CN113554397A - Logistics planning method and device, electronic equipment and computer readable medium - Google Patents
Logistics planning method and device, electronic equipment and computer readable medium Download PDFInfo
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Abstract
The application discloses a logistics planning method, a logistics planning device, electronic equipment and a computer readable medium, wherein the method comprises the following steps: constructing a three-dimensional simulation model corresponding to a commodity SKU; generating a simulated fluid corresponding to a commodity SKU in a purchase order according to the purchase order of the shop; simulating the flowing process of the simulated fluid which completely flows from the shop cone corresponding to the shop initiating the purchase order to the shop cones corresponding to all shops within a preset range in the three-dimensional simulation model corresponding to the commodity SKU; recording all the flow processes of a commodity SKU corresponding to a purchase order and extracting the completion time of the flow processes; and inquiring the warehouse cone table corresponding to the flow process with the shortest completion time, and taking the warehouse position corresponding to the warehouse cone table as the goods taking address of the commodity SKU in the purchase order. The logistics configuration method has the advantage that the logistics configuration is realized in a mode of reversely simulating the logistics process by constructing the three-dimensional simulation model.
Description
Technical Field
The present application relates to the field of computers, and in particular, to a logistics planning method, apparatus, electronic device, and computer readable medium.
Background
The existing small and medium-sized convenience stores usually carry out off-line commodity purchase at suppliers through own channels, and due to the characteristics of the small and medium-sized convenience stores, the small and medium-sized convenience stores cannot purchase goods in large batches, so that effective bargaining can not be carried out with the suppliers, and meanwhile, due to the requirement of wholesale of the suppliers on the purchase quantity, the small and medium-sized convenience stores need to guarantee a certain scale for each purchase, so that the inventory problem is caused.
From the perspective of suppliers, the scattered purchasing mode of small and medium-sized convenience stores leads to the increase of the warehousing cost of the suppliers, so that the supply price is kept high.
In the related art, as shown in fig. 1, orders of a plurality of stores are collected through an internet platform, then uniform purchasing and logistics picking are performed on the orders to a supplier, and then the orders are delivered to the corresponding stores by a carrier vehicle according to the purchasing orders, so that the warehousing cost of the stores such as convenience stores is reduced, and the purchasing flexibility is improved.
In another related art, in order to realize the optimal distribution of technical logistics, as disclosed in chinese patent publication No. CN112016876B, there is provided a method for automatically configuring goods by a computer program, which can configure the path and assembly scheme of a vehicle according to the location of a supplier and a merchant and the order situation.
In other related art, schemes for configuring logistics routes based on pick and delivery addresses and vehicle locations are provided.
However, these solutions tend to rely on more complex algorithms and they only consider the way the two-dimensional route is optimal, not the order itself and the warehousing.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present application provide a logistics planning method, apparatus, electronic device and computer readable medium to solve the technical problems mentioned in the background section above.
As a first aspect of the present application, some embodiments of the present application provide a logistics planning method, including the steps of: constructing a three-dimensional simulation model corresponding to a commodity SKU, and respectively constructing a shop cone, a warehouse cone and a diversion trench in the three-dimensional simulation model to represent shops, warehouses and roads, wherein the shop cone and the warehouse cone have opposite generation directions; generating a simulated fluid corresponding to a commodity SKU in the purchase order according to the purchase order of the shop; simulating the flowing process of the simulated fluid which completely flows into the shop cones corresponding to all the shops within a preset range from the shop cone corresponding to the shop which initiates the purchase order in the three-dimensional simulation model corresponding to the commodity SKU; recording all the flow processes corresponding to a commodity SKU in a purchase order and extracting the completion time of the flow processes; and inquiring the warehouse cone table corresponding to the flow process with the shortest completion time, and taking the position of the warehouse cone table corresponding to the warehouse as the pickup address of the commodity SKU in the purchase order.
Further, the logistics planning method based on dynamic data update further comprises the following steps: and simulating the flowing process and acquiring the picking address for all the commodity SKUs in the purchase order of the shop so as to generate a picking address list containing the picking addresses of all the commodity SKUs in the purchase order of the shop.
Further, the logistics planning method based on dynamic data update further comprises the following steps: and summarizing the goods taking address lists of all shops into a purchasing relation table, wherein the purchasing relation table comprises delivery addresses, goods taking addresses, purchasing order numbers and commodity SKUs with corresponding relations.
Further, the logistics planning method based on dynamic data update further comprises the following steps: and merging the pickup addresses according to the same pickup addresses in the purchasing relation table to generate a supplying relation table, wherein the supplying relation table comprises pickup addresses, delivery address sets, pickup SKU sets, purchasing order number sets and total transportation volume amounts with corresponding relations.
Further, the logistics planning method based on dynamic data update further comprises the following steps: judging whether the transportation volume corresponding to one goods taking address in the supply relation table is larger than a preset maximum transportation capacity volume threshold value or not; splitting the transport volume total if greater than the maximum capacity volume threshold.
Further, the logistics planning method based on dynamic data update further comprises the following steps: if the transportation volume is smaller than the maximum transportation volume threshold value, judging whether the transportation volume corresponding to one goods taking address in the supply relation table is larger than a minimum transportation volume threshold value or not; if the minimum capacity volume threshold is larger than the minimum capacity volume threshold, matching a virtual carrier for a pick address in the supply relation table; and if the total volume of the transportation volume is smaller than the minimum transportation volume threshold value, merging the total volume of the transportation volume with the transportation volumes corresponding to other pickup addresses.
Further, the logistics planning method based on dynamic data update further comprises the following steps: generating a logistics order according to the original transportation volume, the split transportation volume and the combined transportation volume; a matching carrier vehicle for each of said logistics orders.
As a second aspect of the present application, some embodiments of the present application provide a logistics planning method and apparatus based on dynamic data update, including: the model building module is used for building a three-dimensional simulation model corresponding to a commodity SKU, building a shop cone, a warehouse cone and a diversion trench in the three-dimensional model to represent shops, warehouses and roads respectively, wherein the shop cone and the warehouse cone have opposite generating directions; the fluid generation module is used for generating a simulation fluid corresponding to a commodity SKU in the purchase order according to the purchase order of the shop; the fluid simulation module is used for simulating the flowing process of the simulated fluid which completely flows into the shop cones corresponding to all the shops within a preset range from the shop cone corresponding to the shop which initiates the purchase order in the three-dimensional simulation model corresponding to the commodity SKU; the time recording module is used for recording all the flowing processes of one commodity SKU in one corresponding purchase order and extracting the completion time of the flowing processes; and the address query module is used for querying the warehouse cone corresponding to the flow process with the shortest completion time and taking the position of the warehouse cone corresponding to the warehouse as the goods taking address of the commodity SKU in the purchase order.
As a third aspect of the present application, some embodiments of the present application provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method described in any of the implementations of the first aspect.
As a fourth aspect of the present application, some embodiments of the present application provide a computer readable medium on which a computer program is stored, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect.
The beneficial effect of this application lies in: a logistics planning method, an apparatus, an electronic device and a computer readable medium for realizing logistics configuration in a reverse simulation logistics process manner by constructing a three-dimensional simulation model based on E-commerce data and map data are provided.
More specifically, some embodiments of the present application may produce the following specific benefits: the method comprises the steps of helping a store find a warehouse of the fastest route in a mode of constructing a three-dimensional simulation model and simulating fluid; the supply and demand conditions are reflected by the construction of virtual objects of the shops and the warehouses, and the supply and demand condition bringing factors are reflected to the flow process of the simulated fluid; the efficiency of the order is pieced together in commodity circulation has been improved through the amalgamation to getting the goods address.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it.
Further, throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
In the drawings:
FIG. 1 is a schematic view of a pattern of a store's "order-sharing" purchases in the related art;
FIG. 2 is a flow diagram of a logistics planning method according to one embodiment of the present application;
FIG. 3 is a schematic top view of a three-dimensional simulation model according to an embodiment of the present application;
FIG. 4 is a schematic top view of a three-dimensional simulation model according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a pick address list generated after a purchase order for a store has been simulated through a flow process according to one embodiment of the present application;
FIG. 6 is a schematic diagram of a procurement relationship table according to an embodiment of the application;
FIG. 7 is a schematic diagram of a provisioning relationship table according to one embodiment of the present application;
FIG. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
The meaning of the reference symbols in the figures:
100 three-dimensional visualization model, 101 warehouse cone, 102 shop cone.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure 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 this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
As shown in fig. 2, the e-commerce logistics planning method based on the visualization model specifically includes the following steps:
s101: constructing a three-dimensional simulation model corresponding to a commodity SKU, and respectively constructing a shop cone, a warehouse cone and a diversion trench in the three-dimensional simulation model to represent shops, warehouses and roads, wherein the shop cone and the warehouse cone have opposite generating directions;
s102: a simulated fluid is generated for a commodity SKU in the purchase order from the purchase order in the store.
S103: and simulating the flowing process of the simulated fluid from the shop cone corresponding to the shop initiating the purchase order to the shop cones corresponding to all shops within the preset range in the three-dimensional simulation model corresponding to the commodity SKU.
S104: all floating processes for a commodity SKU in a purchase order are recorded and the completion time of the floating process is extracted.
S105: and inquiring the warehouse cone table corresponding to the flow process with the shortest completion time, and taking the warehouse position corresponding to the warehouse cone table as the goods taking address of the commodity SKU in the purchase order.
Wherein, the step S101 specifically includes:
collecting two-dimensional coordinate data of a warehouse and a shop in two-dimensional map data;
collecting two-dimensional coordinate data of roads in the two-dimensional map data;
generating height data of the warehouse according to commodity inventory data of the warehouse;
generating height data of the stores according to the commodity order data of the stores;
and constructing a three-dimensional simulation model reflecting the warehouse, the shop and the road based on the two-dimensional coordinate data and the height data.
More specifically, as shown in fig. 3 and 4, the two-dimensional coordinate data of the warehouse and the store in the two-dimensional map data is collected in order to acquire the position data of the warehouse and the store. Specifically, the two-dimensional coordinates are obtained from a two-dimensional map data because, although the stores and warehouses have altitude differences in actual geographic locations due to terrain and earth curvature, the altitudes are not decisive factors for logistics planning, and most stores and warehouses are located in cities or even limited to business circles considered to be divided due to the shop 'sheet' mode to which the method of the present application is applied, so that from this point of view, when a three-dimensional simulation model of the present application is constructed, only two-dimensional coordinate data is obtained from the two-dimensional map data to represent positions.
Alternatively, the source of the two-dimensional map data may be public map data such as a highrise map, a Baidu map, and a Google map.
In addition, the generating of the height data of the warehouse according to the commodity inventory data of the warehouse specifically means: the current inventory data of a certain commodity in a certain warehouse is obtained, the larger the inventory data is, the larger the height data of the warehouse is, and relatively speaking, the higher the height of the warehouse in a three-dimensional simulation model is.
As a preferred scheme, the inventory data corresponding to the height data of the warehouse may be a quantity corresponding to a certain commodity SKU, and as a further preferred scheme, the inventory data corresponding to the height data of the warehouse may be a volume value corresponding to a certain commodity SKU, and the volume value may be calculated according to the packaging specification and the commodity data. The advantage of using a volume value is that it is more straightforward to reflect the space usage of the inventory.
Similarly, generating the height data of the store according to the commodity order data of the store specifically includes: the method comprises the steps of obtaining purchase data of a certain commodity in order data of a shop, wherein the height data of the shop is larger when the purchase data is larger, and the height of the shop is higher in a three-dimensional simulation model relatively speaking.
Preferably, the purchase data corresponding to the height data of the store may be quantity and volume data, and preferably, the height data of the store corresponds to a volume value corresponding to a certain product SKU.
As a further preferred solution, the ratio of the volume value V to the height data is 1:1, and 1L of volume data is equal to 1m of height data in terms of dimension, calculated according to this dimension standard.
As a more specific aspect, constructing a three-dimensional simulation model reflecting a warehouse, a shop, and a road based on two-dimensional coordinate data and height data includes the steps of: generating a two-dimensional coordinate central point of the warehouse according to the two-dimensional coordinate data of the warehouse; constructing a warehouse conical table to represent a warehouse, wherein the central axis of the warehouse conical table is vertical to the horizontal plane of the three-dimensional simulation model and passes through the two-dimensional coordinate central point of the warehouse, and the height of the warehouse conical table is in direct proportion to the height data of the warehouse; acquiring maximum inventory data of commodities in a warehouse; generating the lower bottom radius of the warehouse cone frustum according to the maximum inventory capacity data; acquiring the last warehouse-out data of commodities in a warehouse; and generating the upper bottom radius of the warehouse cone table according to the last warehouse-out data.
The two-dimensional coordinate center point may be taken as the midpoint of the two-dimensional coordinate center point by the midpoint of the diagonal of the largest rectangular frame of the warehouse building or footprint.
The maximum inventory data refers to the maximum value embodied in the system registration warehouse for storing the maximum capacity data or the historical data of the commodity, and the last delivery data is the data of the product delivery amount on the current date of the last delivery. Preferably, the maximum inventory data and the last output data are produced by using the volume data described above, the ratio of the volume data is selected to be 1:0.1, and 1L of volume data corresponds to 0.1m of length data in terms of dimension. That is, for the maximum stock data and the last shipment data, 1L of the maximum stock data and the last shipment data corresponds to 0.1m at the production radius.
The corresponding proportions of the height and the radius are different (by ten times), so that the influence of the road distance is weakened as the warehouse cone frustum and the shop cone frustum are not too large, and timely data (reflecting on the height) can play a more decisive role in terms of the reaction property of the cone frustum.
By the scheme, the warehouse is constructed into a cone solid object in the visual three-dimensional model, and the solid object can reflect the warehousing attributes of the warehouse for a certain product, such as the height represents the residual inventory, the lower bottom represents the warehousing capacity, and the upper bottom represents the last-day delivery capacity.
Similarly, constructing a three-dimensional simulation model reflecting the warehouse, the shop and the road based on the two-dimensional coordinate data and the height data comprises the following steps: generating a two-dimensional coordinate central point of the warehouse according to the two-dimensional coordinate data of the warehouse; constructing a shop cone stand representative warehouse, wherein the central axis of the shop cone stand is vertical to the horizontal plane of the three-dimensional simulation model and passes through the two-dimensional coordinate center point of the warehouse, and the height of the shop cone stand is in direct proportion to the height data of the shop; collecting historical maximum purchasing quantity data of commodities in a shop; generating the lower bottom radius of the conical table of the shop according to the historical maximum purchasing quantity data; collecting historical minimum purchasing quantity data of commodities of a shop; and generating the upper bottom radius of the conical table of the shop according to the historical minimum purchasing quantity data.
The values of the height and the radius can refer to the scheme of converting the commodity volume to the length size in the scheme.
After the warehouse cone and the store cone described above are constructed, in order to realize the logistics simulation based on the hydrodynamic simulation, the height directions of the warehouse cone and the store cone are made different, that is, with respect to the horizontal plane of the three-dimensional simulation model, if the warehouse cone is formed upward of the horizontal plane, the store cone is formed downward of the horizontal plane. In colloquial terms, if the warehouse cone is a mountain, the shop cone is a basin. Of course, the warehouse cone and the shop cone are only relatively different, and when the analysis purposes are different, they can be inverted, that is, the whole three-dimensional simulation model can be horizontally turned. Depending on the purpose of logistics planning, if the store is taken as the initiating end for the purpose of observing and tactically going from the store to the warehouse, the cone of the store is taken as the "mountain" and the warehouse is taken as the "basin", and vice versa, the inversion is carried out. As shown in fig. 4, the warehouse cone and the store cone are solid in the three-dimensional simulation model when the "mountain is formed, and the warehouse cone and the store cone are concave in the three-dimensional simulation model when the" basin "is formed.
The method for constructing the three-dimensional simulation model reflecting the warehouse, the shop and the road based on the two-dimensional coordinate data and the height data comprises the following steps: and generating a diversion trench representing the road in the three-dimensional simulation model according to the trend of the road in the two-dimensional map data, wherein the cross section of the diversion trench is rectangular and has the same depth and width. Namely, the flow guide grooves are formed according to the trend of the original road, so that the flow guide grooves are connected with the warehouse cone frustum and the shop cone frustum. The reason that the depth and the width of the guide grooves are unified and the guide grooves are not different in the height direction is to highlight the route of the guide grooves reflecting the road and to homogenize other influence conditions.
Preferably, in the three-dimensional simulation model, the length of the diversion trench is the length of the original road, and the width and the depth of the diversion trench are both 1 m.
As a further preferred scheme, the diversion trench extends to the edge cutoff of the warehouse cone and the store cone, which is different from a general design concept, generally does not reflect the volume characteristics of the node in order to realize route planning, and the method omits the road when extending to the warehouse cone and the store cone in order to realize the route planning through hydrodynamic simulation and based on the storage and morning attributes of the node, and reflects the commercial attributes of the logistics node by the method which seems to be inaccurate, especially considers the logistics transportation and storage cost, so that the route planning is more in line with the purpose of the whole system logistics refinement operation.
In the prior art, logistics are planned according to the normal direction of logistics, namely, the mode from a warehouse to a shop. The application is a preferable scheme that a shop is used as the starting side of the logistics simulation to perform simulation and planning.
It should be noted that, although the three-dimensional simulation model is constructed in units of meters, it can be scaled in real operation and display.
Specifically, the step of planning the logistics path for the commodity order according to the three-dimensional position of the warehouse in the three-dimensional simulation model and the path formed by the roads between the warehouse and the shop comprises the following steps: generating transportation volume data according to data of one type of commodities in the purchase order; generating a simulated fluid of a corresponding volume in the three-dimensional simulation model according to the transportation volume data; and setting gravity parameters in the three-dimensional simulation model, and planning a logistics line according to the time and path of the simulation fluid flowing from the shop cone to the storage cone.
For example, if there are some beverage 3 boxes in the purchase order and the volume value is 3L, it can be known by collecting and calculating data, so that a 3L fluid is generated according to the volume value and the store cone of the store starts to move freely, and if no friction exists in the diversion trench, the time from the start of the fluid from the store cone to the final flow into the storage cone is used as a route selection standard, and when there are multiple routes from the store cone to the storage cone, multiple routes can be simulated respectively.
The gravity parameters in the three-dimensional simulation model are set to accelerate the calculation time.
Through the simulation method, simulation of the flowing process and acquisition of the picking address are carried out on all commodity SKUs in the purchase order of one store, and therefore a picking address list containing the picking addresses of all commodity SKUs in the purchase order of the store is generated. This list is shown in fig. 5 (for convenience of understanding, the pickup address, i.e., the warehouse address, is replaced with the supplier name system, and the delivery address is replaced with the store name, the same applies hereinafter). That is, a warehouse for each item SKU of store a is selected for supply by simulation.
Then, the pick address lists of all shops are gathered into a purchase relation table, and the purchase relation table comprises delivery addresses, pick addresses, purchase order numbers and commodity SKUs with corresponding relations. The purchase relationship table is shown in FIG. 6, which is a source of supply for the SKU items in the store purchase orders in all of the systems.
As shown in fig. 6, the picking addresses are combined according to the same picking address in the purchasing relation table to generate a supplying relation table, and the supplying relation table includes the picking address, the delivery address set, the picking SKU set, the purchasing order number set and the total transportation volume.
And analyzing whether the transportation volume of each column can generate a logistics order or not according to the supply relation table. Specifically, the method comprises the following steps:
judging whether the transportation volume corresponding to one goods taking address in the supply relation table is larger than a preset maximum transportation capacity volume threshold value or not; splitting the transport volume total if greater than the maximum capacity volume threshold.
Specifically, the maximum capacity threshold is the maximum volume of the vehicle that can be allocated by the system, and exceeding the maximum capacity threshold indicates exceeding the vehicle that can be allocated by the system, so the total volume of the transportation needs to be split. The splitting principle is that the transportation volumes of all related stores are sorted according to delivery addresses, then the transportation volumes of the smaller stores are preferentially split out to form a single column in a supply relation table, then whether the remaining transportation volumes left after splitting are larger than the maximum volume of the vehicle or not is judged, and if yes, splitting is carried out.
Alternatively, orders for larger stores may be split preferentially according to a maximum capacity volume threshold.
Then, in the following judgment step, if the transportation volume is smaller than the maximum transportation volume threshold value, whether the transportation volume corresponding to one goods taking address in the supply relation table is larger than the minimum transportation volume threshold value is judged.
If the minimum capacity volume threshold is exceeded, a delivery vehicle will be matched for a pick-up address in the supply relationship table.
If the total volume of the transportation volume is smaller than the minimum transportation volume threshold value, the total volume of the transportation volume is merged with the transportation volumes corresponding to other goods taking addresses.
Wherein the minimum transportation volume threshold corresponds to a minimum volume of the vehicle that the system can allocate, and is less than the maximum transportation volume threshold and greater than the minimum transportation volume threshold, it means that the transportation volume in the row of the supply relationship table can be directly allocated to the type of the suitable carrier vehicle, such as a minibus, an evaluo, a pickup truck, a van, etc.
When the transport volume of the row of the supply relationship table is less than the minimum capacity volume threshold, the transport volume of the row of the supply relationship table cannot fill the carrier vehicle with the minimum volume, and the capacity is wasted. At this point, such transport volumes need to be combined.
Specifically, the transportation volumes smaller than the minimum transportation capacity volume threshold value are merged (including the transportation volumes of the new row of the new supply relation table generated after splitting) in the same manner as the delivery addresses in the supply relation table, and the vehicle is allocated if the merged transportation volumes are smaller than the maximum transportation capacity volume threshold value and larger than the minimum transportation capacity volume threshold value.
If the volume threshold value less than the maximum capacity and greater than the minimum capacity, generally still less than the minimum capacity, cannot be met, the following scheme is adopted for processing.
The first scheme is that the logistics tasks corresponding to the transport capacity volumes generate order taking orders, the order taking orders are sent to clients of a certain range of carrier vehicles, and the orders are distributed in a carrier vehicle order taking mode.
The second solution is to match the unallocated shipping volume to the carrier vehicle of the generated logistics order based on the contained approximate (floating range is 90% to 110%) sub-shipping volume (shipping volume of a shipping address) of the generated logistics order, generate a midway logistics order, and go to the midway logistics order for pick-up and delivery after the vehicle completes the allocated sub-shipping volume.
Preferably, after the unallocated transportation volume is matched with the carrier vehicle of the logistics order, the intermediate logistics order is not generated immediately, but the logistics order of all the included approximate sub-transportation volumes is exhausted, all the navigation lines are generated, and the combination of the completed logistics order and the intermediate logistics order with the shortest distance is used as the final matching result.
Through the scheme, the logistics order can be generated according to the original transportation volume, the split transportation volume and the combined transportation volume; a matching carrier vehicle for each of said logistics orders.
In some embodiments, a logistics planning apparatus based on dynamic simulation comprises:
the model building module is used for building a three-dimensional simulation model corresponding to a commodity SKU, building a shop cone, a warehouse cone and a diversion trench in the three-dimensional simulation model to represent shops, warehouses and roads respectively, and the shop cone and the warehouse cone have opposite generating directions.
And the fluid generation module is used for generating a simulation fluid corresponding to a commodity SKU in the purchase order according to the purchase order of the shop.
And the fluid simulation module is used for simulating the flowing process of the simulated fluid which completely flows into the shop cones corresponding to all the shops within a preset range from the shop cone corresponding to the shop initiating the purchase order in the three-dimensional simulation model corresponding to the commodity SKU.
And the time recording module is used for recording all the flowing processes of one commodity SKU in one purchase order and extracting the completion time of the flowing processes.
And the address query module is used for querying the warehouse cone corresponding to the flow process with the shortest completion time and taking the position of the warehouse cone corresponding to the warehouse as the goods taking address of the commodity SKU in the purchase order.
As shown in fig. 8, an electronic device 800 may include a processing means (e.g., central processing unit, graphics processor, etc.) 801 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage means 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data necessary for the operation of the electronic apparatus 800 are also stored. The processing apparatus 801, the ROM802, and the RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
Generally, the following devices may be connected to the I/O interface 805: input devices 806 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.: output devices 807 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 808 including, for example, magnetic tape, hard disk, etc.: and a communication device 809. The communication means 809 may allow the electronic device 800 to communicate wirelessly or by wire with other devices to exchange data. While fig. 8 illustrates an electronic device 800 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 8 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through communications device 809, or installed from storage device 808, or installed from ROM 802. The computer program, when executed by the processing apparatus 801, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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.
In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (hypertext transfer protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be one contained in the electronic device: or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: constructing a three-dimensional simulation model corresponding to a commodity SKU, and respectively constructing a shop cone, a warehouse cone and a diversion trench in the three-dimensional simulation model to represent shops, warehouses and roads, wherein the shop cone and the warehouse cone have opposite generation directions; generating a simulated fluid corresponding to a commodity SKU in the purchase order according to the purchase order of the shop; simulating the flowing process of the simulated fluid which completely flows into the shop cones corresponding to all the shops within a preset range from the shop cone corresponding to the shop which initiates the purchase order in the three-dimensional simulation model corresponding to the commodity SKU; recording all the flow processes corresponding to a commodity SKU in a purchase order and extracting the completion time of the flow processes; and inquiring the warehouse cone table corresponding to the flow process with the shortest completion time, and taking the position of the warehouse cone table corresponding to the warehouse as the pickup address of the commodity SKU in the purchase order.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and including the conventional procedural programming languages: such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures.
For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. A logistics planning method is characterized in that:
the logistics planning method based on dynamic data updating comprises the following steps:
constructing a three-dimensional simulation model corresponding to a commodity SKU, and respectively constructing a shop cone, a warehouse cone and a diversion trench in the three-dimensional simulation model to represent shops, warehouses and roads, wherein the shop cone and the warehouse cone have opposite generation directions;
generating a simulated fluid corresponding to a commodity SKU in the purchase order according to the purchase order of the shop;
simulating the flowing process of the simulated fluid which completely flows into the shop cones corresponding to all the shops within a preset range from the shop cone corresponding to the shop which initiates the purchase order in the three-dimensional simulation model corresponding to the commodity SKU;
recording all the flow processes corresponding to a commodity SKU in a purchase order and extracting the completion time of the flow processes;
and inquiring the warehouse cone table corresponding to the flow process with the shortest completion time, and taking the position of the warehouse cone table corresponding to the warehouse as the pickup address of the commodity SKU in the purchase order.
2. The logistics planning method based on dynamic data update of claim 1, wherein:
the logistics planning method based on dynamic data updating further comprises the following steps:
and simulating the flowing process and acquiring the picking address for all the commodity SKUs in the purchase order of the shop so as to generate a picking address list containing the picking addresses of all the commodity SKUs in the purchase order of the shop.
3. The logistics planning method based on dynamic data update of claim 2, wherein:
the logistics planning method based on dynamic data updating further comprises the following steps:
and summarizing the goods taking address lists of all shops into a purchasing relation table, wherein the purchasing relation table comprises delivery addresses, goods taking addresses, purchasing order numbers and commodity SKUs with corresponding relations.
4. The logistics planning method based on dynamic data update of claim 3, wherein:
the logistics planning method based on dynamic data updating further comprises the following steps:
and merging the pickup addresses according to the same pickup addresses in the purchasing relation table to generate a supplying relation table, wherein the supplying relation table comprises pickup addresses, delivery address sets, pickup SKU sets, purchasing order number sets and total transportation volume amounts with corresponding relations.
5. The logistics planning method based on dynamic data update of claim 4, wherein:
the logistics planning method based on dynamic data updating further comprises the following steps:
judging whether the transportation volume corresponding to one goods taking address in the supply relation table is larger than a preset maximum transportation capacity volume threshold value or not;
splitting the transport volume total if greater than the maximum capacity volume threshold.
6. The logistics planning method based on dynamic data update of claim 5, wherein:
the logistics planning method based on dynamic data updating further comprises the following steps:
if the transportation volume is smaller than the maximum transportation volume threshold value, judging whether the transportation volume corresponding to one goods taking address in the supply relation table is larger than a minimum transportation volume threshold value or not;
if the minimum capacity volume threshold is greater than the minimum capacity volume threshold, matching a delivery address in the supply relationship table with a carrier vehicle;
and if the total volume of the transportation volume is smaller than the minimum transportation volume threshold value, merging the total volume of the transportation volume with the transportation volumes corresponding to other pickup addresses.
7. The logistics planning method based on dynamic data update of claim 6, wherein:
the logistics planning method based on dynamic data updating further comprises the following steps:
generating a logistics order according to the original transportation volume, the split transportation volume and the combined transportation volume;
a matching carrier vehicle for each of said logistics orders.
8. A logistics planning device based on dynamic simulation comprises:
the model building module is used for building a three-dimensional simulation model corresponding to a commodity SKU, building a shop cone, a warehouse cone and a diversion trench in the three-dimensional model to represent shops, warehouses and roads respectively, wherein the shop cone and the warehouse cone have opposite generating directions;
the fluid generation module is used for generating a simulation fluid corresponding to a commodity SKU in the purchase order according to the purchase order of the shop;
the fluid simulation module is used for simulating the flowing process of the simulated fluid which completely flows into the shop cones corresponding to all the shops within a preset range from the shop cone corresponding to the shop which initiates the purchase order in the three-dimensional simulation model corresponding to the commodity SKU;
the time recording module is used for recording all the flowing processes of one commodity SKU in one corresponding purchase order and extracting the completion time of the flowing processes;
and the address query module is used for querying the warehouse cone corresponding to the flow process with the shortest completion time and taking the position of the warehouse cone corresponding to the warehouse as the goods taking address of the commodity SKU in the purchase order.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the processors to implement the method of any one of claims 1 to 7.
10. A computer-readable medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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