CN116933477A - Simulation model construction method and device - Google Patents
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Abstract
The invention discloses a method and a device for constructing a simulation model, and relates to the technical field of warehouse logistics. One embodiment of the method comprises the following steps: obtaining structural information of a logistics network for constructing a simulation model, wherein the structural information indicates logistics nodes and lines contained in the logistics network; processing the structural information of the logistics network to generate line network information and logistics node layout information which accord with a data input model; generating a network simulation model containing logistics nodes based on the line network information; generating a node simulation model corresponding to the logistics node based on the logistics node layout information; and calling the network simulation model and/or the node simulation model to perform simulation optimization on the network simulation model and/or the node simulation model. The implementation mode realizes flexible decoupling and fusion of the network simulation model and the node simulation model corresponding to the logistics network, and improves the universality and expansibility of the simulation model.
Description
Technical Field
The invention relates to the technical field of warehouse logistics, in particular to a method and a device for constructing a simulation model.
Background
By establishing simulation models for actual storage, logistics networks and the like, reasonable evaluation can be performed on a logistics system planning scheme in advance, and a more optimized scheme is searched for floor implementation without implementation in an actual logistics system, so that floor risk can be reduced, experimental cost is reduced, and the simulation modeling technology has wide application prospect in logistics scenes.
In the process of realizing the invention, the inventor finds that at least the following problems exist in the existing simulation model construction technology: as long as any modeling object such as a logistics network and a logistics node has a change, the simulation model needs to be re-established, so that the development workload of the simulation model is large and the repeatability is high in the same scene, and the development of the simulation model is poor and cannot be used for other similar logistics systems; the simulation model can be built for the logistics network only under the condition of simplifying logistics nodes such as warehouses and stations, or the simulation model can be built for the logistics nodes independently, so that the internal information of the logistics nodes and the circuit network in the logistics network cannot be fused, and the accuracy of the simulation model is affected.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a method and a device for constructing a simulation model, which can construct a network simulation model and a node simulation model at the same time, so as to realize flexible fusion and decoupling of the network simulation model and the node simulation model, and improve the universality and expansibility of the simulation model.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method for constructing a simulation model, including:
obtaining structural information of a logistics network for constructing a simulation model, wherein the structural information indicates logistics nodes and lines contained in the logistics network;
processing the structural information of the logistics network to generate line network information and logistics node layout information which accord with a data input model;
generating a network simulation model containing the logistics node based on the line network information; generating a node simulation model corresponding to the logistics node based on the logistics node layout information;
and calling the network simulation model and/or the node simulation model to perform simulation optimization on the network simulation model and/or the node simulation model.
Optionally, the line network information indicates a node identifier, a node type, node position information and whether the node has an independent simulation identifier corresponding to the logistics node;
the logistics node layout information indicates a storage unit inside the logistics node and position information of the storage unit.
Optionally, the generating, based on the logistics node layout information, a node simulation model corresponding to the logistics node includes:
Judging whether the logistics node has an independent simulation identifier according to the line network information;
and under the condition that the logistics nodes have independent simulation identifications, generating a node simulation model corresponding to the logistics nodes.
Optionally, the data input model further indicates order information, resource efficiency information and parameter information applicable to the simulation model;
the order information indicates a generated logistics node, a target logistics node, an order placing time, article information contained in the order and a storage unit where the article is located, which correspond to the order;
the resource efficiency information indicates the resource type, the resource operation efficiency and the resource capacity;
the parameter information indicates the number of resources and the resource scheduling policy.
Optionally, the network simulation model and/or the node simulation model are subjected to simulation optimization by adjusting any one or more of the input order information, resource efficiency information and parameter information which conform to the data input model.
Optionally, in the process of performing simulation optimization on the network simulation model and/or the node simulation model, generating a simulation order based on at least one of the following modes:
Generating a simulation order based on the historical order;
and generating a simulation order based on the preset article class, article quantity and wave number information.
Optionally, the network simulation model and the node simulation model are generated by adopting any one of the following simulation platforms: flexSim, anyLogic, simio.
To achieve the above object, according to another aspect of an embodiment of the present invention, there is provided an apparatus for simulation modeling, including: the system comprises a structure information acquisition module, a structure information processing module, a simulation model construction module and a simulation model optimization module; wherein,
the structure information acquisition module is used for acquiring structure information of a logistics network for constructing a simulation model, wherein the structure information indicates logistics nodes and lines contained in the logistics network;
the structure information processing module is used for processing the structure information of the logistics network to generate line network information and logistics node layout information which accord with a data input model;
the simulation model construction module is used for generating a network simulation model containing the logistics nodes based on the line network information; generating a node simulation model corresponding to the logistics node based on the logistics node layout information;
The simulation model optimization module is used for calling the network simulation model and/or the node simulation model to perform simulation optimization on the network simulation model and/or the node simulation model.
Optionally, the line network information indicates a node identifier, a node type, node position information and whether the node has an independent simulation identifier corresponding to the logistics node;
the logistics node layout information indicates a storage unit inside the logistics node and position information of the storage unit.
Optionally, the generating, based on the logistics node layout information, a node simulation model corresponding to the logistics node includes:
judging whether the logistics node has an independent simulation identifier according to the line network information;
and under the condition that the logistics nodes have independent simulation identifications, generating a node simulation model corresponding to the logistics nodes.
Optionally, the data input model further indicates order information, resource efficiency information and parameter information applicable to the simulation model;
the order information indicates a generated logistics node, a target logistics node, an order placing time, article information contained in the order and a storage unit where the article is located, which correspond to the order;
The resource efficiency information indicates the resource type, the resource operation efficiency and the resource capacity;
the parameter information indicates the number of resources and the resource scheduling policy.
Optionally, the simulation model optimization module is configured to perform simulation optimization on the network simulation model and/or the node simulation model by adjusting any one or more of the input order information, resource efficiency information, and parameter information that conform to the data input model.
Optionally, in the process of performing simulation optimization on the network simulation model and/or the node simulation model, the simulation model optimization module is configured to generate a simulation order based on at least one of the following manners:
generating a simulation order based on the historical order;
and generating a simulation order based on the preset article class, article quantity and wave number information.
Optionally, the simulation model building module is configured to generate the network simulation model and the node simulation model by using any one of the following simulation platforms: flexSim, anyLogic, simio.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided an electronic device for constructing a simulation model, including: one or more processors; and a storage means for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement any of the methods of constructing a simulation model as described above.
To achieve the above object, according to still another aspect of the embodiments of the present invention, there is provided a computer-readable medium having stored thereon a computer program which, when executed by a processor, implements any one of the methods of constructing a simulation model as described above.
One embodiment of the above invention has the following advantages or benefits: the data structure or the data format adopted by the simulation model is uniformly built through the data input model, so that the standardization of simulation input data is realized, different simulation models can be built for different application scenes or the same scene only by adjusting the input data, and the universality and the expansibility of the simulation model building method are improved; meanwhile, a network simulation model and a node simulation model are respectively generated aiming at layout information of the logistics network and the logistics nodes, and flexible decoupling and fusion of the network simulation model and the node simulation model are realized by calling the network simulation model and/or the node simulation model, so that flexible decoupling and fusion of the circuit network and the logistics nodes in the logistics network are realized, and accuracy and applicability of the simulation model are improved.
Further effects of the above-described non-conventional alternatives are described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the main flow of a method of constructing a simulation model according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a network simulation model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a node simulation model in accordance with an embodiment of the present invention;
FIG. 4 is a schematic diagram of the main modules of a simulation model building apparatus according to an embodiment of the present invention;
FIG. 5 is an exemplary system architecture diagram in which embodiments of the present invention may be applied;
fig. 6 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of main flow of a method for constructing a network simulation model according to an embodiment of the present invention, and as shown in fig. 1, the method for constructing a simulation model may specifically include the following steps:
step S101, obtaining structural information of a logistics network for constructing a simulation model, wherein the structural information indicates logistics nodes and lines contained in the logistics network.
The logistics network refers to a network structure composed of a route for executing a logistics sport mission and a node for executing a logistics pause mission, and if a warehouse logistics network is taken as an example, the logistics nodes include but are not limited to a warehouse, a distribution center, a distribution site and the like.
Step S102, processing the structural information of the logistics network to generate circuit network information and logistics node layout information which accord with a data input model.
The data input model is a data structure preset according to actual requirements, so that unified standardized processing is carried out on the formats or structures of all data subjected to simulation modeling, and a foundation is laid for universality and expandability of the simulation model.
In an optional implementation manner, the line network information indicates a node identifier corresponding to the logistics node, a node type, node position information and whether the node has an independent simulation identifier. Specifically, as shown in the following Table 1,
Table 1 line network information example
Based on this, a network structure including a plurality of logistics nodes can be generated by a geographic information system (Geographic Information System or Geo-Information system, GIS), and navigation paths between logistics nodes can be calculated based on node position information of the logistics nodes, namely node longitude and node latitude.
In an alternative embodiment, the logistics node layout information indicates a storage unit inside the logistics node and location information of the storage unit. Specifically, taking a logistics node as a warehouse node for illustration, the warehouse node may define the data structures applicable to the two logistics node layout information shown in the following table 2 and table 3 according to whether the storage area is subjected to storage bit division.
Table 2 example of logistics node layout information—temporary storage class entity
| Field name | Meaning of field |
| Simulation scheme name | A simulation schema name identifies a particular transport network system |
| Node identification | Node name or node number |
| Temporary storage numbering | Unique identification of temporary entity |
| Temporary storage type | Sign temporary storage use |
| Temporary storage size | Length and width of temporary storage entity and maximum allowable stacking height |
| Temporary storage coordinates | Temporary storage of entity angular point coordinates |
The temporary storage entity refers to all entities or storage areas which have a storage function but do not divide storage, such as a receiving temporary storage area, a delivery temporary storage area, a express sorting buffer area, a temporary storage area before packaging and the like.
Table 3 example of logistics node layout information-storage class entity
| Field name | Meaning of field |
| Simulation scheme name | A simulation schema name identifies a particular transport network system |
| Node identification | Node name or node number |
| Region numbering | Unique identification of storage class entity region |
| Number of rows and columns | Storage class entities number of rows and columns within a storage domain |
| Spacing of | Storage class entities row spacing and column spacing within a storage domain |
| Coordinates of | Angular point coordinates of regions |
| Channel | Rows and columns of channels are recorded |
| Memory cell | Memory cell structure in a memory entity |
| Memory cell size | Length, width and maximum allowable stack height of a memory cell |
The storage type entities comprise all storage type entities for carrying out storage location division, such as a pallet ground pile area, various types of shelf areas and the like. Therefore, the abscissa information of the storage unit or the storage area in the simulation model can be determined based on the corner coordinates of the areas indicated by the two logistics node layout information of the temporary storage type entity and the position information such as the storage unit size, the row information, the column information, the row spacing, the column spacing and the like, and the node simulation model containing the node internal layout information is further generated.
In an alternative embodiment, the data input model further indicates order information, resource efficiency information, and parameter information to which the simulation model is applicable; the order information indicates a generated logistics node, a target logistics node, an order placing time, article information contained in the order and a storage unit where the article is located, which correspond to the order; the resource efficiency information indicates the resource type, the resource operation efficiency and the resource capacity; the parameter information indicates the number of resources and the resource scheduling policy. The details are shown in tables 4, 5 and 6 below.
Table 4 order information example
Therefore, the simulation model can be driven by normalized order information to simulate a real logistics network or logistics nodes to operate according to a certain logic or flow.
Table 5 resource efficiency information example
The resources refer to entities which are occupied by the simulation model for completing certain operations, are usually limited resources, and have certain operation efficiency, such as sorting personnel in warehouse operations, pallets for loading cargoes, transportation vehicles capable of being dispatched in a logistics network and the like.
Table 6 parameter information example
| Field name | Meaning of field |
| Simulation plan name | A simulation schema name identifies a particular transport network system |
| Parameter name | Unique name identification of parameters |
| Parameter value | Recording parameter values by adopting character string type for any type of parameters |
The parameters refer to key data which are relied on when the simulation system simulates, and can be instantiated according to actual requirements to simulate different logistics networks or logistics node operation flows, so that comparison and optimization of different schemes are performed, such as the number of sorting personnel, the number of transport vehicles, the number of trays, the vehicle departure time, the order interception time and the like.
In addition, other data required by constructing the simulation model can be standardized according to actual demands, for example, because the actual internal numbering rules of the logistics nodes are various, and further the numbers or the identifications corresponding to the storage units are also various, in order to ensure the universality of the simulation system, the storage units in the simulation model can be uniformly numbered by adopting the same set of numbering rules, and meanwhile, the corresponding relation between the actual numbers of the storage units and the logic numbers in the simulation model is recorded.
Step S103, generating a network simulation model containing the logistics nodes based on the line network information; and generating a node simulation model corresponding to the logistics node based on the logistics node layout information.
Specifically, the network simulation model and the node simulation model are generated by adopting any one of the following simulation platforms: flexSim, anyLogic, simio. In addition, other simulation platforms can be adopted, or Java or Python or other object-oriented programming languages can be directly used for constructing the simulation model.
For the network simulation model, the network simulation model can be divided into four modules for modeling based on an actual logistics operation scheduling link, namely a GIS map module, a vehicle calling module, a transportation flow module and a performance statistics module. Specifically, taking the case that the line network information indicates the warehouse a, the distribution centers B1 and B2 and the distribution sites C1, C2 and C2 as an example, the line network information can be input into FlexSim software to generate a three-dimensional logistics network including six nodes of the warehouse a, the distribution centers B1 and B2 and the distribution sites C1, C2 and C2, and each node has an independent simulation indicating the corresponding node number, the node type and whether the node can be separated from the line network line; secondly, invoking a GIS map module, and calculating navigation paths among nodes based on node longitudes and node dimensions corresponding to a warehouse A, distribution centers B1 and B2, distribution sites C1, C2 and the like; in order to simplify the simulation model and improve the simulation efficiency, the vehicle scheduling module is considered to be set as infinite resources for calling; for the transportation flow module, the cargo delivery is used as a trigger event, and the target logistics nodes such as the delivery site and the like for delivering the cargo are used as end events, and the specific flow is as follows:
(1) After the goods are delivered out of the warehouse, reading a target logistics node in the goods corresponding order, checking whether a vehicle is parked on a platform on the current line, if so, executing the following step (1), and if not, executing the following step (3).
(2) Loading cargoes and checking whether the vehicle is fully loaded or whether the latest departure time is reached; if yes, the following step (4) is executed, and if not, no operation is executed.
(3) And (3) calling the goods, judging whether an available platform exists at present, if so, stopping the vehicle at the platform, and if not, queuing the vehicle for waiting.
(4) The vehicle starts, the navigation route calculated by the GIS map module is obtained according to the target logistics node and the current logistics node or the starting node, the performance statistics module is called, the transportation time is calculated based on the movement speed of the vehicle, the transportation time is calculated, and the cargo delivery to the target logistics node event is triggered.
(5) And unloading the vehicle to the target logistics node, and ending the process.
For the node simulation model, the node simulation model can be divided into four modules for modeling according to the in-bin operation flow, namely a layout control module, an order processing module, an operation logic module and a performance statistics module. Taking the logistics node layout information as an example for indicating a temporary storage area A1 before packaging of one temporary storage entity, namely a goods shelf B1 and a goods shelf B2, the logistics node layout information can be input into FlexSim software; secondly, calling a layout control module, and determining the abscissa information of a storage unit or a storage area in a simulation model according to the corner coordinates corresponding to the temporary storage area A1, the goods shelf B1 and the goods shelf B2 before packaging and the position information such as the size of the storage unit, the row information, the row interval and the like, so as to generate a three-dimensional node simulation model containing entities such as A1, B1 and B2; calling an order processing module to obtain a simulation order; invoking an operation logic module, setting an in-bin operation mode including but not limited to a seeding mode, a fruit picking mode, a partition mode and the like, and processing a simulation order based on the set operation mode; and calling a performance statistics module to calculate the operation time consumed in the simulation order processing process and the like.
More specifically, taking the set in-bin operation mode as a seeding mode as an example, the in-bin operation flow is as follows:
(1) And summarizing the simulation orders according to the SKU classification.
(2) Under each SKU, the simulated order is split into a number of job orders according to the tray volume.
(3) Each job ticket requests to be distributed to a sorting person, and after the request is successful, the sorting person starts sowing.
(4) The sorting personnel selects the receiving temporary storage area to pick up goods, and the time is multiplied and accumulated according to the number of pieces and the unit operation time delay.
(5) Before sowing the sorting personnel into the next storage unit, the sorting personnel firstly judges whether the storage unit has a space capable of working (avoiding overcrowding of working space), if not, the sorting personnel waits in situ, and if so, the sorting personnel moves to the corresponding storage unit.
(6) After the sorting personnel arrive at the storage unit, searching whether a tray for unloading the commodity exists or not, if not, triggering a support supplementing task to be executed for the delivery personnel, and waiting in situ. It is noted that when searching the tray, judging whether the tray meets the condition, preferentially searching the tray with the type matched with the current type of the tray, if not, using the empty tray, setting the type of the tray according to the first acquisition type placed on the tray, and if not, repairing the tray.
(7) After the sorting personnel find the tray, the goods begin to be unloaded, the unloading time is determined by the product of the unloading quantity and the unit operation time delay, and the unit operation time delay is added with an extension coefficient along with the increase of the number of simultaneous sowing people in the storage unit.
(8) Two determinations are made for each item unloaded: judging whether the current tray is full, if so, triggering the plate-beating operation of the current tray to execute the following step (9); and (3) judging whether all orders of the current storage unit in the wave are completed, if yes, triggering the board-beating operation by all non-empty trays of the storage unit operation so as to execute the following step (9).
(9) And the pallet to be plated requests a plate-punching operator, and the pallet is carried to a delivery area after the plate-punching operator requests the plate-punching operator successfully.
(10) Personnel walking line: and calling a navigation algorithm to plan a personnel walking path. It should be noted that, in order to avoid a deadlock that may be caused when a plurality of moving obstacles walk to the same point at the same time, it is preferable to set only a logic for avoiding fixed obstacles, and not to execute a collision avoidance logic between moving obstacles.
In an optional implementation manner, the generating, based on the logistics node layout information, a node simulation model corresponding to the logistics node includes: judging whether the logistics node has an independent simulation identifier according to the line network information; and under the condition that the logistics nodes have independent simulation identifications, generating a node simulation model corresponding to the logistics nodes.
For example, if the logistics network includes A, B, C, D four logistics nodes, and the corresponding line network information indicates that the logistics node A, B has an independent simulation identifier, the corresponding node simulation model a and node simulation model b may be generated based on the node layout information of the logistics node A, B. Therefore, the logistics nodes for constructing the independent simulation model can be flexibly selected through the independent simulation identification, and flexible decoupling and fusion of the network simulation model and the node simulation model can be realized.
It can be understood that under the condition that the internal layout of the logistics node C needs to be optimized, the logistics node C simulation model can be built without repeating the building of the logistics network and the simulation models of other logistics nodes by adjusting the independent simulation identifications corresponding to the logistics node C in the line network information on the basis of the built network simulation model, the node simulation model a and the node simulation model b. Similarly, under the condition that the line of the logistics network has variation, the simulation model can be directly built only for the adjusted line network, and the built node simulation model in the line network is continuously and repeatedly utilized, so that repeated development of the node simulation model corresponding to the same logistics node is avoided, and the construction efficiency of the simulation model is improved.
Step S104, the network simulation model and/or the node simulation model are/is called to carry out simulation optimization on the network simulation model and/or the node simulation model.
Specifically, taking the network simulation model for the logistics network and the node simulation model a and the node simulation model B of the logistics node A, B as examples, the network simulation model can be independently called to perform simulation optimization on the number of transportation vehicles, the dispatching strategy of the transportation vehicles, the transportation route and the like, the node simulation model a or the node simulation model B can be independently called to perform simulation optimization on the number of sorting personnel, the operation model, the operation route and the like in the logistics node A or the logistics node B, and meanwhile, at least one of the network simulation model a and the node simulation model B can be simultaneously called to perform simulation optimization on the transportation route, the in-cabin operation mode and the like. Therefore, flexible decoupling and integration of the network simulation model and the node simulation model are realized, namely, integration and decoupling of the internal layout information of the line network and the logistics nodes in the logistics network are realized, and the accuracy and the applicability of the simulation model are ensured.
In an alternative embodiment, the network simulation model and/or the node simulation model is subjected to simulation optimization by adjusting any one or more of the input order information, resource efficiency information and parameter information which conform to the data input model.
Specifically, referring to the network simulation model 200 shown in fig. 2, it includes: the system comprises a GIS map module 201, a vehicle calling module 202, a transportation flow module 203 and a performance statistics module 204; the GIS map module 201 is used for controlling a route or a navigation path between logistics nodes; the vehicle calling module 202 is used for dispatching vehicles for logistics transportation among logistics nodes; a transportation flow module 203 for controlling circulation of orders and even transportation vehicles in the logistics system; the performance statistics module 204 is configured to calculate performance indicators such as a duration of the operation based on the corresponding resource efficiency information such as vehicles and the number of vehicles indicated by the parameter information.
On this basis, the vehicle calling module 202 can directly adopt the adjusted vehicle number, the latest departure time of the vehicles to schedule the vehicles by adjusting the parameter information such as the vehicle number, the latest departure time of the vehicles and the like input into the network simulation model, and perform the running operation according to the operation logic of the transportation flow module 203, so as to trigger the performance statistics module 204 to calculate the performance indexes such as the preset transportation duration, the average transportation duration and the like based on the statistics data such as the time consumption of the operation, and further optimize the logistics network according to the performance indexes. In addition, different navigation paths can be adopted based on the GIS map module 201, so that the navigation paths can be optimized and the like; based on the vehicle calling module 202 adopting different vehicle scheduling strategies, the optimization of the supervehicle scheduling strategy is realized. That is, by adjusting the input data on which each module of the network simulation model depends, the network simulation model can be optimized in a multi-dimensional manner such as transportation line and vehicle dispatching.
Further, referring to the node simulation model 300 shown in fig. 3, it includes: layout control module 301, order processing module 302, job logic module 303, performance statistics module 304; the layout control module 304 is configured to control a layout inside the logistics node; the order processing module is used for acquiring a simulation order; a job logic module 303 for controlling a job mode of the simulation model; the performance statistics module 304 is configured to statistically calculate a preset performance indicator. Wherein the operation mode comprises any one or a combination of a plurality of modes of a sowing mode, a fruit picking mode and a partitioning mode; the preset performance indicators include, but are not limited to, personnel utilization, personnel accumulation job duration, personnel accumulation travel distance, personnel average travel distance, SKU job time, personnel inefficiency time, order age, etc.
On the basis, the operation efficiency of different simulation orders for placing logistics nodes can be simulated by adjusting the simulation orders input to the order processing module 302 in the node simulation model 300; optimizing sorting modes and the like by adjusting the number of sorting personnel, the operation mode, the sorting path and the like input to the operation logic module; in addition, the layout control module 301 can adjust the layout information in the logistics node adopted by the simulation model, so as to optimize the layout scheme in the logistics node. That is, by adjusting the input data on which each module of the node simulation model depends, multidimensional optimization of layout information, operation modes, picking paths, resource configuration and the like in the node is realized.
In an alternative embodiment, in performing simulation optimization on the network simulation model and/or the node simulation model, a simulation order is generated based on at least one of the following modes: generating a simulation order based on the historical order; and generating a simulation order based on the preset article class, article quantity and wave number information.
Based on the embodiment, the data structure or the data format adopted by the simulation model is uniformly built through the data input model, so that the standardization of simulation input data is realized, different simulation models can be built for different application scenes or the same scene only by adjusting the input data, and the universality and the expansibility of the simulation model building method are improved; meanwhile, a network simulation model and a node simulation model are respectively generated aiming at layout information of the logistics network and the logistics nodes, and flexible decoupling and fusion of the network simulation model and the node simulation model are realized by calling the network simulation model and/or the node simulation model, so that flexible decoupling and fusion of the circuit network and the logistics nodes in the logistics network are realized, and accuracy and applicability of the simulation model are improved.
Referring to fig. 4, on the basis of the above embodiment, an embodiment of the present invention provides a device 400 for constructing a simulation model, including: a structure information acquisition module 401, a structure information processing module 402, a simulation model construction module 403 and a simulation model optimization module 404; wherein,
The structure information obtaining module 401 is configured to obtain structure information of a logistics network for constructing a simulation model, where the structure information indicates logistics nodes and lines included in the logistics network;
the structure information processing module 402 is configured to process the structure information of the logistics network to generate line network information and logistics node layout information that conform to a data input model;
the simulation model building module 403 is configured to generate a network simulation model including the logistics node based on the line network information; generating a node simulation model corresponding to the logistics node based on the logistics node layout information;
the simulation model optimization module 404 is configured to invoke the network simulation model and/or the node simulation model to perform simulation optimization on the network simulation model and/or the node simulation model.
In an optional implementation manner, the line network information indicates a node identifier, a node type, node position information and whether the node has an independent simulation identifier corresponding to the logistics node;
the logistics node layout information indicates a storage unit inside the logistics node and position information of the storage unit.
In an optional implementation manner, the generating, based on the logistics node layout information, a node simulation model corresponding to the logistics node includes:
judging whether the logistics node has an independent simulation identifier according to the line network information;
and under the condition that the logistics nodes have independent simulation identifications, generating a node simulation model corresponding to the logistics nodes.
In an alternative embodiment, the data input model further indicates order information, resource efficiency information, and parameter information to which the simulation model is applicable;
the order information indicates a generated logistics node, a target logistics node, an order placing time, article information contained in the order and a storage unit where the article is located, which correspond to the order;
the resource efficiency information indicates the resource type, the resource operation efficiency and the resource capacity;
the parameter information indicates the number of resources and the resource scheduling policy.
In an alternative embodiment, the simulation model optimization module 404 is configured to perform simulation optimization on the network simulation model and/or the node simulation model by adjusting any one or more of the order information, the resource efficiency information, and the parameter information that are input to conform to the data input model.
In an alternative embodiment, in performing a simulation optimization on the network simulation model and/or the node simulation model, the simulation model optimization module 404 is configured to generate a simulation order based on at least one of the following:
generating a simulation order based on the historical order;
and generating a simulation order based on the preset article class, article quantity and wave number information.
In an optional implementation manner, the simulation model building module is configured to generate the network simulation model and the node simulation model by using any one of the following simulation platforms: flexSim, anyLogic, simio.
FIG. 5 illustrates an exemplary system architecture 500 of a simulation model building method or simulation model building apparatus to which embodiments of the present invention may be applied.
As shown in fig. 5, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505. The network 504 is used as a medium to provide communication links between the terminal devices 501, 502, 503 and the server 505. The network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with the server 505 via the network 504 using the terminal devices 501, 502, 503 to receive or send messages or the like. Various communication client applications, such as a web browser application, etc., may be installed on the terminal devices 501, 502, 503.
The terminal devices 501, 502, 503 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 505 may be a server providing various services, such as a background management server providing support for shopping-type websites browsed by the user using the terminal devices 501, 502, 503. The background management server can analyze and other data such as the received product information inquiry request and the like, and feed back the optimization result of the processing result simulation model to the terminal equipment.
It should be noted that, the method for constructing a simulation model according to the embodiment of the present invention is generally executed by the server 505, and accordingly, the apparatus for constructing a simulation model is generally disposed in the server 505.
It should be understood that the number of terminal devices, networks and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 6, there is illustrated a schematic diagram of a computer system 600 suitable for use in implementing an embodiment of the present invention. The terminal device shown in fig. 6 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, 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 shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 601.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 the context of this document, 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 the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. 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 or flowchart illustration, and combinations of blocks in the block diagrams 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 modules involved in the embodiments of the present invention may be implemented in software or in hardware. The described modules may also be provided in a processor, for example, as: the processor comprises a structure information acquisition module, a structure information processing module, a simulation model construction module and a simulation model optimization module. The names of these modules do not constitute a limitation on the module itself in some cases, and for example, the transmitting unit may also be described as "a module that transmits a picture acquisition request to a connected server".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include: obtaining structural information of a logistics network for constructing a simulation model, wherein the structural information indicates logistics nodes and lines contained in the logistics network; processing the structural information of the logistics network to generate line network information and logistics node layout information which accord with a data input model; generating a network simulation model containing the logistics node based on the line network information; generating a node simulation model corresponding to the logistics node based on the logistics node layout information; and calling the network simulation model and/or the node simulation model to perform simulation optimization.
According to the technical scheme of the embodiment of the invention, the data structure or the data format adopted by the simulation model is uniformly built through the data input model, the standardization of the simulation input data is realized, different simulation models can be built for different application scenes or the same scene only by adjusting the input data, and the universality and the expansibility of the simulation model building method are improved; meanwhile, a network simulation model and a node simulation model are respectively generated aiming at layout information of the logistics network and the logistics nodes, and flexible decoupling and fusion of the network simulation model and the node simulation model are realized by calling the network simulation model and/or the node simulation model, so that flexible decoupling and fusion of the circuit network and the logistics nodes in the logistics network are realized, and accuracy and applicability of the simulation model are improved.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. The method for constructing the simulation model is characterized by comprising the following steps of:
obtaining structural information of a logistics network for constructing a simulation model, wherein the structural information indicates logistics nodes and lines contained in the logistics network;
processing the structural information of the logistics network to generate line network information and logistics node layout information which accord with a data input model;
generating a network simulation model containing the logistics node based on the line network information; generating a node simulation model corresponding to the logistics node based on the logistics node layout information;
and calling the network simulation model and/or the node simulation model to perform simulation optimization on the network simulation model and/or the node simulation model.
2. The method for constructing a simulation model according to claim 1, wherein,
the line network information indicates node identification, node type, node position information and whether the nodes have independent simulation identifications corresponding to the logistics nodes;
the logistics node layout information indicates a storage unit inside the logistics node and position information of the storage unit.
3. The method for constructing a simulation model according to claim 2, wherein generating the node simulation model corresponding to the logistics node based on the logistics node layout information includes:
judging whether the logistics node has an independent simulation identifier according to the line network information;
and under the condition that the logistics nodes have independent simulation identifications, generating a node simulation model corresponding to the logistics nodes.
4. The method for constructing a simulation model according to claim 2, wherein,
the data input model also indicates order information, resource efficiency information and parameter information applicable to the simulation model;
the order information indicates a generated logistics node, a target logistics node, an order placing time, article information contained in the order and a storage unit where the article is located, which correspond to the order;
The resource efficiency information indicates the resource type, the resource operation efficiency and the resource capacity;
the parameter information indicates the number of resources and the resource scheduling policy.
5. The method for constructing a simulation model according to claim 4, wherein,
and performing simulation optimization on the network simulation model and/or the node simulation model by adjusting any one or more of the input order information, resource efficiency information and parameter information which accord with the data input model.
6. The method for constructing a simulation model according to claim 5, wherein,
in the process of performing simulation optimization on the network simulation model and/or the node simulation model, generating a simulation order based on at least one of the following modes:
generating a simulation order based on the historical order;
and generating a simulation order based on the preset article class, article quantity and wave number information.
7. The method for constructing a simulation model according to claim 1, wherein,
generating the network simulation model and the node simulation model by adopting any one of the following simulation platforms: flexSim, anyLogic, simio.
8. A simulation model constructing apparatus, comprising: the system comprises a structure information acquisition module, a structure information processing module, a simulation model construction module and a simulation model optimization module; wherein,
The structure information acquisition module is used for acquiring structure information of a logistics network for constructing a simulation model, wherein the structure information indicates logistics nodes and lines contained in the logistics network;
the structure information processing module is used for processing the structure information of the logistics network to generate line network information and logistics node layout information which accord with a data input model;
the simulation model construction module is used for generating a network simulation model containing the logistics nodes based on the line network information; generating a node simulation model corresponding to the logistics node based on the logistics node layout information;
the simulation model optimization module is used for calling the network simulation model and/or the node simulation model to perform simulation optimization on the network simulation model and/or the node simulation model.
9. An electronic device for constructing a simulation model, comprising:
one or more processors;
storage means for storing one or more programs,
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-7.
10. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-7.
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