-objective decision value is the optimal order combination.

2. The multi-objective picking scheduling method according to claim 1, characterized in that: The calculation of the similarity of the order combination is to calculate the similarity between two orders according to the path nodes of the picking paths of the orders.

3. The multi-objective picking scheduling method according to claim 1, characterized in that: The step of selecting a plurality of order combinations as order combinations to be matched based on the similarity of the order combinations also includes selecting the previous order combinations as the order combinations to be matched, where is a positive integer; The step of using order combinations as order combinations to be matched includes constructing an order combination similarity matrix.

4. The multi-objective picking scheduling method according to claim 1, characterized in that: In the step of calculating the multi-objective decision value of the new order combination according to the corresponding similarity and damage degree in the new order combination, the multi-objective decision value The calculation is as follows: ； in, Indicates similarity, Indicates the degree of destruction, Represents the weight coefficient of similarity, The weight coefficient representing the degree of damage.

5. The multi-objective picking scheduling method according to claim 4, characterized in that: The weight coefficient of the similarity is obtained by converting the ratio of the mean, maximum or minimum value of the similarity; the weight coefficient of the destructiveness is obtained by converting the ratio of the mean, maximum or minimum value of the destructiveness.

6. A storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the multi-objective picking scheduling method according to any one of claims 1 to 5 are implemented.

7. An electronic device, characterized in that It includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the multi-objective picking scheduling method as described in any one of claims 1 to 5 are implemented.

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