Method, Electronic Device, and Computer Storage Medium for Purchase Planning

Abstract

Embodiments of the present disclosure relate to a method, an electronic device, and a computer storage medium for inbound planning, and relate to the field of information processing. According to the method, single-case transportation-related data associated with a product and a set of demand quantities are obtained, the product is associated with a target inbound point, and the set of demand quantities includes a plurality of demand quantities for the product in a plurality of time periods within a cycle; based on the single-case transportation-related data and the set of demand quantities, a total constraint of an objective function with respect to a set of transportation quantities, a set of transportation case numbers, and a set of bulk quantities of the product in a plurality of time periods is generated; and based on the total constraint, by minimizing the objective function, a set of transportation quantities, a set of transportation case numbers, and a set of bulk quantities are generated. Thereby, the efficiency of inbound planning can be improved.

Description

Technical Field

[0001] Embodiments of the present disclosure generally relate to the field of information processing, and more particularly to methods, electronic devices, and computer storage media for inventory planning. Background Art

[0002] Services provided by stores such as restaurants need to be delivered from suppliers or warehouses. In traditional methods, people often decide the inventory frequency and quantity based on experience. On the one hand, the efficiency is low. On the other hand, a globally optimized inventory plan is not formulated, which easily leads to situations such as insufficient store warehouses or excessive transportation costs, posing great challenges to the development of the store. Summary of the Invention

[0003] A method, an electronic device, and a computer storage medium for inventory planning are provided, which can improve the efficiency of inventory planning.

[0004] According to a first aspect of the present disclosure, a method for inventory planning is provided. The method includes: obtaining single-case transportation-related data associated with goods and a set of demand quantities, where the goods are associated with a target inventory point, and the set of demand quantities includes multiple demand quantities for the goods in multiple time periods within a cycle; generating a total constraint of the objective function with respect to a set of transportation quantities, a set of transportation boxes, and a set of bulk quantities of the goods in multiple time periods based on the single-case transportation-related data and the set of demand quantities; and generating the set of transportation quantities, the set of transportation boxes, and the set of bulk quantities by minimizing the objective function based on the total constraint.

[0005] According to a second aspect of the present disclosure, an electronic device is provided. The electronic device includes: at least one processor, and a memory communicatively connected to the at least one processor, where the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to the first aspect.

[0006] In a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0007] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. Brief Description of the Drawings

[0008] In combination with the drawings and with reference to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent. In the drawings, the same or similar reference numerals denote the same or similar elements, where:

[0009] Figure 1 is a schematic diagram of an information processing environment 100 according to an embodiment of the present disclosure;

[0010] Figure 2 is a schematic diagram of a method 200 for purchase planning according to an embodiment of the present disclosure;

[0011] Figure 3 is a schematic diagram of a method 300 for purchase planning according to an embodiment of the present disclosure;

[0012] Figure 4 is a schematic diagram of a method 400 for generating a second constraint according to an embodiment of the present disclosure;

[0013] Figure 5 is a schematic diagram of a method 500 for generating a first sub-constraint according to an embodiment of the present disclosure;

[0014] Figure 6 is a schematic diagram of a method 600 for generating a third constraint according to an embodiment of the present disclosure;

[0015] Figure 7 is a schematic block diagram of a method 700 for generating a total constraint according to an embodiment of the present disclosure; and

[0016] Figure 8 is a block diagram of an electronic device for implementing the method for purchase planning according to an embodiment of the present disclosure. Detailed Embodiments

[0017] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings. Various details of the embodiments of the present disclosure are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted below.

[0018] As used herein, the term "including" and its variants mean open inclusion, i.e., "including but not limited to". Unless otherwise specified, the term "or" means "and / or". The term "based on" means "at least partially based on". The term "an example embodiment" and "an embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc. may refer to different or the same objects. There may also be other explicit and implicit definitions below.

[0019] As described above, traditional purchasing planning is carried out manually, with low efficiency and without overall consideration. In addition, when planning for the layout of new stores, the warehouse area of new stores is often calculated manually based on demand, wasting a large amount of human resources and taking too much time.

[0020] To at least partially solve one or more of the above problems and other potential problems, example embodiments of the present disclosure propose a solution for purchasing planning. In this solution, a computing device obtains single-case transportation-related data associated with goods and a set of demand quantities. The goods are associated with a target purchasing point, and the set of demand quantities includes multiple demand quantities for the goods in multiple time periods within a cycle. The computing device generates a total constraint of the objective function with respect to the set of transportation quantities, the set of transportation case numbers, and the set of bulk quantities of the goods in multiple time periods based on the single-case transportation-related data and the set of demand quantities, and generates the set of transportation quantities, the set of transportation case numbers, and the set of bulk quantities by minimizing the objective function based on the total constraint. In this way, considering the constraints among the purchase quantity, the demand quantity, and the objective function of the goods, the purchasing transportation quantity, the purchasing case number, and the purchasing bulk quantity of each good in each time period can be calculated for the target purchasing point, realizing the minimization of the cycle transportation cost and / or the overall storage volume, and improving the efficiency of the purchasing planning for the target purchasing point.

[0021] Hereinafter, specific examples of this solution will be described in more detail with reference to the accompanying drawings.

[0022] Figure 1 FIG. 100 shows a schematic diagram of an example of an information processing environment 100 according to an embodiment of the present disclosure. The information processing environment 100 may include a computing device 110, single-case transportation-related data 120 associated with goods, a set of demand quantities 130, and a set of transportation quantities 140, a set of transportation case numbers 150, and a set of bulk quantities 160 of the goods in multiple time periods.

[0023] The single-case transportation-related data 120 may include the quantity that can be accommodated in a single case. In addition, the single-case transportation-related data may further include single-case transportation cost data. As an alternative or addition, the single-case transportation-related data may further include the volume of a single case and a set of consumption quantities of the goods in multiple time periods.

[0024] The goods are associated with a target purchasing point. The goods may be one or more. The set of demand quantities includes multiple demand quantities for the goods in multiple time periods within a cycle. The set of transportation quantities 160 includes multiple transportation quantities for the goods in multiple time periods within a cycle. The set of transportation case numbers 170 includes multiple transportation case numbers for the goods in multiple time periods within a cycle. The set of bulk quantities 180 includes multiple bulk quantities for the goods in multiple time periods within a cycle. It should be understood that the bulk quantity here refers to the quantity of the goods transported in bulk.

[0025] The computing device 110 includes, for example, but is not limited to, a personal computer, a desktop computer, a laptop computer, a smart phone, a server computer, a multiprocessor system, a mainframe computer, a distributed computing environment including any one of the above systems or devices, etc.

[0026] The computing device 110 is used to obtain single-case transportation related data 120 associated with the goods and a set of demand quantities 130. The goods are associated with a target receiving point. The set of demand quantities 130 includes multiple demand quantities for the goods in multiple time periods within a cycle; based on the single-case transportation related data 120 and the set of demand quantities 130, generate a total constraint of the objective function with respect to the set of transportation quantities 140, the set of transportation case numbers 150, and the set of bulk quantities 160 of the goods in multiple time periods; and based on the total constraint, generate the set of transportation quantities 140, the set of transportation case numbers 150, and the set of bulk quantities 160 by minimizing the objective function.

[0027] Thus, considering the constraints between the incoming quantity and the demand quantity of the goods, as well as the constraints related to the cycle transportation cost and / or the storage volume related constraints, it is possible to calculate the incoming transportation quantity, the incoming case number, and the incoming bulk quantity of each good in each time period for the target receiving point, minimize the cycle transportation cost and / or the overall storage volume, and improve the efficiency of the incoming planning for the target receiving point.

[0028] Figure 2 The flowchart of a method 200 for incoming planning according to an embodiment of the present disclosure is shown. For example, the method 200 can be executed by a computing device 110 as Figure 1 shown. It should be understood that the method 200 may further include additional blocks not shown and / or the shown blocks may be omitted, and the scope of the present disclosure is not limited in this regard.

[0029] At block 202, the computing device 110 obtains single-case transportation related data 120 associated with the goods and a set of demand quantities 130. The goods are associated with a target receiving point. The set of demand quantities 130 includes multiple demand quantities for the goods in multiple time periods within a cycle.

[0030] The single-case transportation related data 120 may include the quantity that can be accommodated in a single case. In addition, the single-case transportation related data may further include single-case transportation cost data. As an alternative or in addition, the single-case transportation related data may further include the single-case volume and the set of consumption quantities of the goods in multiple time periods.

[0031] At block 204, the computing device 110 generates a total constraint of the objective function with respect to the set of transportation quantities 140, the set of transportation case numbers 150, and the set of bulk quantities 160 of the goods in multiple time periods based on the single-case transportation related data 120 and the set of demand quantities 130.

[0032] In some embodiments, the computing device 110 may generate a first set of constraints associated with a plurality of time periods. Each first constraint in the first set of constraints is such that, for at least one time period included in the period from the start of the cycle to the associated time period, the sum of at least one transportation quantity of the goods is greater than or equal to the sum of at least one demand quantity of the goods for at least one time period.

[0033] In addition, the computing device 110 may also generate a second constraint of the total cycle transportation cost with respect to the set of transportation container numbers and the set of bulk quantities of the goods in a plurality of time periods based on the single-container transportation cost data and the single-container capacity. As an alternative or in addition, the computing device 110 may generate a third constraint of the overall storage volume with respect to the set of transportation quantities of the goods in a plurality of time periods based on the single-container capacity, the single-container volume, and the set of consumption quantities.

[0034] At block 206, the computing device 110 generates a set of transportation quantities 140, a set of transportation container numbers 150, and a set of bulk quantities 160 by minimizing an objective function based on the total constraints.

[0035] In some embodiments, the objective function includes the total cycle transportation cost. As an alternative or in addition, in some embodiments, the objective function may include the overall storage volume.

[0036] Thus, it is possible to calculate the inbound transportation quantity, the inbound container number, and the inbound bulk quantity of each good in each time period for the target inbound point, minimize the objective function, and improve the efficiency of the inbound planning of the target inbound point, taking into account the inbound quantity, the demand quantity, and the constraints of the objective function of the goods.

[0037] Figure 3 FIG. shows a flowchart of a method 300 for inbound planning according to an embodiment of the present disclosure. For example, the method 300 may be executed by a computing device 110 as Figure 1 shown. It should be understood that the method 300 may further include additional blocks not shown and / or may omit the shown blocks, and the scope of the present disclosure is not limited in this regard.

[0038] At block 302, the computing device 110 obtains a single-container capacity 120 and a set of demand quantities 130 associated with the goods. The goods are associated with a target inbound point, and the set of demand quantities 130 includes a plurality of demand quantities for the goods in a plurality of time periods within a cycle.

[0039] The target inbound point includes, for example, but is not limited to, a sales store, an e-commerce warehouse, etc. The sales store includes, for example, a restaurant, a supermarket, a grocery store, etc. The goods may be one or more.

[0040] The period includes, for example, but is not limited to, one week, two weeks, one month, etc. The time period includes, for example, but is not limited to, one day, half a day, one week, etc. Multiple time periods within a period include, for example, but are not limited to, 7 days within one week, 30 days within one month, 4 weeks within one month, and so on.

[0041] In some embodiments, the computing device 110 may obtain a set of sales quantities associated with a sales identifier from the raw sales data associated with a target stocking point, and the set of sales quantities includes multiple demand quantities for the sales identifier in multiple time periods within a period.

[0042] Subsequently, the computing device 110 may convert the set of sales quantities into a set of demand quantities 130 associated with the goods based on the association between the sales identifier and the goods identifier of the goods.

[0043] Thus, multiple demand quantities for the associated sales identifier in multiple time periods within a period can be extracted from the raw sales data.

[0044] In some embodiments, multiple goods may be divided into multiple sets of goods associated with multiple goods types. Each set of goods in the multiple sets of goods may include at least one good. The multiple goods types include, for example, but are not limited to, dry goods, frozen goods, and wet goods. The multiple goods may be divided into a set of goods belonging to dry goods, a set of goods belonging to frozen goods, and a set of goods belonging to wet goods. It should be understood that dry goods, frozen goods, and wet goods are only examples, and the goods types may also be in other forms, such as fresh produce, meat, fruits and vegetables, snacks, alcoholic beverages, etc., and the scope of the present disclosure is not limited herein.

[0045] At block 304, the computing device 110 obtains at least one of the following: single-case transportation cost data, and the single-case volume associated with the goods and the set of consumption quantities of the goods in multiple time periods.

[0046] For example, the computing device 110 may obtain the single-case transportation cost data. Also, for example, the computing device 110 may obtain the single-case volume associated with the goods and the set of consumption quantities of the goods in multiple time periods. Further, for example, the computing device 110 may obtain the single-case transportation cost data, and the single-case volume associated with the goods and the set of consumption quantities of the goods in multiple time periods.

[0047] In some embodiments, the single-case transportation cost data may include multiple single-case transportation costs associated with multiple goods types, such as the single-case transportation cost of dry goods, the single-case transportation cost of frozen goods, and the single-case transportation cost of wet goods.

[0048] It should be understood that each set of consumption quantities in the multiple sets of consumption quantities includes multiple consumption quantities of the corresponding goods in multiple time periods.

[0049] At block 306, computing device 110 generates a first set of constraints associated with multiple time periods, where each first constraint in the first set of constraints is such that the sum of at least one transportation quantity of a good during at least one time period included in the period from the start of the cycle to the associated time period is greater than or equal to the sum of at least one demand quantity of the good during at least one time period.

[0050] The first constraint can be represented by the following formula (1):

[0051]

[0052] where j represents the j-th time period, i represents the i-th good, m represents the number of time periods in the cycle, n represents the number of goods, where n is greater than or equal to 1, y ij represents the transportation quantity of the i-th good in the j-th time period, D ij represents the demand quantity of the i-th good in the j-th time period, and formula (1) represents that the sum of k transportation quantities of good i during k time periods from the 1st time period to the k-th time period is greater than or equal to the sum of k demand quantities of good i during these k time periods.

[0053] At block 308, computing device 110 performs at least one of the following: generating a second constraint on the total cycle transportation cost with respect to the set 150 of transportation boxes and the set 160 of bulk quantities of goods during multiple time periods based on the per-box transportation cost data and the per-box capacity, and generating a third constraint on the total storage volume with respect to the set 160 of transportation quantities of goods during multiple time periods based on the per-box capacity, the per-box volume, and the set of consumption quantities of goods during multiple time periods.

[0054] The second constraint can be represented by the following formula (2):

[0055]

[0056] where Tcost represents the total cycle transportation cost, tcost represents the per-box transportation cost data, z ij represents the number of transportation boxes of the i-th good in the j-th time period, n ij represents the bulk quantity of the i-th good in the j-th time period, N i represents the per-box capacity of the i-th good.

[0057] The third constraint can be represented by the following formula (3):

[0058]

[0059] where Vtot represents the total storage volume, y ij represents the transportation quantity of the i-th good in the j-th time period, Consume ijrepresents the consumption quantity of the i-th item in the j-th period, N i represents the quantity that can be accommodated in a single box of the i-th item, V i represents the volume of a single box of the i-th item. Formula (3) indicates that the total storage volume is greater than or equal to the difference obtained by subtracting the sum of multiple consumption quantity sets of multiple items in multiple periods from the sum of multiple transportation quantity sets of multiple items in multiple periods.

[0060] At block 310, based on at least one of the second constraint and the third constraint and the first constraint set, the computing device 110 generates a transportation quantity set 140, a transportation box number set 150, and a bulk quantity set 160 of items in multiple periods with the goal of minimizing at least one of the periodic transportation total cost and the total storage volume.

[0061] For example, the computing device 110 generates a first goal of minimizing the periodic transportation total cost, min(Tcost), and / or a second goal of minimizing the total storage volume, min(Vtot). Subsequently, the computing device 110 can generate a transportation quantity set 140, a transportation box number set 150, and a bulk quantity set 160 of items in multiple periods based on at least one of the second constraint and the third constraint and the first constraint set, and based on at least one of the first goal and the second goal, via Mixed Integer Linear Programming (MILP).

[0062] Thus, considering the constraints between the incoming quantity and the demand quantity of items, as well as the constraints related to the periodic transportation cost and / or the storage volume, the incoming transportation quantity, incoming box number, and incoming bulk quantity of each item in each period can be calculated for the target incoming point, achieving the minimization of the periodic transportation cost and / or the total storage volume, and improving the efficiency of the incoming planning for the target incoming point.

[0063] Figure 4 shows a flowchart of a method 400 for generating a second constraint according to an embodiment of the present disclosure. For example, the method 400 can be executed by a computing device 110 as shown in Figure 1 It should be understood that the method 400 may further include additional blocks not shown and / or the shown blocks may be omitted, and the scope of the present disclosure is not limited in this regard.

[0064] At block 402, for each item type among multiple item types, based on the transportation cost per box associated with the item type, the computing device 110 generates a first sub-constraint of the periodic transportation cost with respect to at least one transportation box number set and at least one bulk quantity set of at least one item, where the periodic transportation cost and at least one item are associated with the item type.

[0065] Taking the goods type of dry goods as an example, the first sub-constraint for dry goods can be expressed by formula (4):

[0066]

[0067] Among them, represents the periodic transportation cost of the goods set belonging to dry goods, represents the single-box transportation cost data of dry goods, represents the number of transportation boxes of the i-th good in the j-th period in the goods set belonging to dry goods, represents the bulk quantity of the i-th good in the j-th period in the goods set belonging to dry goods, represents the single-box capacity of the i-th good in the goods set belonging to dry goods, nd represents the number of goods in the goods set belonging to dry goods, and m represents the number of periods in the cycle.

[0068] Similarly, the first sub-constraint for wet goods can be expressed by formula (5):

[0069]

[0070] Among them, represents the periodic transportation cost of the goods set belonging to wet goods, represents the single-box transportation cost data of wet goods, represents the number of transportation boxes of the i-th good in the j-th period in the goods set belonging to wet goods, represents the bulk quantity of the i-th good in the j-th period in the goods set belonging to wet goods, represents the single-box capacity of the i-th good in the goods set belonging to wet goods, nw represents the number of goods in the goods set belonging to wet goods, and m represents the number of periods in the cycle.

[0071] Similarly, the first sub-constraint for frozen goods can be expressed by formula (6):

[0072]

[0073] Among them, represents the periodic transportation cost of the goods set belonging to frozen goods, represents the single-box transportation cost data of frozen goods, represents the number of transportation boxes of the i-th good in the j-th period in the goods set belonging to frozen goods, represents the bulk quantity of the i-th good in the j-th period in the goods set belonging to frozen goods, represents the single-box capacity of the i-th good in the goods set belonging to frozen goods, nf represents the number of goods in the goods set belonging to frozen goods, and m represents the number of periods in the cycle.

[0074] At block 404, computing device 110 generates a second sub-constraint of the total periodic transportation cost relative to multiple periodic transportation costs associated with multiple item types.

[0075] The second sub-constraint can be represented, for example, by Equation (7):

[0076]

[0077] Thereby, multiple constraints on the transportation cost can be generated corresponding to multiple item types, making the constraint on the total periodic transportation cost more refined, so as to meet the respective requirements of each item type.

[0078] In some embodiments, each unit-box transportation cost among multiple unit-box transportation costs is associated with the transportation frequency for the associated item type within a period.

[0079] Figure 5 FIG. shows a flowchart of a method 500 for generating a first sub-constraint according to an embodiment of the present disclosure. For example, method 500 can be executed by a computing device 110 as Figure 1 shown. It should be understood that method 500 may further include additional blocks not shown and / or the shown blocks may be omitted, and the scope of the present disclosure is not limited in this regard.

[0080] At block 502, computing device 110 generates a constraint of the number of transportation time periods for an item type within a period relative to the transportation frequency for the item type within the period.

[0081] Taking the item type of dry goods as an example, this constraint for dry goods can be represented, for example, by Equation (8):

[0082]

[0083] where m represents the number of time periods within a period, indicates whether the transportation frequency of dry goods is s, taking values of 0 or 1, 0 indicates that the transportation frequency of dry goods is not s, 1 indicates that the transportation frequency of dry goods is s, for s from 1 to m, only one takes the value of 1, and the rest take the value of 0. indicates whether dry goods are transported in the j-th time period, taking values of 0 or 1, 0 indicates not transported, 1 indicates transported, represents the number of transportation time periods for dry goods within a period. M is a very large value, used to make the number of transportation time periods for dry goods within a period equal to s, while in the case where the number of transportation time periods for dry goods within a period is not equal to s,

[0084] Similarly, this constraint for wet goods can be expressed, for example, by Equation (9):

[0085]

[0086] where m represents the number of time periods within a cycle, indicates whether the transportation frequency of wet goods is s, taking values of 0 or 1. 0 means the transportation frequency of wet goods is not s, and 1 means the transportation frequency of wet goods is s. For s ranging from 1 to m, only one takes the value of 1, and the rest take the value of 0. indicates whether wet goods are transported in the j-th time period, taking values of 0 or 1. 0 means not transported, and 1 means transported, represents the number of transportation time periods for wet goods within a cycle. M is a very large value used to make the number of transportation time periods for wet goods within a cycle equal to s, while in the case where the number of transportation time periods for wet goods within a cycle is not equal to s,

[0087] Similarly, this constraint for frozen goods can be expressed, for example, by Equation (10):

[0088]

[0089] where m represents the number of time periods within a cycle, indicates whether the transportation frequency of frozen goods is s, taking values of 0 or 1. 0 means the transportation frequency of frozen goods is not s, and 1 means the transportation frequency of frozen goods is s. For s ranging from 1 to m, only one takes the value of 1, and the rest take the value of 0. indicates whether frozen goods are transported in the j-th time period, taking values of 0 or 1. 0 means not transported, and 1 means transported, represents the number of transportation time periods for frozen goods within a cycle. M is a very large value used to make the number of transportation time periods for frozen goods within a cycle equal to s, while in the case where the number of transportation time periods for frozen goods within a cycle is not equal to s,

[0090] At block 504, computing device 110 generates a constraint of the set of transportation time periods for a goods type within a cycle relative to at least one set of transportation quantities of at least one goods associated with the goods type.

[0091] Taking dry goods as an example of the goods type, this constraint for dry goods can be expressed, for example, by Equation (11):

[0092]

[0093] where, Indicates whether the dry goods are transported in the j-th period, with a value of 0 or 1. 0 means not transported, and 1 means transported. Indicates the transportation quantity of the i-th item in the goods set belonging to dry goods in the j-th period. nd represents the quantity of goods in the goods set belonging to dry goods, and m represents the number of periods in a cycle. M is a very large value, used to make if the sum of at least one transportation quantity of at least one item in the goods set belonging to dry goods in the j-th period is not 0, then That is, the dry goods are transported in the j-th period, and if the sum of at least one transportation quantity of at least one item in the goods set belonging to dry goods in the j-th period is 0, then That is, the dry goods are not transported in the j-th period.

[0094] Similarly, this constraint for wet goods can be expressed, for example, by formula (12):

[0095]

[0096] Wherein, Indicates whether the wet goods are transported in the j-th period, with a value of 0 or 1. 0 means not transported, and 1 means transported. Indicates the transportation quantity of the i-th item in the goods set belonging to wet goods in the j-th period. nw represents the quantity of goods in the goods set belonging to wet goods, and m represents the number of periods in a cycle. M is a very large value, used to make if the sum of at least one transportation quantity of at least one item in the goods set belonging to wet goods in the j-th period is not 0, then That is, the wet goods are transported in the j-th period, and if the sum of at least one transportation quantity of at least one item in the goods set belonging to wet goods in the j-th period is 0, then That is, the wet goods are not transported in the j-th period.

[0097] Similarly, this constraint for frozen goods can be expressed, for example, by formula (13):

[0098]

[0099] Wherein, Indicates whether the frozen goods are transported in the j-th period, with a value of 0 or 1. 0 means not transported, and 1 means transported. Indicates the transportation quantity of the i-th item in the goods set belonging to frozen goods in the j-th period. nf represents the quantity of goods in the goods set belonging to frozen goods, and m represents the number of periods in a cycle. M is a very large value, such that if the sum of at least one transportation quantity of at least one item in the goods set belonging to frozen goods in the j-th period is not 0, then That is, frozen goods are transported in the j-th period, and if the sum of at least one transportation quantity of at least one of the goods included in the concentrated goods of frozen goods is 0 in the j-th period, then That is, frozen goods are not transported in the j-th period.

[0100] At block 506, computing device 110 generates constraints on the periodic transportation cost with respect to the transportation frequency for the goods type within a period, and at least one set of transportation container numbers and at least one set of bulk quantities of at least one good. The periodic transportation cost and at least one good are associated with the goods type.

[0101] Taking the goods type of dry goods as an example, this constraint for dry goods can be expressed by formula (14):

[0102]

[0103] Where, represents the periodic transportation cost of the set of goods belonging to dry goods, represents the single-container transportation cost data of dry goods at transportation frequency s, represents whether the transportation frequency of dry goods is s, taking values of 0 or 1, 0 indicates that the transportation frequency of dry goods is not s, 1 indicates that the transportation frequency of dry goods is s, for s from 1 to m, only one takes the value of 1, and the rest take the value of 0. represents the number of transportation containers of the i-th good in the set of goods belonging to dry goods in the j-th period, represents the bulk quantity of the i-th good in the set of goods belonging to dry goods in the j-th period, represents the single-container capacity of the i-th good in the set of goods belonging to dry goods, nd represents the number of goods in the set of goods belonging to dry goods, and m represents the number of periods within a cycle.

[0104] Similarly, this constraint for wet goods can be expressed by formula (15):

[0105]

[0106] Where, represents the periodic transportation cost of the set of goods belonging to wet goods, represents the single-container transportation cost data of wet goods at transportation frequency s, represents whether the transportation frequency of wet goods is s, taking values of 0 or 1, 0 indicates that the transportation frequency of wet goods is not s, 1 indicates that the transportation frequency of wet goods is s, for s from 1 to m, only one takes the value of 1, and the rest take the value of 0. represents the number of transportation containers of the i-th good in the set of goods belonging to wet goods in the j-th period, represents the bulk quantity of the \(i\)-th item in the wet goods concentration at the \(j\)-th time period, represents the quantity that can be accommodated in a single box of the \(i\)-th item in the wet goods concentration, \(nw\) represents the quantity of items in the wet goods concentration, and \(m\) represents the number of time periods within a cycle.

[0107] Similarly, this constraint for frozen goods can be expressed by formula (16):

[0108]

[0109] wherein, represents the cycle transportation cost of the frozen goods set, represents the single-box transportation cost data of frozen goods at transportation frequency \(s\), represents whether the transportation frequency of frozen goods is \(s\), taking values of 0 or 1. 0 means the transportation frequency of frozen goods is not \(s\), and 1 means the transportation frequency of frozen goods is \(s\). For \(s\) from 1 to \(m\), only one takes the value of 1, and the rest take the value of 0. represents the number of transportation boxes of the \(i\)-th item in the frozen goods concentration at the \(j\)-th time period, represents the bulk quantity of the \(i\)-th item in the frozen goods concentration at the \(j\)-th time period, represents the quantity that can be accommodated in a single box of the \(i\)-th item in the frozen goods concentration, \(nf\) represents the quantity of items in the frozen goods concentration, and \(m\) represents the number of time periods within a cycle.

[0110] Thus, for the case where the single-box transportation cost is associated with the transportation frequency of the associated goods type within a cycle, constraints can be generated between the transportation frequency of the goods type and the number of transportation time periods, constraints for the set of transportation time periods of the goods type relative to at least one set of transportation quantity of at least one good associated with the goods type, and constraints for the cycle transportation cost associated with the goods type relative to the transportation frequency of the goods type within a cycle, as well as at least one set of transportation box numbers and at least one set of bulk quantities of at least one good associated with the goods type, thereby making the constraints on transportation costs more refined.

[0111] In some embodiments, each single-box transportation cost among multiple single-box transportation costs includes a fixed transportation cost and an additional transportation cost. The fixed transportation cost is for transportation frequencies less than or equal to a predetermined transportation frequency. The additional transportation cost is for transportation frequencies greater than the predetermined transportation frequency and is associated with the transportation frequency. In addition, each single-box transportation cost may further include loading and unloading costs.

[0112] In the case where the single - case transportation cost for each item includes fixed transportation costs and additional transportation costs, the above formulas (14) - (16) can be updated to formulas (17) - (19).

[0113] Taking the goods type of dry goods as an example, this constraint for dry goods can be represented by formula (17):

[0114]

[0115] where, represents the periodic transportation cost of the set of goods belonging to dry goods, p represents the predetermined number of transportation times, represents the fixed transportation cost of dry goods when the transportation frequency is less than or equal to p, represents the additional transportation cost of dry goods when the transportation frequency s is greater than p. For the remaining parameters, refer to the above text and will not be elaborated here.

[0116] Similarly, this constraint for wet goods can be represented by formula (18):

[0117]

[0118] where, represents the periodic transportation cost of the set of goods belonging to wet goods, p represents the predetermined number of transportation times, represents the fixed transportation cost of wet goods when the transportation frequency is less than or equal to p, represents the additional transportation cost of wet goods when the transportation frequency s is greater than p. For the remaining parameters, refer to the above text and will not be elaborated here.

[0119] Similarly, this constraint for frozen goods can be represented by formula (19):

[0120]

[0121] where, represents the periodic transportation cost of the set of goods belonging to frozen goods, p represents the predetermined number of transportation times, represents the fixed transportation cost of frozen goods when the transportation frequency is less than or equal to p, represents the additional transportation cost of frozen goods when the transportation frequency s is greater than p. For the remaining parameters, refer to the above text and will not be elaborated here.

[0122] Thus, it is possible to generate more targeted constraints on transportation costs for the case where there are fixed transportation costs within the predetermined transportation frequency and additional transportation costs outside the predetermined transportation frequency.

[0123] In some embodiments, the computing device 110 can also obtain transportation frequency rule data, and the transportation frequency rule data indicates the minimum transportation frequency. In one example, the minimum transportation frequency can be the same as the above - mentioned predetermined transportation frequency p.

[0124] Subsequently, computing device 110 may also generate a constraint regarding that the transportation frequency for a goods type within a period is greater than or equal to a minimum transportation frequency. For example, the range of s from 1 - m above is constrained to the range from the indicated minimum transportation frequency to m, such as the range from p to m.

[0125] As a supplement or alternative, in some embodiments, the transportation frequency rule data may also indicate a maximum transportation frequency. Computing device 110 may also generate a constraint regarding that the transportation frequency for a goods type within a period is less than or equal to the maximum transportation frequency. For example, the range of s from 1 - m above is constrained to the range from 1 to the indicated maximum transportation frequency, or the range of s from 1 - m above is constrained to the range from the indicated minimum transportation frequency to the indicated maximum transportation frequency.

[0126] Thus, a constraint regarding that the transportation frequency for a goods type within a period is greater than or equal to the minimum transportation frequency and / or a constraint regarding that the transportation frequency for a goods type within a period is less than or equal to the maximum transportation frequency can be generated according to the transportation frequency rule data, thereby realizing the on - demand constraint of the transportation frequency.

[0127] Figure 6 A flowchart of a method 600 for generating a third constraint according to an embodiment of the present disclosure is shown. For example, method 600 may be executed by a computing device 110 as Figure 1 shown. It should be understood that method 600 may also include additional blocks not shown and / or the shown blocks may be omitted, and the scope of the present disclosure is not limited in this regard.

[0128] At block 602, for each goods type among a plurality of goods types, computing device 110 generates a third sub - constraint of the storage volume associated with the goods type relative to at least one set of transportation quantities of at least one goods based on at least one single - box volume, at least one quantity that can be accommodated in a single box, and at least one set of consumption quantities of at least one goods associated with the goods type. It should be understood that each consumption quantity set in the at least one set of consumption quantities includes a plurality of consumption quantities of the corresponding goods in a plurality of time periods. Each transportation quantity set in the at least one set of transportation quantities includes a plurality of transportation quantities of the corresponding goods in a plurality of time periods.

[0129] Taking the goods type of dry goods as an example, the third sub - constraint for dry goods can be represented by formula (20):

[0130]

[0131] where V d represents the storage volume of the set of goods belonging to dry goods, Denote the transportation quantity of the \(i\)-th dry goods item in the \(j\)-th time period in the dry goods consignment. Denote the consumption quantity of the \(i\)-th dry goods item in the \(j\)-th time period in the dry goods consignment. Denote the quantity that can be accommodated in a single box of the \(i\)-th dry goods item in the dry goods consignment. Denote the volume of a single box of the \(i\)-th dry goods item in the dry goods consignment. Let \(nd\) denote the number of items in the dry goods consignment, and \(m\) denote the number of time periods in the cycle.

[0132] Similarly, the third sub-constraint for wet goods can be expressed by formula (21):

[0133]

[0134] where \(V\) w Denote the storage volume of the wet goods consignment. Denote the transportation quantity of the \(i\)-th wet goods item in the \(j\)-th time period in the wet goods consignment. Denote the consumption quantity of the \(i\)-th wet goods item in the \(j\)-th time period in the wet goods consignment. Denote the quantity that can be accommodated in a single box of the \(i\)-th wet goods item in the wet goods consignment. Denote the volume of a single box of the \(i\)-th wet goods item in the wet goods consignment. Let \(nw\) denote the number of items in the wet goods consignment, and \(m\) denote the number of time periods in the cycle.

[0135] Similarly, the third sub-constraint for frozen goods can be expressed by formula (22):

[0136]

[0137] where \(V\) f Denote the storage volume of the frozen goods consignment. Denote the transportation quantity of the \(i\)-th frozen goods item in the \(j\)-th time period in the frozen goods consignment. Denote the consumption quantity of the \(i\)-th frozen goods item in the \(j\)-th time period in the frozen goods consignment. Denote the quantity that can be accommodated in a single box of the \(i\)-th frozen goods item in the frozen goods consignment. Denote the volume of a single box of the \(i\)-th frozen goods item in the frozen goods consignment. Let \(nf\) denote the number of items in the frozen goods consignment, and \(m\) denote the number of time periods in the cycle.

[0138] At block 604, computing device 110 generates a fourth sub-constraint for the total storage volume relative to the multiple storage volumes associated with multiple item types.

[0139] The fourth sub-constraint can be expressed by formula (23) for example:

[0140] Vtot≥V d +V w +V f (23)

[0141] Thus, multiple constraints on the storage volume can be generated corresponding to multiple goods types, making the constraints on the overall storage volume more refined, so as to meet the respective storage volume requirements of each goods type.

[0142] Figure 7 The flowchart of method 700 for generating the total constraint according to an embodiment of the present disclosure is shown. For example, method 700 can be executed by a computing device 110 as Figure 1 shown. It should be understood that method 700 may further include additional blocks not shown and / or the shown blocks may be omitted, and the scope of the present disclosure is not limited in this regard.

[0143] At block 702, the computing device 110 generates a fourth constraint of the number of single-box accommodable, the set of transportation quantities, and the set of transportation box numbers associated with the goods relative to the set of bulk quantities associated with the goods.

[0144] The fourth constraint can be represented by formulas (24)-(26):

[0145] z ij +1≥y ij / N i , i = 1, 2...n, j = 1, 2...n (24)

[0146] z ij +1≤y ij / N i +1, i = 1, 2...n, j = 1, 2...n (25)

[0147] n ij =y ij -z ij *N i , i = 1, 2...n, j = 1, 2...m (26)

[0148] Wherein, z ij represents the number of transportation boxes of the i-th good in the j-th period, n ij represents the bulk quantity of the i-th good in the j-th period, y ij represents the transportation quantity of the i-th good in the j-th period, N i represents the number of single-box accommodable of the i-th good, z ij *N i represents the number of full-box transportation of the i-th good in the j-th period.

[0149] At block 704, computing device 110 generates a fifth constraint of the set of bulk status indicators of the goods over multiple time periods relative to the set of bulk quantities associated with the goods. Each bulk status indicator in the set of bulk status indicators indicates whether the goods are in bulk during the associated time period.

[0150] The fifth constraint can be represented by Equation (27):

[0151] x ij *M ≥ n ij , i = 1, 2...n, j = 1, 2...m (27)

[0152] Wherein, the bulk status indicator x ij indicates whether the i-th good is in bulk (also known as unpacking) during the j-th time period, and its value is 0 or 1. 0 indicates not in bulk, and 1 indicates in bulk. n ij represents the bulk quantity of the i-th good during the j-th time period, M is a very large value such that when the bulk quantity of the i-th good during the j-th time period is not 0, x ij = 1, and when the bulk quantity of the i-th good during the j-th time period is 0, x ij = 0.

[0153] In some embodiments, there are multiple goods, and computing device 110 can obtain bulk rule data, which indicates that at least one of the multiple goods cannot be in bulk.

[0154] Subsequently, computing device 110 can set at least one bulk status indicator set of at least one of the goods indicated by the bulk rule data over multiple time periods to a value indicating not in bulk.

[0155] For example, if the bulk rule data indicates that the 3rd good cannot be in bulk, then for j = 1 to m, x 3j can be set to 0, indicating that the 3rd good is not in bulk over multiple time periods.

[0156] Next, for each of the multiple goods, computing device 110 can generate a fifth constraint of the set of bulk status indicators of the goods over multiple time periods relative to the set of bulk quantities associated with the goods if it is determined that the set of bulk status indicators of the goods over multiple time periods has not been set to a value indicating not in bulk. Otherwise, computing device 110 may not generate the fifth constraint for the good. For example, if x ij is not equal to 0, then the fifth constraint can be generated for the i-th good. If x ij is equal to 0, then the fifth constraint is not generated for the i-th good.

[0157] Thus, it is possible to set whether the goods are in bulk as needed, so that the constraints on bulk can be configured and better match the business scenarios.

[0158] In some embodiments, the computing device 110 may generate a third objective that minimizes the sum of multiple sets of bulk quantities associated with multiple goods.

[0159] The third objective is, for example, or where cost unfold represents the bulk unpacking cost

[0160] Subsequently, the computing device 110 may, based on at least one of the second constraint and the third constraint, multiple sets of first constraints, multiple fourth constraints, and multiple fifth constraints, and based on at least one of the first objective of minimizing the total cost of periodic transportation and the second objective of minimizing the overall storage volume and the third objective, generate multiple sets of transportation quantities, multiple sets of transportation box numbers, and multiple sets of bulk quantities for multiple goods over multiple time periods via mixed-integer linear programming. Thus, the bulk unpacking cost can be minimized by minimizing the sum of multiple sets of bulk quantities.

[0161] Thus, it is possible to generate, for the bulk situation, the number of quantities that can be accommodated in a single box associated with the goods, the set of transportation quantities, the constraints of the set of transportation box numbers relative to the set of bulk quantities associated with the goods, and the set of bulk status indicators of the goods over multiple time periods relative to the set of bulk quantities associated with the goods. Based on these constraints, it is more convenient to calculate multiple sets of transportation quantities, multiple sets of transportation box numbers, and multiple sets of bulk quantities. In addition, the sum of multiple sets of bulk quantities can be minimized, thereby minimizing the bulk unpacking cost.

[0162] In some embodiments, the computing device 110 may also, for each goods type among multiple goods types, generate a storage volume associated with the goods type based on at least one set of transportation quantities, at least one number of quantities that can be accommodated in a single box, and at least one single-box volume of at least one good associated with the goods type.

[0163] Subsequently, the computing device 110 may obtain the shelf height and multiple warehouse shelf area coefficients associated with multiple goods types.

[0164] The shelf height includes, for example, but is not limited to, 1.9 meters, 2 meters, 3 meters, etc. The warehouse shelf area coefficient for dry goods is, for example, 1, and the warehouse shelf area coefficient for wet goods and frozen goods is, for example, 2. It should be understood that these are only examples, and the scope of the present disclosure is not limited herein.

[0165] Next, the computing device 110 can generate multiple storage areas associated with multiple product types based on the shelf height, multiple warehouse shelf area coefficients associated with multiple product types, and multiple storage volumes.

[0166] For example, multiple storage areas can be calculated based on the following formulas (28)-(30):

[0167]

[0168]

[0169]

[0170] Where S d , S w and S f represent the storage areas of dry goods, wet goods, and frozen goods respectively, V d , V w and V f represent the storage volumes of dry goods, wet goods, and frozen goods respectively, C d , C w and C f represent the warehouse shelf area coefficients of dry goods, wet goods, and frozen goods respectively, h represents the shelf height, and e represents the coefficient of the model volume to the actual volume, which is used to convert the storage volume calculated by the MILP model into the actual storage volume, such as 0.7225.

[0171] Thus, the transportation quantity of the goods obtained by decision-making can be combined with the quantity that can be accommodated in a single box and the volume of a single box to generate the storage volume associated with the product type, and then combined with the shelf height and the warehouse shelf area coefficient to convert the storage volume into the storage area. In addition, the storage areas of each product form generated can be used for the design and planning of the warehouse area for each product form at the new target inbound point, and for judging whether the existing warehouse area of the existing target inbound point can support it.

[0172] In some embodiments, the computing device 110 can also obtain multiple current warehouse areas of the target inbound point associated with multiple product types.

[0173] Subsequently, the computing device 110 can determine the multiple storage statuses of the target inbound point for multiple product types based on the multiple generated storage areas and the multiple current warehouse areas.

[0174] For example, if it is determined that the generated storage area for a certain product type is less than the storage area of that product type, it is determined that the storage space of the target inbound point for that product type is insufficient, otherwise it is determined that the storage space of the target inbound point for that product type is sufficient.

[0175] Thus, by comparing the storage areas of each goods type generated by the decision-making with the current warehouse area of the target receiving point, the storage status of the target receiving point for each goods type can be given.

[0176] In some embodiments, the computing device 110 can obtain multiple current warehouse areas of the target receiving point associated with multiple goods types.

[0177] Subsequently, for each goods type among the multiple goods types, the computing device 110 generates a sixth constraint, where the sixth constraint is that the storage area associated with the goods type is less than or equal to the current warehouse area associated with the goods type.

[0178] Next, the computing device 110 can generate multiple sets of transportation quantities, multiple sets of transportation box numbers, and multiple sets of bulk quantities based on at least one of the second constraint and the third constraint, multiple first constraint sets, and multiple sixth constraints, with the goal of minimizing at least one of the total periodic transportation cost and the total storage volume.

[0179] Thus, the multiple current warehouse areas of the target receiving point associated with multiple goods types can also be considered in the receiving planning decision-making process, making the receiving planning more compatible with the actual warehouse situation of the receiving point.

[0180] Figure 8 The schematic block diagram of an example device 800 that can be used to implement the embodiments of the present disclosure is shown. For example, the computing device 110 as shown can be implemented by the device 800. As shown in the figure, the device 800 includes a central processing unit (CPU) 801, which can execute various appropriate actions and processes according to the computer program instructions stored in the read-only memory (ROM) 802 or the computer program instructions loaded from the storage unit 808 into the random access memory (RAM) 803. In the random access memory 803, various programs and data required for the operation of the device 800 can also be stored. The central processing unit 801, the read-only memory 802, and the random access memory 803 are connected to each other through a bus 804. The input / output (I / O) interface 805 is also connected to the bus 804. Figure 1 Multiple components in the device 800 are connected to the input / output interface 805, including: an input unit 806, such as a keyboard, a mouse, a microphone, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, an optical disc, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0181]

[0182] ​The various processes and treatments described above, such as methods 200-700, may be executed by a central processing unit 801. For example, in some embodiments, methods 200-700 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed onto device 800 via read-only memory 802 and / or communication unit 809. When the computer program is loaded into random access memory 803 and executed by central processing unit 801, one or more actions of methods 200-700 described above may be performed.

[0183] The present disclosure relates to methods, apparatuses, systems, electronic devices, computer-readable storage media, and / or computer program products. The computer program product may include computer-readable program instructions for performing various aspects of the present disclosure.

[0184] A computer-readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punch card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium as used herein is not construed as being a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0185] The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0186] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine - related instructions, microcode, firmware instructions, state - setting data, or source code or object code written in any combination of one or more programming languages, including object - oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer - readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand - alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer - readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field - programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer - readable program instructions to implement various aspects of the present disclosure.

[0187] Aspects of the present disclosure are described herein with reference to the flowchart and / or block diagram of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowchart and / or block diagram, and the combinations of blocks in the flowchart and / or block diagram, can be implemented by computer - readable program instructions.

[0188] These computer - readable program instructions can be provided to a processing unit of a general - purpose computer, a special - purpose computer, or other programmable data - processing apparatus to produce a machine such that, when the instructions are executed by the processing unit of the computer or other programmable data - processing apparatus, a device is produced that implements the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer - readable program instructions can also be stored in a computer - readable storage medium, which causes a computer, a programmable data - processing apparatus, and / or other devices to operate in a particular manner. Thus, the computer - readable medium storing the instructions includes a manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0189] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other devices to produce a computer-implemented process such that the instructions executed on the computer, other programmable data processing apparatus, or other devices implement the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0190] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two consecutive blocks may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block of the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or acts, or by a combination of dedicated hardware and computer instructions.

[0191] The embodiments of the present disclosure have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, the practical application, or the technical improvement of the technology in the market, or to enable other ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for purchase planning, comprising: Obtaining single - box transportation - related data associated with goods and a set of demand quantities, where the goods are associated with a target purchase point, the set of demand quantities includes multiple demand quantities for the goods in multiple time periods within a cycle, the single - box transportation - related data includes the quantity that can be accommodated in a single box and single - box transportation cost data, there are multiple goods, and the multiple goods are divided into multiple sets of goods associated with multiple goods types, each set of goods in the multiple sets of goods includes at least one good, and the single - box transportation cost data includes multiple single - box transportation costs associated with the multiple goods types; Generating a total constraint of an objective function with respect to a set of transportation quantities, a set of transportation box numbers, and a set of bulk quantities of the goods in multiple time periods based on the single - box transportation - related data and the set of demand quantities; And Generating the set of transportation quantities, the set of transportation box numbers, and the set of bulk quantities by minimizing the objective function based on the total constraint, where generating the total constraint includes: generating a second constraint of the total cycle transportation cost with respect to the set of transportation box numbers and the set of bulk quantities of the goods in the multiple time periods based on the single - box transportation cost data and the quantity that can be accommodated in a single box; where generating the second constraint includes: for each goods type in the multiple goods types, generating a first sub - constraint of the cycle transportation cost with respect to at least one set of transportation box numbers and at least one set of bulk quantities of at least one good based on the single - box transportation cost associated with the goods type; where generating the first sub - constraint includes: Generating a constraint of the number of transportation time periods for the goods type within the cycle with respect to the transportation frequency of the goods type within the cycle; Generating a constraint of the set of transportation time periods for the goods type within the cycle with respect to at least one set of transportation quantities of at least one good associated with the goods type; and Generating a constraint of the cycle transportation cost with respect to the transportation frequency of the goods type within the cycle, and at least one set of transportation box numbers and at least one set of bulk quantities of at least one good based on the single - box transportation cost associated with the goods type.

2. The method according to claim 1, wherein the single - box transportation - related data further includes at least one of the following: the volume of a single box and the set of consumption quantities of the goods in the multiple time periods; and The objective function includes at least one of the total cycle transportation cost and the total storage volume.

3. The method according to claim 2, wherein generating the total constraint includes: Generating a first set of constraints associated with the multiple time periods, each first constraint in the first set of constraints is such that the sum of at least one transportation quantity of the goods used in at least one time period included in the period from the start of the cycle to the associated time period is greater than or equal to the sum of at least one demand quantity of the goods in the at least one time period; And Generating a third constraint of the total storage volume with respect to the set of transportation quantities of the goods in the multiple time periods based on the quantity that can be accommodated in a single box, the volume of a single box, and the set of consumption quantities.

4. The method according to claim 1, wherein the periodic transportation cost and the at least one item are associated with the item type; and generating the second constraint includes: generating a second sub-constraint of the total periodic transportation cost relative to a plurality of periodic transportation costs associated with the plurality of item types.

5. The method according to claim 4, wherein each of the plurality of per-case transportation costs is associated with the transportation frequency used for the associated item type within the period.

6. The method according to claim 5, wherein each of the plurality of per-case transportation costs includes a fixed transportation cost and an additional transportation cost, the fixed transportation cost being for transportation frequencies less than or equal to a predetermined transportation frequency, and the additional transportation cost being for transportation frequencies greater than the predetermined transportation frequency and being associated with the transportation frequency.

7. The method according to claim 5 or 6, wherein generating the first sub-constraint further includes: obtaining transportation frequency rule data, the transportation frequency rule data indicating at least one of a minimum transportation frequency and a maximum transportation frequency; and generating a constraint on at least one of: the transportation frequency used for the item type within the period is greater than or equal to the minimum transportation frequency, and the transportation frequency used for the item type within the period is less than or equal to the maximum transportation frequency.

8. The method according to claim 3, wherein generating the third constraint includes: for each item type among the plurality of item types, generating a third sub-constraint of the storage volume associated with the item type relative to at least one transportation quantity set of the at least one item based on at least one per-case volume, at least one per-case accommodation quantity, and at least one consumption quantity set of the at least one item associated with the item type; and generating a fourth sub-constraint of the total storage volume relative to a plurality of storage volumes associated with the plurality of item types.

9. The method according to any one of claims 1-6 and 8, wherein generating the total constraint includes: generating a fourth constraint of the per-case accommodation quantity, transportation quantity set, and transportation case number set associated with the item relative to the bulk quantity set associated with the item; and generating a fifth constraint of the bulk status indicator set of the item in the plurality of time periods relative to the bulk quantity set associated with the item.

10. The method according to claim 9, wherein for a plurality of items, generating the fifth constraint includes: obtaining bulk rule data, the bulk rule data indicating that at least one of the plurality of items cannot be in bulk; setting at least one bulk status indicator set of at least one item indicated by the bulk rule data in the plurality of time periods to a value indicating non-bulk; and for each of the plurality of items, if it is determined that the bulk status indicator set of the item in the plurality of time periods is not set to a value indicating non-bulk, generating a fifth constraint of the bulk status indicator set of the item in the plurality of time periods relative to the bulk quantity set associated with the item.

11. The method according to any one of claims 1-6 and 8 further comprises: For each of the plurality of goods types, generating a storage volume associated with the goods type based on at least one set of transportation quantities, at least one quantity that can be accommodated in a single box, and at least one single-box volume of at least one good associated with the goods type; And Obtaining a shelf height and a plurality of warehouse shelf area coefficients associated with the plurality of goods types; Based on the shelf height, the plurality of warehouse shelf area coefficients associated with the plurality of goods types, and the plurality of storage volumes, generating a plurality of storage areas associated with the plurality of goods types.

12. The method according to claim 11 further comprises: Obtaining a plurality of current warehouse areas associated with the plurality of goods types at the target inbound point; And Based on the generated plurality of storage areas and the plurality of current warehouse areas, determining a plurality of storage statuses of the target inbound point for the plurality of goods types.

13. The method according to claim 11, wherein generating the total constraint further comprises: Obtaining a plurality of current warehouse areas associated with the plurality of goods types at the target inbound point; And For each of the plurality of goods types, generating a sixth constraint that the storage area associated with the goods type is less than or equal to the current warehouse area associated with the goods type.

14. The method according to claim 1, wherein obtaining the set of demand quantities comprises: Obtaining a set of sales quantities associated with a sales identifier from sales raw data associated with the target inbound point, the set of sales quantities including a plurality of demand quantities for the sales identifier during a plurality of time periods within a cycle; and Based on the association between the sales identifier and the goods identifier of the goods, converting the set of sales quantities into the set of demand quantities associated with the goods.

15. An electronic device comprising: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the method according to any one of claims 1-14.

16. A computer-readable storage medium having stored thereon a computer program, which when executed by a processor implements the method according to any one of claims 1-14.

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