CN113887828A

CN113887828A - Intelligent supply chain production, transportation and marketing cooperation and real-time network planning method and device

Info

Publication number: CN113887828A
Application number: CN202111241020.4A
Authority: CN
Inventors: 马潇宇
Original assignee: Beijing Foreign Studies University
Current assignee: Beijing Foreign Studies University
Priority date: 2021-10-25
Filing date: 2021-10-25
Publication date: 2022-01-04
Anticipated expiration: 2041-10-25
Also published as: CN113887828B

Abstract

The invention provides an intelligent supply chain production, transportation and sale cooperation and real-time network planning method and device, and belongs to the field of supply chain planning. The method comprises the following steps: receiving product data of a manufacturer and demand data of a consumer; when the product meets the demand, a supply chain network is constructed, and the nodes of the supply chain network at least comprise a production node, a demand node and a transfer node; determining, in the supply chain network, a plurality of shortest transportation paths between the manufacturer and the consumer; constructing a supply chain network optimization model; and solving the supply chain network optimization model based on the plurality of shortest transportation paths to generate an optimal vehicle distribution scheme between the manufacturer and the consumer. By adopting the invention, the production, transportation and marketing cooperation with the consumer as the center can be realized, and the distribution efficiency is improved.

Description

Intelligent supply chain production, transportation and marketing cooperation and real-time network planning method and device

Technical Field

The invention relates to the field of supply chain planning, in particular to a method and a device for intelligent supply chain production, transportation and sale cooperation and real-time network planning.

Background

Supply chain production, transportation and sale coordination and network planning problems are one of the most critical problems in the field of supply chain planning. The method needs to make comprehensive operation decisions such as transportation network planning, vehicle path planning, cargo transportation schemes and the like according to various information such as manufacturer production conditions, consumer demand conditions, transportation vehicle and transit node conditions and the like.

Most of the existing network planning, path planning and transportation schemes are manually made by related workers according to experience, the method is time-consuming and labor-consuming, and the made schemes also have the defects of mismatching of production and transportation, low cargo timeliness, capacity waste and the like, so that the cost of manufacturers is high, and the experience of consumers is poor.

Therefore, an intelligent supply chain operation and sales coordination and real-time network planning method is needed to improve the supply chain operation and sales coordination and distribution efficiency.

Disclosure of Invention

In order to solve the problems in the prior art, the embodiment of the invention provides a method and a device for intelligent supply chain production, transportation and marketing cooperation and real-time network planning. The technical scheme is as follows:

according to an aspect of the present invention, there is provided an intelligent supply chain production-transportation-sales coordination and real-time network planning method, the method including:

receiving product data of a manufacturer and demand data of a consumer;

when the product meets the demand, a supply chain network is constructed, and the nodes of the supply chain network at least comprise a production node, a demand node and a transfer node;

determining, in the supply chain network, a plurality of shortest transportation paths between the manufacturer and the consumer;

constructing a supply chain network optimization model;

and solving the supply chain network optimization model based on the plurality of shortest transportation paths to generate an optimal vehicle distribution scheme between the manufacturer and the consumer.

According to another aspect of the present invention, there is provided an intelligent supply chain production-transportation-sales coordination and real-time network planning apparatus, the apparatus comprising:

the receiving module is used for receiving product data of a manufacturer and demand data of a consumer;

the system comprises a first construction module, a second construction module and a third construction module, wherein the first construction module is used for constructing a supply chain network when a product meets requirements, and nodes of the supply chain network at least comprise a production node, a demand node and a transfer node;

a determination module to determine a plurality of shortest transportation paths between the manufacturer and the consumer in the supply chain network;

the second construction module is used for constructing a supply chain network optimization model;

and the generating module is used for solving the supply chain network optimization model based on the plurality of shortest transportation paths and generating an optimal vehicle distribution scheme between the manufacturer and the consumer.

According to another aspect of the present invention, there is provided an electronic apparatus including:

a processor; and

a memory for storing a program, wherein the program is stored in the memory,

wherein the program comprises instructions which, when executed by the processor, cause the processor to perform the intelligent supply chain production and transportation and marketing coordination and real-time network planning method described above.

According to another aspect of the present invention, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the above intelligent supply chain production and transportation and marketing coordination and real-time network planning method.

In the embodiment of the invention, the product data of a manufacturer and the demand data of a consumer are utilized to carry out real-time network planning through an intelligent optimization algorithm, so that the production, transportation and marketing cooperation with the consumer as the center is realized, and the distribution efficiency is improved.

Drawings

Further details, features and advantages of the invention are disclosed in the following description of exemplary embodiments with reference to the accompanying drawings, in which:

FIG. 1 illustrates a flow diagram of an intelligent supply chain production and transportation coordination and real-time network planning method according to an exemplary embodiment of the present invention;

FIG. 2 illustrates a supply chain network diagram according to an exemplary embodiment of the present invention;

FIG. 3 shows a schematic view of a transport arc according to an exemplary embodiment of the invention;

FIG. 4 shows a schematic block diagram of an intelligent supply chain production and transportation and marketing coordination and real-time network planning apparatus according to an exemplary embodiment of the present invention;

FIG. 5 illustrates a block diagram of an exemplary electronic device that can be used to implement an embodiment of the invention.

Detailed Description

Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and the embodiments of the present invention are illustrative only and are not intended to limit the scope of the present invention.

It should be understood that the various steps recited in the method embodiments of the present invention may be performed in a different order and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the invention is not limited in this respect.

The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description. It should be noted that the terms "first", "second", and the like in the present invention are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.

It is noted that references to "a", "an", and "the" modifications in the present invention are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that reference to "one or more" unless the context clearly dictates otherwise.

The names of messages or information exchanged between devices in the embodiments of the present invention are for illustrative purposes only, and are not intended to limit the scope of the messages or information.

The invention provides an intelligent supply chain production, transportation and sale cooperation and real-time network planning method which can be completed by a terminal, a server and/or other equipment with processing capacity. The method provided by the embodiment of the present invention may be performed by any one of the above devices, or may be performed by a plurality of devices, which is not limited in this respect.

In this embodiment, a supply chain production, transportation and sale cooperation system is taken as an example, and the method is described with reference to a flow chart of an intelligent supply chain production, transportation and sale cooperation and real-time network planning method shown in fig. 1.

Step 101, product data of a manufacturer and demand data of a consumer are received.

In one possible implementation, the supply chain production, transportation and sale coordination system may include at least a manufacturer, a consumer, and a server providing background support. The present embodiment is described with a server as an execution subject.

The server may have the following data stored therein:

(1) manufacturer's production data: geographic location of the manufacturer, type of product produced, volume of product produced, weight of product produced, current inventory quantity, productivity.

(2) Consumer demand data: the geographic location of the consumer, the type and quantity of products ordered by the consumer, the time of receipt (which may be specific to the hour) expected by the consumer.

(3) Data of transport vehicle: the geographical position of the transport vehicle, the volume in the vehicle, the amount of load in the vehicle, the travel distance limit, the use cost of different vehicle types, the cargo loading and unloading speed of the vehicle, and the travel speed of the vehicle on different roads in different time periods.

(4) Data of the transit node: the geographic position of the transit node, the capacity space of the transit node, the speed of loading and unloading goods by the transit node, and the operation cost consumed by each piece of goods through the transit node.

(5) Other parameters: the start time and end time of each time period are planned (e.g., 8: 00-8: 30).

The method comprises the steps of obtaining production data of a manufacturer and demand data of a consumer in real time based on an Application Programming Interface (API), obtaining data of a transport vehicle and data of a transfer node in real time based on intelligent hardware comprising a sensor, and uploading the data to a server. Further, the server may receive product data for a plurality of manufacturers and demand data for a plurality of consumers.

Step 102, when the product meets the demand, a supply chain network is constructed.

As shown in fig. 2, the nodes of the supply chain network at least include a production node, a demand node, and a transit node. The production node corresponds to the geographic location of the manufacturer and the demand node corresponds to the geographic location of the consumer. The transit node is a node connected with a logistics line in the logistics network and is a logistics aggregation point with functions of transit and comprehensive service.

In one possible embodiment, the server may determine whether the type and quantity of the product currently produced by the manufacturer meet the needs of all consumers when the planning instruction is triggered. The condition for triggering the planning instruction may be a periodic trigger or a manual trigger, which is not limited in this embodiment.

If so, execution may begin at step 103, where the shortest transportation path is calculated.

Optionally, when the product does not meet the demand, determining product data to be produced based on the product data and the demand data; and issuing the data of the product to be produced to the manufacturer.

In one possible embodiment, the type and amount of stock shortage may be determined based on the current inventory amount of the manufacturer and the demand data of the customer, and the server may place an order for the corresponding type of manufacturer to supply the corresponding product.

In step 103, a plurality of shortest transportation routes between the manufacturer and the customer are determined in the supply chain network.

The server may calculate the shortest transportation path between each production node and demand node in the supply chain network based on Dijkstra's algorithm.

In a preferred embodiment, the dijkstra algorithm can be modified to reduce the repetition rate of the transport path. At this time, the process of step 103 may be as follows:

determining an initial shortest transportation path between each production node and each demand node based on a Dijkstra algorithm;

determining the total quantity of the transported goods of each initial shortest transportation path, and sequencing the total quantity of the transported goods to obtain a sequencing table of the initial shortest transportation path;

the following screening process is performed on the sorted list loop: acquiring and storing an initial shortest transportation path with the largest total quantity of transported goods; traversing the other initial shortest transportation paths of the current sorting table, and deleting the initial shortest transportation path completely superposed with the initial shortest transportation path; when the traversal is finished, deleting the initial shortest transportation path in the current sorting table;

determining the stored plurality of initial shortest transportation paths as the plurality of shortest transportation paths between the manufacturer and the consumer until the sorted list is empty.

The total delivery quantity may refer to a total delivery quantity in a predicted planning period, and may be obtained according to the data stored in the server.

Through the processing, the shortest transportation path with larger total amount of transported goods can be reserved, and the transportation path completely coincident with the shortest transportation path can be deleted, so that the repeated transportation on the same path can be avoided, and the transportation efficiency can be improved.

And 104, constructing a supply chain network optimization model.

The objective function of the supply chain network optimization model is to minimize transportation costs, which may include at least node enablement costs, vehicle enablement costs, and vehicle fuel consumption costs.

The objective function can be expressed by the following formula:

Min∑_is_iy_i+∑_k(f_kz_k+cd_k) (1)

wherein, y_iVariable 0-1, z, enabled or not for the ith node_kVariable 0-1, s, for whether kth vehicle is active or not_iFor the activation cost of the ith node, f_kThe starting cost of the kth vehicle, c the fuel consumption per kilometer, d_kAnd distributing the distance for the transportation of the k-th vehicle. The decision variable is y_iAnd z_kThat is, solving the supply chain network optimization model is solving which nodes to enable and determine the transportation path and which transportation vehicles to use for delivery.

The constraints may include at least a supply chain network traffic balancing constraint and a vehicle resource limiting constraint, and may be expressed by the following formula:

wherein A is_oIs m_o×n_oA dimensional matrix representing an incidence matrix of nodes of the supply chain network and the transport arcs, wherein m_oIs the number of nodes involved from the o node (production node) to the d node (demand node) in the supply chain network, n_oIs the number of arc of transportation for each possible transfer scenario, so A_oAll modes of transport (direct and transit) are covered. As shown in the schematic diagram of the transportation arc shown in FIG. 3, when there is a supply chain distribution demand between the nodes o and d, the direct path is from the node o to the node d, and the transit paths are o-1-d, o-2-d, and o-1-2-d, i.e. the goods can be passed through the transitAnd directly, or through forwarding in the node 1 or the node 2 or the

nodes

1 and 2.

x^o _ijThe traffic is distributed for a certain transport arc (node i to node j) on the supply chain network from node o to node d. D_oIs the demand for the dispatch from the o node to the d node. l_koAnd the kth vehicle is the traffic distributed from the o node to the d node in the supply chain network. W is the load of a single load, v is the volume of a single load, W_kIs the load limit of the kth vehicle, V_kIs the volume limit of the kth vehicle.

The constraint (2) is a supply chain network flow balance constraint, and ensures that all goods can be delivered to the position of a consumer from the position of a manufacturer in a direct or transfer mode, and ensures that the quantity of the actually transported goods is equal to the total demand; constraints (3) and (4) are vehicle resource limit constraints, the constraint (3) indicates that the total amount of cargo carried by the kth vehicle in the supply chain network cannot exceed the loading limit, and the constraint (4) indicates that the volume of cargo carried by the kth vehicle in the supply chain network cannot exceed the volume limit; constraint (5) indicates that the decision variable is a 0-1 variable; the constraint (6) indicates that the decision variable is a non-negative variable.

And 105, solving a supply chain network optimization model based on the plurality of shortest transportation paths to generate an optimal vehicle distribution scheme between the manufacturer and the consumer.

In a possible implementation, the processing of step 105 may be as follows: taking the shortest transportation paths as initial feasible solutions, solving a supply chain network optimization model, and calculating the optimal transportation path of the supply chain network and a vehicle combination corresponding to each transportation path; an optimal vehicle distribution scheme between the manufacturer and the customer is generated based on each transportation path and the corresponding vehicle combination.

That is, in such an embodiment, the transport route may be fixed, and the vehicle combination corresponding to each transport route may be calculated.

In another possible implementation, the processing of step 105 may be as follows: and solving a supply chain network optimization model based on a column generation algorithm and a plurality of shortest transportation paths to generate an optimal vehicle distribution scheme between a manufacturer and a consumer.

The vehicle distribution scheme comprises the shortest transportation path and the corresponding vehicle combination, and/or the non-shortest transportation path and the corresponding vehicle combination.

The specific treatment is as follows:

determining a plurality of non-shortest transportation paths between a manufacturer and a consumer in a supply chain network;

solving a supply chain network optimization model based on a column generation algorithm, screening out a plurality of target transportation paths from a plurality of shortest transportation paths and a plurality of non-shortest transportation paths, and calculating corresponding vehicle combinations;

an optimal vehicle distribution scheme between the manufacturer and the customer is generated based on the plurality of target transportation paths and the corresponding vehicle combinations.

The plurality of target transportation paths may include a shortest transportation path and/or a non-shortest transportation path.

That is to say, by utilizing a column generation algorithm, vehicle distribution schemes for transporting along non-shortest transportation paths are added to the goods to be transported between the nodes in each time period, and by adding the vehicle distribution schemes for transporting along the non-shortest transportation paths, the goods sharing is realized, the cost is reduced, and the timeliness is improved. And in addition, a train generation algorithm is used for selecting a vehicle distribution scheme which can reduce the cost from a plurality of vehicle distribution schemes which are found along the non-shortest transportation path, the problem is added, invalid vehicle distribution schemes are screened out, the scale of the model can be effectively controlled, and the solving speed is improved.

After the vehicle distribution scheme is obtained, a cargo transportation scheme table of each supply chain node (including a production node, a transfer node and a demand node), a vehicle route table of the vehicle in each time period and the like can be output, and a future path track can be visually shown through hardware. These can provide guidance for the work of high-level decision makers of enterprises and operators such as dispatchers and distributors.

The cargo transportation schedule is shown in table 1 below:

starting node

Transfer node

1

……

Transfer node n

Destination node

Cargo

1

o1

Node

1

……

Node 5

d1

Goods

2

o3

Node 3

……

Node 6

d2

Goods 3

o2

Node

2

……

Node 6

d2

TABLE 1

The driving route table is shown in the following table 2:

	8:00-8:30	8:30-9:00	……	16:30-17:00
					vehicle 1	o 1-node 1	Node 1-node 4	……	Node 5-d1
Vehicle 2	o 3-node 3	Node 3-node 5	……	Node 6-d2
					Vehicle 3	o 2-node 2	Node 2-node 6	……	Free up

TABLE 2

The embodiment of the invention can obtain the following beneficial effects:

(1) the invention utilizes the API interface and intelligent hardware to obtain the multidimensional data of production, transportation and demand sides, and carries out real-time network planning through an intelligent optimization algorithm, thereby realizing the production, transportation and marketing cooperation taking consumers as the center.

(2) When planning the transportation scheme of the goods, the invention considers various vehicle distribution schemes along the shortest transportation path and along the non-shortest transportation path, and utilizes the column generation algorithm to carry out intelligent selection, thereby realizing the goods sharing, effectively reducing the cost and improving the timeliness.

(3) The invention uses the column generation method, can effectively control the problem scale and improve the solving speed.

(4) The invention can automatically solve the large-scale complex problems of multiple vehicles, multiple transfer nodes and multiple time periods in a short time, and can flexibly adjust correspondingly according to the change of the vehicle running line and the position of the transfer node. Such large-scale complex problems are difficult to effectively solve by manual scheduling.

The embodiment of the invention provides an intelligent supply chain production, transportation and sale cooperation and real-time network planning device, which is used for realizing the intelligent supply chain production, transportation and sale cooperation and real-time network planning method. As shown in fig. 4, a schematic block diagram of the intelligent supply chain production, transportation, and sale coordination and real-time network planning apparatus 400 includes: a receiving module 401, a first building module 402, a determining module 403, a second building module 404, a generating module 405.

A receiving module 401, configured to receive product data of a manufacturer and demand data of a consumer;

a first building module 402, configured to build a supply chain network when a product meets a demand, wherein nodes of the supply chain network at least include a production node, a demand node, and a transit node;

a determining module 403, configured to determine a plurality of shortest transportation paths between the manufacturer and the consumer in the supply chain network;

a second construction module 404 for constructing a supply chain network optimization model;

a generating module 405, configured to solve the supply chain network optimization model based on the plurality of shortest transportation paths, and generate an optimal vehicle distribution scheme between the manufacturer and the customer.

Optionally, the apparatus further includes an issuing module, where the issuing module is configured to:

when the product does not meet the demand, determining product data to be produced based on the product data and the demand data;

and transmitting the product data to be produced to the manufacturer.

Optionally, the determining module 403 is configured to:

determining the total quantity of the transported goods of each initial shortest transportation path, and sequencing the total quantity of the transported goods to obtain a sequencing list of the initial shortest transportation path;

performing the following screening process on the sorting table cycle: acquiring and storing an initial shortest transportation path with the largest total quantity of transported goods; traversing the other initial shortest transportation paths of the current sorting table, and deleting the initial shortest transportation path completely superposed with the initial shortest transportation path; when the traversal is finished, deleting the initial shortest transportation path in the current sorting table;

Optionally, the objective function of the supply chain network optimization model is to minimize transportation cost, where the transportation cost at least includes node enabling cost, vehicle enabling cost, and vehicle fuel consumption cost; the constraints include at least supply chain network traffic balancing constraints and vehicle resource limiting constraints.

Optionally, the generating module 405 is configured to:

taking the shortest transportation paths as initial feasible solutions, solving the supply chain network optimization model, and calculating the optimal transportation path of the supply chain network and the vehicle combination corresponding to each transportation path;

generating an optimal vehicle distribution scheme between the manufacturer and the customer based on each transportation path and the corresponding vehicle combination.

Optionally, the generating module 405 is configured to:

and solving the supply chain network optimization model based on a column generation algorithm and the plurality of shortest transportation paths, and generating an optimal vehicle distribution scheme between the manufacturer and the consumer, wherein the optimal vehicle distribution scheme comprises the shortest transportation paths and corresponding vehicle combinations, and/or non-shortest transportation paths and corresponding vehicle combinations.

Optionally, the generating module 405 is configured to:

determining, in the supply chain network, a plurality of non-shortest transportation paths between the manufacturer and the consumer;

solving the supply chain network optimization model based on a column generation algorithm, screening out a plurality of target transportation paths from the plurality of shortest transportation paths and the plurality of non-shortest transportation paths, and calculating corresponding vehicle combinations;

generating an optimal vehicle distribution scheme between the manufacturer and the customer based on the plurality of target transportation paths and the corresponding vehicle combinations.

An exemplary embodiment of the present invention also provides an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program, when executed by the at least one processor, is for causing the electronic device to perform a method according to an embodiment of the invention.

Exemplary embodiments of the present invention also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is operable to cause the computer to perform a method according to an embodiment of the present invention.

Exemplary embodiments of the present invention also provide a computer program product comprising a computer program, wherein the computer program is operative, when executed by a processor of a computer, to cause the computer to perform a method according to an embodiment of the present invention.

Referring to fig. 5, a block diagram of a structure of an electronic device 500, which may be a server or a terminal of the present invention, which is an example of a hardware device that may be applied to aspects of the present invention, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 5, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The calculation unit 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

A number of components in the electronic device 500 are connected to the I/O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 may be any type of device capable of inputting information to the electronic device 500, and the input unit 506 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device. Output unit 507 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. The storage unit 508 may include, but is not limited to, a magnetic disk, an optical disk. The communication unit 509 allows the electronic device 500 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and/or the like.

The computing unit 501 may be a variety of general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 501 performs the respective methods and processes described above. For example, in some embodiments, the intelligent supply chain production, transportation, marketing coordination and real-time network planning method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 500 via the ROM 502 and/or the communication unit 509. In some embodiments, the computing unit 501 may be configured to perform the intelligent supply chain logistics and distribution coordination and real-time network planning method by any other suitable means (e.g., by means of firmware).

Program code for implementing the methods of the present invention may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

Claims

1. An intelligent supply chain production, transportation and marketing cooperation and real-time network planning method is characterized by comprising the following steps:

receiving product data of a manufacturer and demand data of a consumer;

constructing a supply chain network optimization model;

2. The intelligent supply chain production, transportation, marketing coordination and real-time network planning method according to claim 1, further comprising:

and transmitting the product data to be produced to the manufacturer.

3. The intelligent supply chain production, transportation, marketing coordination and real-time network planning method according to claim 1, wherein the determining a plurality of shortest transportation paths between the manufacturer and the consumer in the supply chain network comprises:

4. The intelligent supply chain production, transportation, marketing coordination and real-time network planning method according to claim 1, wherein an objective function of the supply chain network optimization model is to minimize transportation costs, the transportation costs including at least a node enabling cost, a vehicle enabling cost and a vehicle fuel consumption cost; the constraints include at least supply chain network traffic balancing constraints and vehicle resource limiting constraints.

5. The intelligent supply chain production, transportation, marketing coordination and real-time network planning method according to claim 1, wherein solving the supply chain network optimization model based on the plurality of shortest transportation paths to generate an optimal vehicle distribution scheme between the manufacturer and the consumer comprises:

6. The intelligent supply chain production, transportation, marketing coordination and real-time network planning method according to claim 1, wherein solving the supply chain network optimization model based on the plurality of shortest transportation paths to generate an optimal vehicle distribution scheme between the manufacturer and the consumer comprises:

7. The intelligent supply chain production, transportation, marketing coordination and real-time network planning method according to claim 6, wherein the solving the supply chain network optimization model based on the column generation algorithm and the plurality of shortest transportation paths to generate the optimal vehicle distribution scheme between the manufacturer and the consumer comprises:

8. An intelligent supply chain production, transportation and marketing cooperation and real-time network planning device, which is characterized by comprising:

a first construction module, configured to construct a supply chain network when the product meets the demand, where nodes of the supply chain network at least include a production node, a demand node, and a transfer node;

9. An electronic device, comprising:

a processor; and

a memory for storing a program, wherein the program is stored in the memory,

wherein the program comprises instructions which, when executed by the processor, cause the processor to carry out the method according to any one of claims 1-7.

10. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-7.