CN114254862A

CN114254862A - Data processing method, device, equipment, storage medium and program product

Info

Publication number: CN114254862A
Application number: CN202111363087.5A
Authority: CN
Inventors: 于全刚; 王连魁; 周文玲; 夏宗基; 朱广慈; 张玉丽; 高勇; 陈晶; 夏玉萍
Original assignee: Qingdao Haier Technology Co Ltd; Haier Smart Home Co Ltd
Current assignee: Qingdao Haier Technology Co Ltd; Haier Smart Home Co Ltd
Priority date: 2021-11-17
Filing date: 2021-11-17
Publication date: 2022-03-29

Abstract

The embodiment of the application provides a data processing method, a data processing device, data processing equipment, a storage medium and a program product. The method comprises the following steps: acquiring the accumulated production quantity of the goods produced by the factory, the inventory quantity of the goods stored in a warehouse corresponding to the factory, the single-day maximum delivery quantity of the warehouse and the latest delivery date of the goods; determining delivery plan information corresponding to the factory according to the accumulated production, the inventory, the single-day maximum delivery and the latest delivery date; acquiring the carrying capacity of a delivery vehicle; and determining the vehicle allocation plan information according to the delivery plan information and the bearing capacity. The data processing method, the data processing device, the data processing equipment, the storage medium and the program product are used for improving the customization efficiency of the plan.

Description

Data processing method, device, equipment, storage medium and program product

Technical Field

The present application relates to the field of data processing technologies, and in particular, to a data processing method, apparatus, device, storage medium, and program product.

Background

Currently, a factory park typically includes multiple factories. The goods produced by each factory eventually need to be sent to the corresponding industry (e.g., supply warehouse per province).

In the related art, a park scheduling staff member is generally required to derive production data (e.g., order data, inventory data, scheduling plan, vehicle resources, etc.) of a plurality of factories from a work system; and according to the generated data, making a delivery plan, and then making a vehicle allocation plan by the park logistics allocation personnel according to the delivery plan of each factory. And then the goods produced by each factory are sent to corresponding industry and trade according to the delivery plan and the allocation plan.

In the process, the park scheduling plan staff make a delivery plan and the park logistics vehicle allocation staff make a vehicle allocation plan, so that the planning customization efficiency is low.

Disclosure of Invention

Embodiments of the present application provide a data processing method, apparatus, device, storage medium, and program product, so as to solve the problem of low efficiency in customizing a plan.

In a first aspect, an embodiment of the present application provides a data processing method, including:

acquiring the accumulated production quantity of the goods produced by the factory, the inventory quantity of the goods stored in a warehouse corresponding to the factory, the single-day maximum delivery quantity of the warehouse and the latest delivery date of the goods;

determining delivery plan information corresponding to the factory according to the accumulated production, the inventory, the single-day maximum delivery and the latest delivery date;

acquiring the carrying capacity of a delivery vehicle;

and determining the vehicle allocation plan information according to the delivery plan information and the bearing capacity.

In one possible design, the delivery plan information includes: the method comprises the steps that a target earliest delivery date and a target latest delivery date of goods, a warehouse identifier corresponding to the goods and a delay identifier indicating whether to delay delivery are carried out;

determining delivery plan information corresponding to the factory according to the accumulated production capacity, the inventory and the single-day maximum delivery quantity, wherein the delivery plan information comprises the following steps:

constructing a first constraint condition according to the accumulated production, the inventory and the single-day maximum delivery;

processing a first optimization target model stored in advance according to a first constraint condition to obtain a warehouse identifier, a delay identifier and a planned delivery date; the first optimization target model comprises variables corresponding to planned delivery date, warehouse identification and delay identification respectively;

if the planned shipment date is greater than or equal to the latest shipment date, determining the planned shipment date as the target latest shipment date, and determining the latest shipment date as the target earliest shipment date;

if the planned ship date is less than the latest ship date, the latest ship date is determined as the target latest ship date, and the planned ship date is determined as the target earliest ship date.

In one possible design, a first constraint is constructed based on the cumulative production volume, inventory and single day maximum shipment, including:

constructing a first sub-constraint condition according to the maximum daily shipment and a strategy that the daily shipment of the warehouse is not more than the maximum daily shipment corresponding to the warehouse;

according to the first preset value and a strategy that the goods have a delivery state, constructing a second sub-constraint condition; the delivery state is a delivered state or a non-delivered state;

constructing a third sub-constraint condition according to the accumulated production capacity and the inventory quantity and a strategy that the accumulated delivery volume of the goods in the preset time period is not more than the accumulated delivery volume of the warehouse in the preset time period;

and determining the first sub-constraint condition, the second sub-constraint condition and the third sub-constraint condition as the first constraint condition.

In one possible design, processing a first optimization objective model stored in advance according to a first constraint condition to obtain a warehouse identifier, a postponed identifier, and a planned delivery date includes:

and controlling a first solver, and processing the first optimization target model according to the first constraint condition to obtain a warehouse identifier, a postponing identifier and a planned delivery date.

In one possible design, the vehicle allocation plan information includes a target delivery date of the goods and a vehicle identifier corresponding to the goods;

according to the delivery plan information and the bearing capacity, determining the vehicle allocation plan information, which comprises the following steps:

constructing a second constraint condition according to the bearing capacity;

processing a second optimization target model stored in advance according to the target earliest delivery date and the target latest delivery date in the delivery plan information and a second constraint condition to obtain the target delivery date of the goods and the vehicle identification corresponding to the goods; the second optimization objective model comprises variables corresponding to the target delivery date and the vehicle identification respectively.

In one possible design, the second constraint is constructed based on the load bearing capacity, and includes:

constructing a fourth sub-constraint condition according to the bearing capacity and a strategy that the bearing capacity of the delivery vehicle is not more than the maximum bearing capacity of the delivery vehicle;

according to a second preset value, constructing a fifth sub-constraint condition according to a strategy that each cargo is delivered or delayed by a single delivery vehicle;

and determining the fourth sub-constraint condition and the fifth sub-constraint condition as the second constraint condition.

In a second aspect, the present application provides a data processing apparatus comprising: the device comprises a first acquisition module, a first determination module, a second acquisition module and a second determination module; wherein the content of the first and second substances,

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring the accumulated production of the goods produced by the factory, the inventory of the stored goods in a warehouse corresponding to the factory, the single-day maximum delivery quantity of the warehouse and the latest delivery date of the goods;

the first determining module is used for determining delivery plan information corresponding to the factory according to the accumulated production capacity, the inventory, the single-day maximum delivery quantity and the latest delivery date;

the second acquisition module is used for acquiring the carrying capacity of the delivery vehicle;

and the second determining module is used for determining the vehicle allocation plan information according to the delivery plan information and the bearing capacity.

the first determining module is specifically configured to:

In one possible design, the first determining module is specifically configured to:

In one possible design, the vehicle allocation plan information includes a target delivery date of the goods and a vehicle identifier corresponding to the goods; the second determining module is specifically configured to:

constructing a second constraint condition according to the bearing capacity;

In one possible design, the second determining module is specifically configured to:

In a third aspect, an embodiment of the present application provides an electronic device, including: a processor, and a memory communicatively coupled to the processor;

the memory stores computer-executable instructions;

the processor executes computer-executable instructions stored by the memory to implement the method as in any one of the first aspect.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the computer-executable instructions are used to implement the method according to any one of the first aspect.

In a fifth aspect, the present application provides a computer program product comprising a computer program that, when executed by a processor, implements the method of any one of the first aspects.

An embodiment of the application provides a data processing method, a device, equipment, a storage medium and a program product, wherein the method comprises the following steps: acquiring the accumulated production quantity of the goods produced by the factory, the inventory quantity of the goods stored in a warehouse corresponding to the factory, the single-day maximum delivery quantity of the warehouse and the latest delivery date of the goods; determining delivery plan information corresponding to the factory according to the accumulated production, the inventory, the single-day maximum delivery and the latest delivery date; acquiring the carrying capacity of a delivery vehicle; and determining the vehicle allocation plan information according to the delivery plan information and the bearing capacity. In the method, delivery plan information corresponding to a factory is automatically determined according to the accumulated production capacity, the stock quantity and the single-day maximum delivery quantity; and according to the delivery plan information and the bearing capacity, the vehicle allocation plan information is determined, so that manual delivery plan and vehicle allocation plan making is avoided, and plan making efficiency is improved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.

Fig. 1 is a schematic view of an application scenario of a data processing method according to an embodiment of the present application;

FIG. 2 is a schematic diagram of planning in a related art provided by an embodiment of the present application;

fig. 3 is a first flowchart of a data processing method according to an embodiment of the present application;

fig. 4 is a second flowchart of a data processing method according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application;

fig. 6 is a hardware schematic diagram of an electronic device according to an embodiment of the present application.

With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

First, terms referred to in the present application will be explained.

Industry and trade refer to supply warehouses arranged in each province, city, county, etc. Industry and trade are used for storing goods from the corresponding warehouse of factory. The corresponding warehouse of the plant is usually located in the plant park (see also fig. 1).

The shipment refers to a process in which each factory in a factory park needs to distribute goods to a designated trade and industry within a specified time according to order requirements.

The collaborative delivery plan refers to a delivery plan which can improve the planning precision and efficiency and reduce the logistics cost by taking the delivery plan and the allocation plan of the whole factory park into consideration cooperatively on the premise of the delivery date of the goods so as to achieve the goals of centralizing the delivery point and the delivery date, fully loading delivery vehicles and the like on the whole.

In the related art, a park scheduling staff member prepares a delivery plan and a park logistics vehicle allocation staff member prepares a vehicle allocation plan, so that the planning customization efficiency is low.

In the present application, in order to improve the plan customization efficiency, the inventor thinks that delivery plan information corresponding to a factory is automatically generated according to the accumulated production amount of the factory-produced goods, the stock amount of the stored goods in a warehouse corresponding to the factory, and the daily maximum delivery amount of the warehouse, and the mobile distribution vehicle plan information is automatically generated according to the delivery plan information and the carrying amount of the delivery vehicle, so that the delivery plan and the distribution vehicle plan are not manually prepared, and the plan customization efficiency is improved.

The following describes an application scenario related to the present application with reference to fig. 1.

Fig. 1 is a schematic view of an application scenario of a data processing method according to an embodiment of the present application. As shown in fig. 1, the plant campus comprises: a plurality of plants, a plurality of plant warehouses, and a plurality of outside warehouses.

For example, the plurality of factories includes W1, W2, W3, and W4.

For example, the plurality of factory warehouses includes F1, F2, F3, and F4.

For example, the plurality of external warehouses include E1, E2, E3, E4, and E5.

Wherein each factory has a corresponding factory warehouse. For example, W1 corresponds to F1, W2 corresponds to F2, W3 corresponds to F3, and W4 corresponds to F4.

Wherein each factory has at least one corresponding external warehouse. For example, W1 corresponds to E1, E2 and E3, W2 corresponds to E2 and E4, W3 corresponds to E1 and E3, and W4 corresponds to E3, E4 and E5.

It should be noted that, for a factory, the goods in the warehouse of the corresponding factory can be transferred to at least one corresponding external warehouse.

For complex warehousing networks within a plant campus as shown in fig. 1, a flow planning as shown in fig. 2 below is generally employed in the related art.

Fig. 2 is a schematic diagram of planning in the related art provided in an embodiment of the present application. As shown in fig. 2, includes: a data collection stage, a delivery planning stage and a vehicle allocation planning stage.

In the collect data phase, the park scheduling personnel derive production data for a plurality of plants from the work system. For example, a schedule is derived from a production system, order data is derived from an order system, inventory data is derived from an inventory system, and vehicle resources are derived from a vehicle system.

In the delivery planning stage, a park delivery planning worker needs to sort and count a large number of orders (according to dimensions such as customer types, destinations, products, delivery volumes and delivery dates), match the orders with inventory data and a delivery plan (matching delivery orders with inventory and delivery), and meet the aims of concentrating delivery time of goods to a single worker and reducing delivery points (including factory warehouses and external warehouses) as much as possible, and finally only can make a feasible delivery plan which meets a single factory.

In the vehicle allocation planning stage, logistics vehicle allocation personnel in the park gather delivery plans of various factories, and a vehicle allocation plan is made according to available vehicle resources so as to meet the aims of delivery in advance as much as possible and full vehicles as much as possible of each order within the delivery time (from the planned delivery starting date to the maximum date when the delivery meets the delivery), and finally complete delivery collaborative work of the whole park.

In the delivery planning stage, a park scheduling staff member prepares a delivery plan and a park logistics vehicle allocation staff member prepares a vehicle allocation plan, so that the planning customization efficiency is low.

In order to improve the customization efficiency of the plan, the inventor thinks that the delivery plan information corresponding to the factory is automatically determined according to part of information in the production data, and the vehicle allocation plan information is automatically determined according to the bearing capacity of the delivery vehicle and the delivery plan information, so that the delivery plan information and the vehicle allocation plan information are prevented from being manually made, and the customization efficiency of the plan is improved.

The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.

Fig. 3 is a first flowchart of a data processing method according to an embodiment of the present application. As shown in fig. 3, the method includes:

s301, acquiring the accumulated production of the goods produced by the factory, the stock of the goods stored in the warehouse corresponding to the factory, the single-day maximum delivery quantity of the warehouse and the latest delivery date of the goods.

Optionally, the execution subject of the embodiment of the present application is an electronic device, and may also be a data processing device disposed in the electronic device, where the data processing device may be implemented by a combination of software and/or hardware.

Goods refers to a product that is produced by a factory in accordance with an order.

The cumulative production volume of the good may be obtained from the plant's scheduling plan. The cumulative production amount is the cumulative production amount of the goods when d' is cumulated.

Optionally, the scheduling plan includes an identification of a plurality of goods, and a cumulative production capacity or daily production capacity corresponding to each identification).

When the scheduling plan comprises a plurality of goods identifications and the accumulated production amount corresponding to each identification, the accumulated production amount corresponding to the goods identification is obtained from the scheduling plan, and the accumulated production amount corresponding to the goods identification is determined as the accumulated production amount of the goods.

When the scheduling plan comprises a plurality of goods identifications and daily production corresponding to each identification, the daily production corresponding to the goods identifications is obtained from the scheduling plan, and the daily production corresponding to the identifications is accumulated to obtain the accumulated production of the goods.

The inventory quantities may be obtained from the plant's inventory data.

Optionally, the inventory data includes warehouse identifications of a plurality of warehouses, goods identifications of a plurality of goods corresponding to each warehouse identification, and an inventory quantity corresponding to each goods identification. Optionally, first, according to the warehouse identifier of the warehouse, determining a plurality of goods identifiers corresponding to the warehouse identifier in the inventory data, and according to the goods identifier of the required goods, determining the inventory corresponding to the goods identifier of the required goods in the inventory corresponding to each of the plurality of goods identifiers as the inventory of the goods stored in the warehouse corresponding to the factory.

The single-day maximum delivery volume (i.e., the upper limit of the bearing capacity of the lunar platform) of the warehouse can also be acquired from warehouse information or inherent main data of the warehouse.

The latest delivery date of the goods may be obtained from the order data.

The order data comprises the goods identifications of the multiple goods and the latest delivery date corresponding to each goods identification, the latest delivery date corresponding to the goods identification of the goods is obtained from the order data, and the latest delivery date corresponding to the goods identification is determined as the latest delivery date of the goods.

And S302, determining the delivery plan information corresponding to the factory according to the accumulated production, the stock and the single-day maximum delivery.

The delivery plan information includes a target earliest delivery date and a target latest delivery date of the goods, a corresponding warehouse identifier of the goods, and a delay identifier indicating whether to delay delivery.

For example, delivery plan information is shown in table 1 below.

TABLE 1

Order form	Target earliest delivery date	Latest delivery date of destination	Pick-up point (warehouse sign)	Delay mark
					Order 1	1	5	1	0
Order 2	4	3	3	0
					Order 3	/	/	/	1
…	…	…	…	…

The "order" in the table above is a dimension, the lower value indicates a specific order number, each order number corresponds to a specific order of the order data, and the order is provided with the following information: product model number, quantity, destination, latest shipping date, etc.

The "/" in the above table indicates that there is no, e.g., "order 3" belongs to a deferred status, and thus there is no content in dimensions such as "target earliest delivery date".

S303, acquiring the carrying capacity of the delivery vehicle.

The bearing capacity can be obtained from vehicle resources.

For example, the vehicle resource includes identifiers of a plurality of vehicles and bearing capacities corresponding to the identifiers, and the bearing capacity corresponding to the identifier may be acquired from the vehicle resource according to the identifier of the delivery vehicle, and the bearing capacity corresponding to the identifier may be determined as the bearing capacity of the delivery vehicle.

And S304, determining vehicle allocation plan information according to the delivery plan information and the bearing capacity.

The vehicle allocation plan information comprises a target delivery date of the goods and a vehicle identifier corresponding to the goods. The vehicle allocation plan information also comprises a warehouse identifier corresponding to the goods and a delay identifier indicating whether to delay delivery.

For example, the vehicle allocation plan information is shown in table 2 below.

TABLE 2

Order form	Warehouse identification	Date of destination shipment	Vehicle identification	Delay mark
					Order 1	12	2	12	0
Order 2	/	/	/	1
					Order 3	5	4	43	0
…	…	…	…	…

That is, the "postpone mark" of "order 2 should be" 1 ", which indicates that the order is postponed, does not satisfy the delivery condition, and cannot be allocated.

In the data processing method provided in the embodiment of fig. 3, the cumulative production amount of the goods produced by the factory, the inventory amount of the goods stored in the warehouse corresponding to the factory, and the maximum daily delivery amount of the warehouse are obtained; determining delivery plan information corresponding to the factory according to the accumulated production, the inventory and the single-day maximum delivery; acquiring the carrying capacity of a delivery vehicle; and determining the vehicle allocation plan information according to the delivery plan information and the bearing capacity. In the method, delivery plan information corresponding to a factory is automatically determined according to the accumulated production capacity, the stock quantity and the single-day maximum delivery quantity; and according to the delivery plan information and the bearing capacity, the vehicle allocation plan information is determined, so that manual delivery plan and vehicle allocation plan making is avoided, and the planning customization efficiency is improved.

On the basis of the above embodiments, the data processing method provided by the embodiments of the present application is further described in detail with reference to fig. 4, specifically, refer to fig. 4.

Fig. 4 is a second flowchart of a data processing method according to an embodiment of the present application. As shown in fig. 4, the method includes:

s401, acquiring the accumulated production of the goods produced by the factory, the stock of the goods stored in the warehouse corresponding to the factory, the single-day maximum delivery quantity of the warehouse and the latest delivery date of the goods.

Specifically, the execution method of S401 is the same as the execution method of S301, and the execution process of S401 is not described herein again.

S402, constructing a first constraint condition according to the accumulated production capacity, the inventory quantity and the single-day maximum delivery quantity.

Optionally, before building the first constraint, a variable declaration is made. The variable declaration needs to be enumerated according to date and warehouse.

The delivery status of the order i in the warehouse s on the date d is represented by a boolean variable.

The status of the order i is shown as a delay status (i.e., a delay identifier), and the status is a boolean variable, 1 shows a delay, and 0 shows no delay. Specifically, the following table 3 shows.

TABLE 3 Simplex factory delivery plan variables

In one possible design, a first constraint is constructed based on the cumulative production volume, inventory and single day maximum shipment, including: constructing a first sub-constraint condition according to the maximum daily shipment and a strategy that the daily shipment of the warehouse is not more than the maximum daily shipment corresponding to the warehouse;

For example, the first sub-constraint is shown in equation 1 below:

wherein the content of the first and second substances,

indicating the amount of shipment of order i,

the maximum delivery amount per day corresponding to the warehouse S is shown, D is a list of shippable dates, and S is a list of warehouses including a factory warehouse and a warehouse.

For example, the second sub-constraint is shown in equation 2 below:

where a denotes a first preset value (e.g., 1), and I denotes an order list.

For example, the third sub-constraint is shown in equation 3 below:

wherein the content of the first and second substances,

indicating the shipping status of all orders taken at s warehouse accumulated up to d' day,

for the stock quantity of the stored order i in the warehouse s,

the goods i 'indicated by order i are accumulated to the accumulated production of d'.

And S403, processing the first optimization target model stored in advance according to the first constraint condition to obtain a warehouse identifier, a delay identifier and a delivery date.

The first optimization objective model comprises variables corresponding to the planned delivery date, the warehouse identifier and the delay identifier respectively.

Optionally, the first solver is controlled to process the first optimization target model according to the first constraint condition, so as to obtain a warehouse identifier, a postpone identifier and a delivery date.

Specifically, the variables corresponding to the planned delivery date, the warehouse identifier and the postpone identifier are solved to obtain the planned delivery date, the warehouse identifier and the postpone identifier.

Specifically, the first optimization objective model may be, for example: cost_all_1＝cost01+cost02。cost_all"1" is the first optimization objective function, cost01 is the sub-optimization function created with the goal of delivering the goods indicated by the order as early as possible, and cost02 is the sub-optimization function created with the goal of concentrating the goods indicated by the order addressed to the same trade as possible in a few warehouses.

Wherein the content of the first and second substances,

and d is a variable corresponding to the planned delivery date. d "is the latest delivery date.

The state of the warehouse s for the trade g is an intermediate variable, which is a boolean variable.

Indicating the total number of warehouses occupied by goods sent to trade and worker G, and G indicating the list of all trade and worker in the order data.

Note that the difference between d and d' is equal to Δ t, and when Δ t ≧ 0 (indicating early delivery), K₁₀Less than 0; when Δ t < 0 (indicating late delivery), K₁₁Is greater than 0; when Δ t is Delay (indicating a deferred shipment), K₁₂＞＞K₁₁。k₁₃The weight value is the weight of the integration degree of the picking points sent to the same industry and trade.

Alternatively, the cost mentioned above_allA _1 may also be the first optimized objective function after normalization. K above₁₀、K₁₁、K₁₂And k₁₃The expression can also be designed according to the dependent variable.

S404, judging whether the scheduled delivery date is larger than or equal to the latest delivery date.

If yes, go to step S405, otherwise, go to step S406.

S405, determining the planned delivery date as the latest target delivery date, and determining the latest delivery date as the earliest target delivery date.

S406, determining the latest delivery date as the target latest delivery date, and determining the planned delivery date as the target earliest delivery date.

S407, the carrying capacity of the delivery vehicle is obtained.

Specifically, the execution method of S407 is the same as the execution method of S303, and the execution process of S407 is not described herein again.

It is pointed out

Order i equal to 1 does not participate in the execution of S408-S409.

And S408, constructing a second constraint condition according to the bearing capacity.

Optionally, before constructing the second constraint, variable declarations are made as shown in table 4 below. d denotes the current date, i.e. the date passage is generated, s_i,jIndicating a state of order i matching onto delivery vehicle j (a boolean variable with a value of 1 indicating a match, and a value of 0 indicating a no match),

indicating whether the order i delays the vehicle allocation (the Boolean type variable with the value of 1 indicates the delayed vehicle allocation and the value of 0 indicates the normal vehicle allocation).

TABLE 4 List the results of the "delivery date-industry trade" allocation plan

In one possible design, S408 includes: constructing a fourth sub-constraint condition according to the bearing capacity and a strategy that the bearing capacity of the delivery vehicle is not more than the maximum bearing capacity of the delivery vehicle;

For example, the fourth sub-constraint is shown in equation 4 below:

wherein, X_iRepresenting the volume (or weight), X, of the goods indicated by order i_jRepresenting the volume capacity (or weight capacity) of vehicle J, and J representing the list of vehicles available on day d.

For example, the fifth sub-constraint is shown in equation 5 below:

wherein B represents a second preset value (e.g., 1).

And S409, processing a second optimized target model stored in advance according to the target earliest delivery date and the target latest delivery date in the delivery plan and the second constraint condition to obtain the target delivery date of the goods and the vehicle identifier corresponding to the goods.

The second optimization objective model comprises variables corresponding to the target delivery date and the vehicle identification respectively.

Optionally, controlling a second solver, and processing the second optimized target model according to a second constraint condition to obtain a target delivery date of the goods and a vehicle identifier corresponding to the goods.

For example, the second optimization objective model is shown in equation 6 below:

cost_all2-Cost 03+ Cost04+ Cost05 formula 6;

wherein, cost_all"2" is the second optimization objective function, Cost03 is the sub-optimization function established with the goal of order delivery as early as possible, Cost04 is the total Cost of delivery vehicles as full as possible, and Cost05 is the sub-optimization function established with the goal of delivery vehicles passing through as few warehouses as possible. By "pass-through" is meant that the vehicle arrives at a warehouse for pickup.

The Cost of the order is delivery available in a vehicle, wherein Cost03 is Cost06+ Cost07, Cost06 is Cost of the order in the vehicle allocation and delivery, and Cost07 is Cost of the order in the vehicle allocation.

As a variable corresponding to the target delivery date,. DELTA.d_iA value of > 0 indicates that the target ship date for order i is less than the maximum shippable date,

indicating the target earliest shipping date for order i,

indicating the target latest delivery date of order i (two dates belonging to Table 1), k₁And k₂Indicating an adjustable weight. Note that Δ d_iDeltad represents delivery ahead of time, is ≧ 0_i< 0 indicates late delivery.

k₃And k₄Indicating an adjustable weight.

M is a positive integer;

indicating whether delivery vehicle j is carrying an order, k₅Represents a weight when

At the minimum, Cost04 is the smallest. Wherein the content of the first and second substances,

is a constraint relationship established by the large M method.

M is a positive integer; k is a radical of₆It is shown that the weight can be adjusted,

a state (boolean variable whose value is 1 indicating passing and whose value is 0 indicating not passing) I indicating that the delivery vehicle j passes through the warehouse s^sAn order listing indicating the need to pick up goods in warehouse s, the values from table 1.

Is a constraint relationship established by the large M method.

Alternatively, the cost mentioned above_allAnd 2 can also be a second optimized objective function after normalization. K above₁To k is₅The expression can also be designed according to the dependent variable.

In the data processing method provided in the embodiment of fig. 4, according to a first constraint condition, a first optimization target model stored in advance is processed to obtain a warehouse identifier, a postponed identifier and a delivery date, and according to a target earliest delivery date and a target latest delivery date in a delivery plan and a second constraint condition, a second optimization target model stored in advance is processed to obtain a target delivery date of a cargo and a vehicle identifier corresponding to the cargo, so that a step-by-step MIP algorithm is adopted to obtain delivery plan information and vehicle allocation plan information respectively, thereby reducing optimization solving time, improving optimization solving efficiency, and further improving planning customization efficiency.

Further, in the application, a step-by-step MIP algorithm is adopted to synthesize production data, inventory data, vehicle resources and the like of each factory in a factory park to cooperatively plan the optimal combination of resources, so that the comprehensive transportation cost is effectively reduced, the delivery efficiency of delivery vehicles is improved, and the precision of advanced stock in a warehouse is greatly improved.

In addition, the method replaces manual operation, and the complexity of offline communication and manual plan making is effectively avoided. According to the method, delivery plan information and allocation plan information are directly calculated through a modeling method (the method does not embody any allocation technology, so that all logics and descriptions related to allocation and allocation need to be deleted), and vehicle allocation plan information is obtained, so that the working efficiency is greatly improved. And the problem that each factory shipper can only concentrate on the shipper and is difficult to realize macroscopic resource coordination is solved, the best combination of stock and vehicle allocation can be obtained only through overall consideration, the stock precision is improved, the waiting time of delivery vehicles is reduced, and the logistics cost is greatly reduced.

Fig. 5 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application. As shown in fig. 5, the data processing apparatus 10 includes: a first obtaining module 101, a first determining module 102, a second obtaining module 103 and a second determining module 104; wherein the content of the first and second substances,

the first obtaining module 101 is configured to obtain an accumulated production amount of the goods produced by the factory, an inventory amount of the goods stored in a warehouse corresponding to the factory, a single-day maximum delivery amount of the warehouse, and a latest delivery date of the goods;

the first determining module 102 is used for determining delivery plan information corresponding to a factory according to the accumulated production capacity, the inventory, the single-day maximum delivery quantity and the latest delivery date;

a second obtaining module 103, configured to obtain a carrying capacity of the delivery vehicle;

and the second determining module 104 is configured to determine the vehicle allocation plan information according to the delivery plan information and the bearing capacity.

The data processing apparatus provided in the embodiment of the present application can execute the data processing method, and the implementation principle and the beneficial effect thereof are similar, and are not described herein again.

the first determining module 102 is specifically configured to:

In one possible design, the first determining module 102 is specifically configured to:

In one possible design, the vehicle allocation plan information includes a target delivery date of the goods and a vehicle identifier corresponding to the goods; the second determining module 104 is specifically configured to:

constructing a second constraint condition according to the bearing capacity;

In one possible design, the second determining module 104 is specifically configured to:

Fig. 6 is a hardware schematic diagram of an electronic device according to an embodiment of the present application. As shown in fig. 6, the electronic device 20 may include: a transceiver 201, a memory 202, and a processor 203.

The transceiver 201 may include: a transmitter and/or a receiver. The transmitter may also be referred to as a transmitter, a transmission port or a transmission interface, and the like.

A receiver may also be referred to as a receiver, a receive port, or a receive interface, and the like.

The transceiver 201, the memory 202, and the processor 203 are illustratively interconnected via a bus 204.

The memory 202 is used to store computer-executable instructions.

The processor 203 is configured to execute computer-executable instructions stored in the memory 202, so that the processor 203 executes the data processing method described above.

An embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the data processing method is implemented.

An embodiment of the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the data processing method can be implemented.

All or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The aforementioned program may be stored in a readable memory. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned memory (storage medium) includes: read-only memory (ROM), RAM, flash memory, hard disk, solid state disk, magnetic tape (magnetic tape), floppy disk (optical disk), and any combination thereof.

Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be apparent to those skilled in the art that various changes and modifications may be made in the embodiments of the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to encompass such modifications and variations.

In the present application, the terms "include" and variations thereof may refer to non-limiting inclusions; the term "or" and variations thereof may mean "and/or". The terms "first," "second," and the like in this application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. In the present application, "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A data processing method, comprising:

acquiring the accumulated production quantity of the goods produced by a factory, the inventory quantity of the goods stored in a warehouse corresponding to the factory, the single-day maximum delivery quantity of the warehouse and the latest delivery date of the goods;

determining delivery plan information corresponding to the factory according to the accumulated production, the inventory, the single-day maximum delivery volume and the latest delivery date;

acquiring the carrying capacity of a delivery vehicle;

2. The method of claim 1, wherein the delivery plan information comprises: the method comprises the steps that a target earliest delivery date and a target latest delivery date of goods, a warehouse identifier corresponding to the goods and a delay identifier indicating whether to delay delivery are carried out;

the determining the delivery plan information corresponding to the factory according to the accumulated production capacity, the inventory amount and the single-day maximum delivery amount includes:

constructing a first constraint condition according to the accumulated production capacity, the inventory and the single-day maximum delivery capacity;

processing a first optimization target model stored in advance according to the first constraint condition to obtain the warehouse identifier, the postpone identifier and a planned delivery date; the first optimization objective model comprises variables corresponding to the planned delivery date, the warehouse identifier and the postponement identifier respectively;

if the planned shipment date is greater than or equal to the latest shipment date, determining the planned shipment date as the target latest shipment date and determining the latest shipment date as the target earliest shipment date;

if the scheduled delivery date is less than the latest delivery date, determining the latest delivery date as the target latest delivery date, and determining the scheduled delivery date as the target earliest delivery date.

3. The method of claim 2, wherein said building a first constraint based on said cumulative production capacity, said inventory level, and said single-day maximum shipment comprises:

according to the maximum daily shipment, constructing a first sub-constraint condition according to a strategy that the daily shipment of the warehouse is not more than the maximum daily shipment corresponding to the warehouse;

according to the accumulated production capacity and the inventory quantity, constructing a third sub-constraint condition according to a strategy that the accumulated delivery quantity of the goods in a preset time period is not more than the accumulated delivery quantity of the warehouse in the preset time period;

determining the first sub-constraint, the second sub-constraint and the third sub-constraint as the first constraint.

4. The method of claim 2, wherein the processing a first pre-stored optimization goal model according to the first constraint to obtain the warehouse identifier, the postponement identifier, and a planned delivery date comprises:

and controlling a first solver, and processing the first optimization target model according to the first constraint condition to obtain the warehouse identifier, the postpone identifier and the planned delivery date.

5. The method of any of claims 1-4, wherein the allocation plan information includes a target delivery date for the good and a vehicle identification corresponding to the good;

the determining the vehicle allocation plan information according to the delivery plan information and the bearing capacity comprises the following steps:

constructing a second constraint condition according to the bearing capacity;

processing a second optimization target model stored in advance according to the target earliest delivery date and the target latest delivery date in the delivery plan information and the second constraint condition to obtain the target delivery date of the goods and the vehicle identifier corresponding to the goods; the second optimization objective model includes variables corresponding to the target delivery date and the vehicle identification.

6. The method according to claim 5, wherein the constructing a second constraint condition according to the carrying capacity comprises:

according to the bearing capacity, constructing a fourth sub-constraint condition according to a strategy that the bearing capacity of the delivery vehicle is not larger than the maximum bearing capacity of the delivery vehicle;

7. A data processing apparatus, comprising: the device comprises a first acquisition module, a first determination module, a second acquisition module and a second determination module; wherein the content of the first and second substances,

the first obtaining module is used for obtaining the accumulated production of the goods produced by a factory, the stock of the goods stored in the warehouse corresponding to the factory, the single-day maximum delivery volume of the warehouse and the latest delivery date of the goods;

the first determining module is used for determining delivery plan information corresponding to the factory according to the accumulated production capacity, the inventory, the single-day maximum delivery volume and the latest delivery date;

8. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;

the memory stores computer-executable instructions;

the processor executes computer-executable instructions stored by the memory to implement the method of any of claims 1-6.

9. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, perform the method of any one of claims 1-6.

10. A computer program product comprising a computer program which, when executed by a processor, implements the method of any one of claims 1-6.