CN113259144A - Storage network planning method and device - Google Patents
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Abstract
The invention discloses a method and a device for planning a warehouse network, and relates to the technical field of warehouse logistics. One embodiment of the method comprises: acquiring warehousing network data; constructing a planning model according to the warehousing network data, the transportation amount of each type of article from a warehouse to a station and the constraint conditions; and solving the planning model to obtain target transportation amount, and planning the warehousing network according to the target transportation amount. The implementation mode improves the planning effect and the operation efficiency of the warehousing network.
Description
Technical Field
The invention relates to the technical field of warehouse logistics, in particular to a method and a device for planning a warehouse network.
Background
In order to improve the logistics speed and improve the user experience, more and more logistics companies or e-commerce companies begin to arrange a warehousing network consisting of different nodes such as warehouses, sorting centers and stations.
The warehouse of different network levels in the warehousing network realizes the delivery of the goods to each station through the cooperative support. For example, the goods in the low-level warehouse (such as the front warehouse) are usually delivered by the high-level warehouse (such as the central warehouse) through the inter-warehouse distribution transportation mode, and the demands of the sites covered by the low-level warehouse are cooperatively delivered by the multi-level network warehouse according to the goods inventory.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
the existing planning method depends on experience of service personnel for planning and layout, and has the problems of poor planning effect and low operation efficiency.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for planning a storage network, which can significantly improve the planning effect and the operation efficiency of the storage network.
To achieve the above object, according to a first aspect of the embodiments of the present invention, there is provided a method for planning a storage network, including:
acquiring warehousing network data;
constructing a planning model according to the warehousing network data, the transportation amount of each type of article from a warehouse to a station and the constraint conditions;
and solving the planning model to obtain target transportation amount, and planning the warehousing network according to the target transportation amount.
Further, the warehousing network data comprises a task order performance cost, a performance time type proportion and an item class priority, wherein the task order performance cost comprises the average cost and the internal transportation cost of each item for transporting from the warehouse to the site.
Further, the warehousing network data further comprises routing data of each article transported from the warehouse to the station, the task order performance cost further comprises warehouse-out transportation cost and warehouse-out sorting cost, and the average cost of each article transported from the warehouse to the station is determined according to the sorting times in the routing node sequence indicated by the routing data, the distance between every two nodes, the warehouse-out transportation cost and the warehouse-out sorting cost.
Further, the planning model comprises a cost optimization sub-model, a performance time-efficiency optimization sub-model and a quality optimization sub-model, and the warehousing network planning model further comprises: a weight coefficient is configured for each submodel.
Further, the step of constructing the planning model according to the warehousing network data, the transportation amount of each type of article from the warehouse to the station and the constraint conditions comprises the following steps: constructing a cost optimization sub-model according to the performance cost of the mission order, the transportation amount of each article from the warehouse to the station and the constraint condition; constructing a performance timeliness optimization sub-model according to the performance timeliness type proportion, the transportation amount of each type of article from the warehouse to the station and the constraint condition; and constructing a grade optimization sub-model according to the grade priority, the transportation amount of each grade of article from the warehouse to the station and the constraint conditions.
Further, the constraints include: demand constraint, ex-warehouse quantity constraint and class proportion constraint.
Further, the step of planning the warehouse network according to the target traffic volume comprises: and planning at least one of the class layout of the warehousing network, the site coverage and the task single amount distribution of the central bin and the front bin according to the target traffic.
According to a second aspect of the embodiments of the present invention, there is provided a storage network planning apparatus, including:
the warehousing network data acquisition module is used for acquiring warehousing network data;
the planning model building module is used for building a planning model according to the warehousing network data, the transportation amount of each type of article from the warehouse to the station and the constraint conditions;
and the planning module is used for solving the planning model to obtain target transportation amount and planning the warehousing network according to the target transportation amount.
According to a third aspect of embodiments of the present invention, there is provided an electronic apparatus, including:
one or more processors;
a storage device for storing one or more programs,
when executed by one or more processors, cause the one or more processors to implement any of the warehouse network planning methods described above.
According to a fourth aspect of embodiments of the present invention, there is provided a computer-readable medium, on which a computer program is stored, which when executed by a processor, implements any one of the warehouse network planning methods described above.
One embodiment of the above invention has the following advantages or benefits: the warehouse network data is acquired; constructing a planning model according to the warehousing network data, the transportation amount of each type of article from a warehouse to a station and the constraint conditions; the technical means of obtaining the target transportation amount based on solving the planning model and planning the warehousing network according to the target transportation amount overcomes the technical problems of poor planning effect and low operation efficiency of the existing planning method due to the fact that planning layout is performed by relying on experience of service personnel, and further achieves the technical effect of remarkably improving the planning effect and the operation efficiency of the warehousing network.
Further effects of the above-mentioned non-conventional alternatives will be 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 a main flow of a warehousing network planning method according to a first embodiment of the present invention;
fig. 2a is a schematic diagram of a main flow of a warehousing network planning method according to a second embodiment of the present invention;
FIG. 2b is a schematic illustration of the cost of transporting an item from a warehouse to a site according to the method of FIG. 2 a;
fig. 3 is a schematic diagram of main modules of a warehousing network planning device according to an embodiment of the present invention;
FIG. 4 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 5 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as 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 a main flow of a warehousing network planning method according to a first embodiment of the present invention; as shown in fig. 1, the warehousing network planning method provided by the embodiment of the present invention mainly includes:
and step S101, acquiring warehousing network data.
Specifically, according to the embodiment of the present invention, the warehousing network data includes a task order performance cost, a performance time type ratio, and a class priority, wherein the task order performance cost includes an average cost and an internal transportation cost of each item transported from the warehouse to the station.
Cost of performing the mission order: refers to the cost required to complete a fulfillment service that transports items from the warehouse to the site.
The type proportion of the performing aging is as follows: the method refers to the time efficiency type ratio corresponding to the completion of the fulfillment services of all articles transported from a warehouse to a station, and generally, the fulfillment time efficiency types are divided into current-day arrivals, next-day arrivals and multi-day arrivals. Specifically, according to the embodiment of the present invention, the proportion of each performance age type of an item transported from warehouse j to site k may be determined according to the performance age tags indicated in the historical order data.
Class priority: the sales rate of each type of article in all stations is determined as a priority, and the higher the sales rate is, the higher the corresponding priority of the type of article is. Specifically, according to the embodiment of the invention, the sales volume proportion of each item in the station can be determined according to the historical task list data, and the item priority of each item can be determined according to the sales volume proportion.
Specifically, according to the embodiment of the present invention, the warehousing network data further includes a warehouse capacity relaxation coefficient corresponding to each warehouse and historical sales data of each type of article. The nodes in the warehousing network comprise a central warehouse, a front warehouse and a site. In particular, a sorting center may also be included.
Through the arrangement, historical service data (namely the warehousing network data) are obtained, the problems of poor planning effect and the like caused by the fact that corresponding warehousing network planning is carried out depending on experience of service personnel are solved, meanwhile, the optimized result and the planning result which are more fit with service practice can be obtained by combining the historical service data, and the planned warehousing network has good landing performance.
According to a specific implementation manner of the embodiment of the present invention, the job ticket performing cost, the performing aging type ratio, the category priority, the bin slack coefficient corresponding to each warehouse, and the historical sales data of each category are obtained by integrating and calculating the data of the stock quantity of each category in the warehouse, the coordinate position of each node in the warehouse network, the historical job ticket data, and the like.
Further, according to the embodiment of the present invention, the warehousing network data further includes routing data of each item transported from the warehouse to the station, the task order performance cost further includes an internal transportation cost, an ex-warehouse transportation cost and an ex-warehouse sorting cost, and an average cost of each item transported from the warehouse to the station is determined according to the sorting times in the routing node sequence indicated by the routing data, the distance between every two nodes, the internal transportation cost, the ex-warehouse transportation cost and the ex-warehouse sorting cost.
For the whole warehousing network, the cost corresponding to the transportation of the articles from the warehouse to the station not only comprises the ex-warehouse delivery cost of the front warehouse to the station, but also comprises the internal distribution transportation cost generated by the central warehouse to carry the articles internally to the front warehouse.
Specifically, according to a specific implementation manner of the embodiment of the invention, the internal transportation cost is determined according to the internal quantity and the internal transportation cost parameter corresponding to each article in the warehouse.
And S102, constructing a planning model according to the warehousing network data, the transportation amount of each type of article from the warehouse to the station and the constraint conditions.
According to the embodiment of the invention, the planning model comprises a cost optimization sub-model, a performance time-efficiency optimization sub-model and a quality optimization sub-model, and the warehousing network planning model further comprises: a weight coefficient is configured for each submodel.
Although the warehousing network constructed only by a single target (such as the cost optimization submodel with cost optimization as a target) can achieve optimal cost control, the operation cost of the whole warehousing network is reduced to the minimum, a larger optimization space is provided in the aspects of utilization efficiency, user experience and the like, and therefore, by setting a plurality of optimization targets and setting a corresponding weight coefficient for each optimization target, the warehousing network planned by an optimization result can not only reduce the operation cost and improve the operation efficiency, but also remarkably improve the user experience. According to the emphasis points of the warehousing network services in different regions, different weight coefficients can be set for different optimization targets (namely, sub-models), for example, if the cost is optimized to be the first target, a higher weight coefficient can be set for the cost optimization sub-model, and a slightly lower weight coefficient can be set for the fulfillment time-efficiency optimization sub-model and the quality optimization sub-model.
Further, according to an embodiment of the present invention, the step of constructing the planning model according to the warehousing network data, the transportation amount of each item from the warehouse to the station, and the constraint condition includes: constructing a cost optimization sub-model according to the performance cost of the mission order, the transportation amount of each article from the warehouse to the station and the constraint condition; constructing a performance timeliness optimization sub-model according to the performance timeliness type proportion, the transportation amount of each type of article from the warehouse to the station and the constraint condition; and constructing a grade optimization sub-model according to the grade priority, the transportation amount of each grade of article from the warehouse to the station and the constraint conditions. Through the arrangement, the planning model is constructed according to the performance cost, the performance timeliness type proportion, the quality priority, the transportation volume and corresponding constraint conditions, so that the planning model can achieve the purposes of reducing the cost and improving the efficiency.
According to an embodiment of the present invention, the constraint conditions include: demand constraint, ex-warehouse quantity constraint and class proportion constraint.
According to the embodiment of the present invention, the warehouse network planning method further includes: and determining the demand of each station for each type of articles according to the task list data.
And (3) restricting the demand: the category representing the items transported to the station by each warehouse and the corresponding number of the categories should be consistent with the demand of the station for the items, so as to ensure the supply and demand balance.
According to the embodiment of the present invention, the warehouse network planning method further includes: and determining the maximum ex-warehouse quantity of each warehouse according to the historical task list data, and determining the maximum historical ex-warehouse quantity corresponding to each warehouse according to the warehouse capacity relaxation coefficient and the maximum ex-warehouse quantity corresponding to each warehouse. The corresponding bin capacity relaxation coefficient of each warehouse is a value which is slightly larger than 1, and the aim is to slightly improve the upper limit of the warehouse delivery amount so as to enable more types or quantities of articles to be deployed in the warehouse. It should be noted that the bin capacity relaxation factor needs to be set according to actual conditions, and in general, the maximum delivery amount of each warehouse does not reach the capacity limit of the warehouse, and therefore, the value may be set to be slightly larger than 1.
And (4) ex-warehouse quantity constraint: the total delivery quantity of all the products of each warehouse is not more than the maximum historical delivery quantity corresponding to the warehouse.
According to the embodiment of the present invention, the warehouse network planning method further includes: and determining the stock proportion of the various articles in each warehouse according to the stock quantity of the various articles in the warehouse, the historical task list data and the historical sales data of the various articles. According to a specific implementation of the embodiment of the present invention, only the stock proportion of each item in the central warehouse can be determined.
And (4) restricting the proportion of categories: the proportion representing the optimized delivery of each item type within a warehouse should be consistent with the proportion of that item type in each warehouse.
Through the arrangement, the optimization result can be ensured to meet the layout strategy of the current warehouse goods as much as possible. Under the constraint, the warehouse network can be planned to have better landing performance aiming at optimizing the warehouse-out quantity.
And S103, solving the planning model to obtain target transportation amount, and planning the warehousing network according to the target transportation amount.
Specifically, according to the embodiment of the present invention, the planning model may be solved by calling a solver, so as to obtain a decision variable (target traffic) under the condition of optimizing the target optimal solution, and further plan the warehouse network according to the target traffic.
According to an embodiment of the present invention, the step of planning the warehouse network according to the target traffic volume includes: and planning at least one of the class layout of the warehousing network, the site coverage and the task single amount distribution of the central bin and the front bin according to the target traffic.
Specifically, according to the embodiment of the present invention, the central warehouse and the front warehouse only represent two-layer networks with support relationship in a macroscopic view of the warehousing network mentioned in the present invention, and in a practical situation, the central warehouse may include a primary central warehouse, a secondary central warehouse, and the like; the front warehouse may include a primary front warehouse, a secondary front warehouse, and the like.
According to the technical scheme of the embodiment of the invention, the warehousing network data is acquired; constructing a planning model according to the warehousing network data, the transportation amount of each type of article from a warehouse to a station and the constraint conditions; the technical means of obtaining the target transportation amount based on solving the planning model and planning the warehousing network according to the target transportation amount overcomes the technical problems of poor planning effect and low operation efficiency of the existing planning method due to the fact that planning layout is performed by relying on experience of service personnel, and further achieves the technical effect of remarkably improving the planning effect and the operation efficiency of the warehousing network.
Fig. 2a is a schematic diagram of a main flow of a warehousing network planning method according to a second embodiment of the present invention; as shown in fig. 2a, the warehousing network planning method provided by the embodiment of the present invention mainly includes:
step S201, the data of the storage network is obtained, including the stock of each article in the storage, the coordinate position of each node in the storage network and the historical task list data.
Through the arrangement, the warehousing network data are acquired, the problems that planning effect is poor and the like caused by the fact that corresponding warehousing network planning is carried out depending on experience of business personnel are avoided, meanwhile, the optimization result and the planning result which are more fit with business reality can be obtained by combining the warehousing network data, and the planned warehousing network has good landing performance.
Step S202, determining the task order performance cost, the performance timeliness type proportion and the class priority according to the storage network data, wherein the task order performance cost comprises the average cost and the internal distribution transportation cost of each article transported from the warehouse to the station.
Specifically, according to the embodiment of the present invention, the warehousing network data further includes a bin capacity relaxation coefficient corresponding to each warehouse and historical sales data of each category of articles. And integrating and calculating the data of the inventory quantity of various articles in the warehouse, the coordinate position of each node in the warehouse network, historical mission sheet data and the like indicated in the warehouse network data to obtain the mission sheet performance cost, the performance timeliness type proportion, the category priority, the corresponding bin capacity relaxation coefficient of each warehouse and the historical sales volume data of various articles.
According to a specific implementation manner of the embodiment of the invention, the task order performance cost further comprises an ex-warehouse transportation cost and an ex-warehouse sorting cost.
According to an embodiment of the present invention, the average cost of transporting each item from the warehouse to the station is determined according to the following method: determining routing data from each warehouse j to a station k (mainly comprising routing nodes for transporting articles from the warehouse j to the station k, and then storing the routing nodes to a dictionary route [ j, k ] according to the sequence]Performing the following steps; then, the distance dist [ j, k ] between every two routing nodes is determined]And the number of sortings num _ sortation [ j, k ] that the item has passed to the station](ii) a Finally, the average cost C of each article for transporting from the warehouse to the station is determined by combining the data with an ex-warehouse cost coefficient (ob _ transFee (element/piece/kilometer) and an ex-warehouse sorting cost coefficient (ob _ operFee element/piece)j,kThe unit: and (5) Yuan. As shown in fig. 2b, the primary bin group represents a central bin, the secondary bin group represents a front bin, and the cost required to transport items included in the order from the warehouse to the site includes: delivery costs and sorting costs for direct delivery of the items from the warehouse to the site, and delivery costs for direct delivery of the items from the warehouse to the siteThe central bin is transported to the interior of the mountain city where the front bin is located.
According to the embodiment of the present invention, the warehouse network planning method further includes: according to the probability ibQtty that each article is delivered from the warehouse and belongs to the internal distribution article, the internal distribution BennrCost required by each article delivered from the warehouse is determined according to the probability and the internal distribution transportation cost coefficient (ob _ TransFee (element/element)), and specifically, the following relationship is provided:
innerCost＝ibQtty*ob_transFee
according to the embodiment of the present invention, the warehouse network planning method further includes: and determining the proportion lineprogress of each performance timeliness type of the goods transported from the warehouse j to the station k according to the performance timeliness labels indicated in the historical task order data.
Specifically, the performance aging types can be divided into a current day arrival, a next day arrival and a multiple day arrival, the aging type proportion corresponding to each article in the historical task list is determined by performing statistical summary on the data of the task list, and the aging type proportion can be represented by a vector [ the current day arrival, the next day arrival and the multiple day arrival ]. If the proportion of the type of the fulfillment time efficiency of the goods transported from the warehouse j to the station k is [0.8,0.1,0.1], the proportion of the goods delivered on the day, the proportion of the goods delivered on the next day and the proportion of the goods delivered on more days in the existing storage network route are 80%, 10% and 10%.
According to the embodiment of the present invention, the warehouse network planning method further includes: and determining the sales volume proportion of each class of articles in the station according to the historical task list data, and determining the class priority catePrority [ i ] of each class of articles according to the sales volume proportion.
Specifically, the items may be sorted in order of high sales volume and then assigned in order of natural numbers from small to large.
Step S203, a planning model is constructed according to the average cost, the internal cost, the type proportion of the performance time efficiency, the class priority, the transportation amount of each class of articles from the warehouse to the station and the constraint conditions of each article from the warehouse to the station.
For the convenience of model description, let the article class I have I, I ═ cate1,cate2,cate3,…,catei](ii) a Store J has J total, J ═ store1,store2,…,storej]Wherein the collection of central warehouses is J*The set of front warehouses is J**Existence of J*∪J**J; the station K has K stations, K is site1,site2,…,sitek]。
Based on the data, the transport volume X from warehouse to station for each type of articlei,j,kFor decision variables, the planning model is constructed as follows:
specifically, according to the embodiment of the present invention, the planning model includes a cost optimization submodel, a performance time-efficiency optimization submodel, and a category optimization submodel, and the warehousing network planning model further includes: a weight coefficient is configured for each submodel. The purpose of the planning model is to base the decision variable X oni,j,kMinimizing the optimization objective (the objective of each sub-model). Meanwhile, in order to embody the sequence of the optimization targets, the priority of each optimization target can be realized by assigning values.
As shown in the above planning model, the cost optimization submodel corresponds to a weight factor of 1010The weight coefficient corresponding to the performing time-effect optimization submodel is 105The weight coefficient corresponding to the category optimization submodel is 1, which indicates that the planning model corresponding to this embodiment sets the cost optimization to the first priority, the performance time optimization to the second priority, and the category optimization to the third priority. It should be noted that the magnitude of the weighting factor is only an example of the embodiment of the present invention, and the magnitude can be set according to the planning requirement of the warehousing network in the specific implementation.
Wherein, L in the fulfillment aging optimization submodel represents a fulfillment aging constraint vector, the vector corresponding to the fulfillment aging type is represented by [ day, next day, and multiple day ], if the proportion of the fulfillment aging type of the item transported from the warehouse j to the station k is [0.8,0.1,0.1], in order to reduce the proportion of the single quantity other than "day (i.e. to make the aging faster), the single quantity of" day "can be screened out through the fulfillment aging constraint vector L, that is, L is [0,1,1], and the fulfillment aging optimization submodel constructed through the above setting can reduce the single quantity of various low aging, so that the occupancy of the" day "is higher.
Through the arrangement, the planning model is constructed according to the transportation cost, the performance time efficiency type proportion, the class priority, the transportation quantity and corresponding constraint conditions, so that the planning model can achieve the purposes of reducing the cost and improving the efficiency. Although the warehousing network constructed by only a single target (cost optimization target) can achieve optimal cost control, the operation cost of the whole warehousing network is reduced to the minimum, a large optimization space exists in the aspects of utilization efficiency, user experience and the like, and therefore, by setting a plurality of optimization targets and setting a corresponding weight coefficient for each optimization target, the warehousing network planned by an optimization result can reduce the operation cost, improve the operation efficiency and remarkably improve the user experience. According to the emphasis points of the warehousing network services in different regions, different weight coefficients can be set for different optimization targets, for example, if the cost is optimized to be the first target, a higher weight coefficient can be set for cost optimization, and a slightly lower weight coefficient can be set for aging optimization and sales optimization.
According to an embodiment of the present invention, the constraint conditions include: demand constraint, ex-warehouse quantity constraint and class proportion constraint.
According to the embodiment of the present invention, the warehouse network planning method further includes: determining the demand D of each station for each kind of articles according to the task list datai,k。
And (3) restricting the demand: the category representing the items transported to the station by each warehouse and the corresponding number of the categories should be consistent with the demand of the station for the items, so as to ensure the supply and demand balance. Specifically, it can be represented according to the following formula:
according to the embodiment of the present invention, the warehouse network planning method further includes: and determining the maximum ex-warehouse quantity obCap [ j ] of each warehouse according to the historical task list data, and determining the maximum historical ex-warehouse quantity corresponding to each warehouse according to the warehouse capacity relaxation coefficient ξ and the maximum ex-warehouse quantity obCap [ j ] corresponding to each warehouse. The corresponding bin capacity relaxation coefficient of each warehouse is a value which is slightly larger than 1, and the aim is to slightly improve the upper limit of the warehouse delivery amount so as to enable more types or quantities of articles to be deployed in the warehouse. It should be noted that the bin capacity relaxation factor needs to be set according to actual conditions, and in general, the maximum delivery amount of each warehouse does not reach the capacity limit of the warehouse, and therefore, the value may be set to be slightly larger than 1. According to a specific implementation manner of the embodiment of the present invention, the bin relaxation coefficient may default to 1.2 (for example only, it may be adjusted according to actual conditions).
And (4) ex-warehouse quantity constraint: the total delivery quantity of all the products of each warehouse is not more than the maximum historical delivery quantity corresponding to the warehouse. Specifically, it can be represented according to the following formula:
according to the embodiment of the present invention, the warehouse network planning method further includes: and determining the stock proportion cateRatio [ i, j ] of each class of articles in each warehouse according to the stock quantity of each class of articles in the warehouse, the historical task list data and the historical sales data of each class of articles. According to an embodiment of the present invention, only the inventory ratio cateRDCratio [ i, j ] of each item in the central warehouse may be determined.
And (4) restricting the proportion of categories: the proportion representing the optimized delivery of each item type within a warehouse should be consistent with the proportion of that item type in each warehouse. Specifically, it can be represented according to the following formula:
according to the embodiment of the invention, the constraint conditions further include transportation volumes of various articles from the warehouse to the station, namely decision variable constraints, and the purpose of the constraint conditions is to avoid negative values and not conform to practical application scenarios. Specifically, it can be represented by the following formula:
Xi,j,k≥0
through the arrangement, the optimization result can be ensured to meet the layout strategy of the current warehouse goods as much as possible. Under the constraint, the warehouse network can be planned to have better landing performance aiming at optimizing the warehouse-out quantity.
And step S204, solving the planning model to obtain the target traffic volume.
Specifically, according to the embodiment of the present invention, the planning model may be solved by calling a solver, so as to obtain a decision variable (optimized ex-warehouse quantity) under the condition of an optimal solution of an optimization target, so as to plan the warehouse network according to the optimized ex-warehouse quantity.
And S205, planning at least one of the class layout, the station coverage and the task single amount distribution of the central bin and the front bin of the warehousing network according to the target traffic.
Specifically, according to the embodiment of the present invention, the central warehouse and the front warehouse only represent two-layer networks with support relationship in a macroscopic view of the warehousing network mentioned in the present invention, and in a practical situation, the central warehouse may include a primary central warehouse, a secondary central warehouse, and the like; the front warehouse may include a primary front warehouse, a secondary front warehouse, and the like.
Specifically, according to the embodiment of the invention, a key decision analysis index and a decision supporting direction can be provided for the planning of the warehousing network according to the target traffic volume. For example, basic data indexes such as transportation cost of each route and the like can be obtained; the optimization effect can be visually evaluated according to the change of the local satisfaction rate of the front-end bin before and after planning; determining an aging improvement effect according to the performance aging type proportion before and after planning; reasonably planning the class layout in the front bin and the site range covered by the front bin; and analyzing whether the product class layout of the central warehouse is reasonable or not to carry out corresponding electron.
According to an embodiment of the invention, the planning of the warehouse network according to the target traffic volume further comprises: and performing ascending and descending adjustment or warehouse type adjustment on the front warehouse. Specifically, according to the embodiment of the present invention, the front warehouse may be upgraded (expanded) or downgraded (shut down) or the warehouse type may be adjusted (e.g., converted into a grade warehouse) according to the target traffic volume.
According to the technical scheme of the embodiment of the invention, the warehousing network data is acquired; constructing a planning model according to the warehousing network data, the transportation amount of each type of article from a warehouse to a station and the constraint conditions; the technical means of obtaining the target transportation amount based on solving the planning model and planning the warehousing network according to the target transportation amount overcomes the technical problems of poor planning effect and low operation efficiency of the existing planning method due to the fact that planning layout is performed by relying on experience of service personnel, and further achieves the technical effect of remarkably improving the planning effect and the operation efficiency of the warehousing network.
Fig. 3 is a schematic diagram of main modules of a warehousing network planning device according to an embodiment of the present invention; as shown in fig. 3, the warehousing network planning apparatus 300 provided by the embodiment of the present invention mainly includes:
the warehousing network data acquisition module 301 is configured to acquire warehousing network data.
Specifically, according to the embodiment of the present invention, the warehousing network data includes a task order performance cost, a performance time type ratio, and a class priority, wherein the task order performance cost includes an average cost and an internal transportation cost of each item transported from the warehouse to the station.
Cost of performing the mission order: refers to the cost required to complete a fulfillment service that transports items from the warehouse to the site.
The type proportion of the performing aging is as follows: the method refers to the time efficiency type ratio corresponding to the completion of the fulfillment services of all articles transported from a warehouse to a station, and generally, the fulfillment time efficiency types are divided into current-day arrivals, next-day arrivals and multi-day arrivals. Specifically, according to the embodiment of the present invention, the proportion of each performance age type of an item transported from warehouse j to site k may be determined according to the performance age tags indicated in the historical order data.
Class priority: the sales rate of each type of article in all stations is determined as a priority, and the higher the sales rate is, the higher the corresponding priority of the type of article is. Specifically, according to the embodiment of the invention, the sales volume proportion of each item in the station can be determined according to the historical task list data, and the item priority of each item can be determined according to the sales volume proportion.
Specifically, according to the embodiment of the present invention, the warehousing network data further includes a warehouse capacity relaxation coefficient corresponding to each warehouse and historical sales data of each type of article. The nodes in the warehousing network comprise a central warehouse, a front warehouse and a site. In particular, a sorting center may also be included.
Through the arrangement, historical service data (namely the warehousing network data) are obtained, the problems of poor planning effect and the like caused by the fact that corresponding warehousing network planning is carried out depending on experience of service personnel are solved, meanwhile, the optimized result and the planning result which are more fit with service practice can be obtained by combining the historical service data, and the planned warehousing network has good landing performance.
According to a specific implementation manner of the embodiment of the present invention, the job ticket performing cost, the performing aging type ratio, the category priority, the bin slack coefficient corresponding to each warehouse, and the historical sales data of each category are obtained by integrating and calculating the data of the stock quantity of each category in the warehouse, the coordinate position of each node in the warehouse network, the historical job ticket data, and the like.
Further, according to the embodiment of the present invention, the warehousing network data further includes routing data of each item transported from the warehouse to the station, the task order performance cost further includes an internal transportation cost, an ex-warehouse transportation cost and an ex-warehouse sorting cost, and an average cost of each item transported from the warehouse to the station is determined according to the sorting times in the routing node sequence indicated by the routing data, the distance between every two nodes, the internal transportation cost, the ex-warehouse transportation cost and the ex-warehouse sorting cost.
For the whole warehousing network, the cost corresponding to the transportation of the articles from the warehouse to the station not only comprises the ex-warehouse delivery cost of the front warehouse to the station, but also comprises the internal distribution transportation cost generated by the central warehouse to carry the articles internally to the front warehouse.
Specifically, according to a specific implementation manner of the embodiment of the invention, the internal transportation cost is determined according to the internal quantity and the internal transportation cost parameter corresponding to each article in the warehouse.
And a planning model construction module 302, configured to construct a planning model according to the warehousing network data, the transportation amount of each item from the warehouse to the station, and the constraint condition.
According to the embodiment of the invention, the planning model comprises a cost optimization sub-model, a performance time-efficiency optimization sub-model and a quality optimization sub-model, and the warehousing network planning model further comprises: a weight coefficient is configured for each submodel.
Although the warehousing network constructed only by a single target (such as the cost optimization submodel with cost optimization as a target) can achieve optimal cost control, the operation cost of the whole warehousing network is reduced to the minimum, a larger optimization space is provided in the aspects of utilization efficiency, user experience and the like, and therefore, by setting a plurality of optimization targets and setting a corresponding weight coefficient for each optimization target, the warehousing network planned by an optimization result can not only reduce the operation cost and improve the operation efficiency, but also remarkably improve the user experience. According to the emphasis points of the warehousing network services in different regions, different weight coefficients can be set for different optimization targets (namely, sub-models), for example, if the cost is optimized to be the first target, a higher weight coefficient can be set for the cost optimization sub-model, and a slightly lower weight coefficient can be set for the fulfillment time-efficiency optimization sub-model and the quality optimization sub-model.
According to an embodiment of the present invention, the planning model building module 302 is further configured to: constructing a cost optimization sub-model according to the performance cost of the mission order, the transportation amount of each article from the warehouse to the station and the constraint condition; constructing a performance timeliness optimization sub-model according to the performance timeliness type proportion, the transportation amount of each type of article from the warehouse to the station and the constraint condition; and constructing a grade optimization sub-model according to the grade priority, the transportation amount of each grade of article from the warehouse to the station and the constraint conditions.
Through the arrangement, the planning model is constructed according to the performance cost, the performance timeliness type proportion, the quality priority, the transportation volume and corresponding constraint conditions, so that the planning model can achieve the purposes of reducing the cost and improving the efficiency.
According to an embodiment of the present invention, the constraint conditions include: demand constraint, ex-warehouse quantity constraint and class proportion constraint.
According to an embodiment of the present invention, the warehousing network planning device 300 further includes a demand determining module, configured to determine, according to the task list data, the demand of each station for each item.
And (3) restricting the demand: the category representing the items transported to the station by each warehouse and the corresponding number of the categories should be consistent with the demand of the station for the items, so as to ensure the supply and demand balance.
According to an embodiment of the present invention, the warehousing network planning device 300 further includes a maximum ex-warehouse quantity determining module, configured to determine the maximum ex-warehouse quantity of each warehouse according to the historical task list data, and determine the maximum historical ex-warehouse quantity corresponding to each warehouse according to the warehouse capacity relaxation coefficient and the maximum ex-warehouse quantity corresponding to each warehouse. The corresponding bin capacity relaxation coefficient of each warehouse is a value which is slightly larger than 1, and the aim is to slightly improve the upper limit of the warehouse delivery amount so as to enable more types or quantities of articles to be deployed in the warehouse. It should be noted that the bin capacity relaxation factor needs to be set according to actual conditions, and in general, the maximum delivery amount of each warehouse does not reach the capacity limit of the warehouse, and therefore, the value may be set to be slightly larger than 1.
And (4) ex-warehouse quantity constraint: the total delivery quantity of all the products of each warehouse is not more than the maximum historical delivery quantity corresponding to the warehouse.
According to an embodiment of the present invention, the warehousing network planning device 300 further includes an inventory ratio determining module, configured to determine an inventory ratio of each item in each warehouse according to the inventory amount of each item in the warehouse, the historical order data, and the historical sales data of each item. According to a specific implementation of the embodiment of the present invention, only the stock proportion of each item in the central warehouse can be determined.
And (4) restricting the proportion of categories: the proportion representing the optimized delivery of each item type within a warehouse should be consistent with the proportion of that item type in each warehouse.
Through the arrangement, the optimization result can be ensured to meet the layout strategy of the current warehouse goods as much as possible. Under the constraint, the warehouse network can be planned to have better landing performance aiming at optimizing the warehouse-out quantity.
And the planning module 303 is configured to obtain a target transportation amount based on the solution of the planning model, and plan the warehouse network according to the target transportation amount.
Specifically, according to the embodiment of the present invention, the planning model may be solved by calling a solver, so as to obtain a decision variable (optimized ex-warehouse quantity) under the condition of an optimal solution of an optimization target, and then the warehouse network may be planned according to the optimized ex-warehouse quantity.
According to an embodiment of the present invention, the planning module 303 is further configured to: and planning at least one of the class layout of the warehousing network, the site coverage and the task single amount distribution of the central bin and the front bin according to the target traffic.
Specifically, according to the embodiment of the present invention, the central warehouse and the front warehouse only represent two-layer networks with support relationship in a macroscopic view of the warehousing network mentioned in the present invention, and in a practical situation, the central warehouse may include a primary central warehouse, a secondary central warehouse, and the like; the front warehouse may include a primary front warehouse, a secondary front warehouse, and the like.
According to the technical scheme of the embodiment of the invention, the warehousing network data is acquired; constructing a planning model according to the warehousing network data, the transportation amount of each type of article from a warehouse to a station and the constraint conditions; the technical means of obtaining the target transportation amount based on solving the planning model and planning the warehousing network according to the target transportation amount overcomes the technical problems of poor planning effect and low operation efficiency of the existing planning method due to the fact that planning layout is performed by relying on experience of service personnel, and further achieves the technical effect of remarkably improving the planning effect and the operation efficiency of the warehousing network.
Fig. 4 illustrates an exemplary system architecture 400 of a warehouse network planning method or warehouse network planning apparatus to which embodiments of the present invention may be applied.
As shown in fig. 4, the system architecture 400 may include terminal devices 401, 402, 403, a network 404, and a server 405 (this architecture is merely an example, and the components included in a particular architecture may be adapted according to application specific circumstances). The network 404 serves as a medium for providing communication links between the terminal devices 401, 402, 403 and the server 405. Network 404 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use terminal devices 401, 402, 403 to interact with a server 405 over a network 404 to receive or send messages or the like. The terminal devices 401, 402, 403 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 401, 402, 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 405 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 401, 402, 403. The back-office management server may analyze and otherwise process the received data, such as the warehousing network data, and feed back the processing results (e.g., planning model, target traffic, just an example) to the terminal device.
It should be noted that the warehousing network planning method provided by the embodiment of the present invention is generally executed by the server 405, and accordingly, the warehousing network planning device is generally disposed in the server 405.
It should be understood that the number of terminal devices, networks, and servers in fig. 4 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 5, shown is a block diagram of a computer system 500 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU)501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the 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 illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 501.
It should be noted that the computer readable medium shown in the present invention can 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 the present invention, 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, 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: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
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 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 described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a warehousing network data acquisition module, a planning model construction module, and a planning module. The names of these modules do not in some cases constitute a limitation on the modules themselves, for example, the warehouse network data acquisition module may also be described as a "module for acquiring warehouse network data".
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 separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: acquiring warehousing network data; constructing a planning model according to the warehousing network data, the transportation amount of each type of article from a warehouse to a station and the constraint conditions; and solving the planning model to obtain target transportation amount, and planning the warehousing network according to the target transportation amount.
According to the technical scheme of the embodiment of the invention, the warehousing network data is acquired; constructing a planning model according to the warehousing network data, the transportation amount of each type of article from a warehouse to a station and the constraint conditions; the technical means of obtaining the target transportation amount based on solving the planning model and planning the warehousing network according to the target transportation amount overcomes the technical problems of poor planning effect and low operation efficiency of the existing planning method due to the fact that planning layout is performed by relying on experience of service personnel, and further achieves the technical effect of remarkably improving the planning effect and the operation efficiency of the warehousing network.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for planning a warehousing network, comprising:
acquiring warehousing network data;
constructing a planning model according to the warehousing network data, the transportation amount of each type of article from a warehouse to a station and constraint conditions;
and solving the planning model to obtain target transportation amount, and planning the warehousing network according to the target transportation amount.
2. The warehouse network planning method of claim 1, wherein the warehouse network data comprises a mission order performance cost, a performance timeliness type proportion, and a category priority, wherein the mission order performance cost comprises an average cost and an internal transportation cost for transporting each item from a warehouse to a site.
3. The warehouse network planning method of claim 2, wherein the warehouse network data further comprises routing data for each item transported from the warehouse to the site, the mission profile performance cost further comprises an outbound transportation cost and an outbound sorting cost, and the average cost for each item transported from the warehouse to the site is determined according to the sorting times in the sequence of routing nodes and the distance between each two nodes, and the outbound transportation cost and the outbound sorting cost indicated by the routing data.
4. The warehouse network planning method of claim 1, wherein the planning model comprises a cost optimization sub-model, a performance aging optimization sub-model, and a class optimization sub-model, the warehouse network planning model further comprising: a weight coefficient is configured for each submodel.
5. The warehouse network planning method of claim 4, wherein the step of constructing a planning model based on the warehouse network data, the transportation volumes of the various items from the warehouse to the station, and constraints comprises: constructing a cost optimization sub-model according to the performance cost of the mission order, the transportation amount of each article from the warehouse to the station and the constraint condition; constructing a performance timeliness optimization sub-model according to the performance timeliness type proportion, the transportation amount of each type of article from the warehouse to the station and the constraint condition; and constructing a grade optimization sub-model according to the grade priority, the transportation amount of each grade of article from the warehouse to the station and the constraint conditions.
6. The warehouse network planning method of claim 1, wherein the constraints comprise: demand constraint, ex-warehouse quantity constraint and class proportion constraint.
7. The warehouse network planning method of claim 1, wherein the step of planning the warehouse network according to the target traffic volume comprises: and planning at least one of the class layout, the site coverage and the task single amount distribution of the central bin and the front bin of the warehousing network according to the target transportation amount.
8. A warehousing network planning device, comprising:
the warehousing network data acquisition module is used for acquiring warehousing network data;
the planning model building module is used for building a planning model according to the warehousing network data, the transportation amount of each type of article from the warehouse to the station and the constraint conditions;
and the planning module is used for solving the planning model to obtain target transportation amount and planning the warehousing network according to the target transportation amount.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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| CN114219353B (en) * | 2021-12-28 | 2023-04-28 | 上海置潮信息科技有限公司 | Logistics management method based on big data and intelligent warehouse |
| CN114331270B (en) * | 2021-12-28 | 2023-09-01 | 上海赢链通网络科技有限公司 | Warehouse management system based on intelligent manufacturing |
| CN116362646A (en) * | 2023-05-31 | 2023-06-30 | 北京京东乾石科技有限公司 | Logistics network upgrading method and device |
| CN116362646B (en) * | 2023-05-31 | 2023-09-26 | 北京京东乾石科技有限公司 | Logistics network upgrading method and device |
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