CN118657273B - A store route clustering planning method, system, electronic device and storage medium - Google Patents
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Abstract
本发明提出一种门店路线聚类规划方法、系统、电子设备及存储介质，属于路线规划领域。其中，方法包括：根据门店的拜访频率、预定义等级和销售能力值，计算门店的综合权重；根据待聚类门店与聚类中心门店的距离和门店的综合权重，对待聚类门店进行聚类，得到多个聚类群组；根据聚类群组内的相邻门店之间的道路距离，进行路线规划，得到最优访问路径。本发明提出的方案能够充分利用了业务与地理信息的结合，更有效地协调门店间的服务路线，并采用了先进的数学模型与算法，从而保障了路线优化结果的精度和效率，对行业实际操作有着明显的改进作用。
The present invention proposes a store route clustering planning method, system, electronic device and storage medium, which belongs to the field of route planning. The method includes: calculating the comprehensive weight of the store according to the store's visit frequency, predefined level and sales capacity value; clustering the stores to be clustered according to the distance between the store to be clustered and the cluster center store and the comprehensive weight of the store to obtain multiple cluster groups; and planning the route according to the road distance between adjacent stores in the cluster group to obtain the optimal access path. The scheme proposed by the present invention can make full use of the combination of business and geographic information, more effectively coordinate the service routes between stores, and adopt advanced mathematical models and algorithms to ensure the accuracy and efficiency of route optimization results, which has a significant improvement effect on the actual operation of the industry.
Description
Technical Field
The invention belongs to the field of route planning, and particularly relates to a store route clustering planning method, a store route clustering planning system, electronic equipment and a storage medium.
Background
With the increase of the urban speed and the expansion of supermarket business, how to effectively organize logistics distribution and personnel visit of supermarket stores becomes the key for improving the efficiency and reducing the cost. The challenge is how to minimize the total delivery route mileage on the premise of ensuring the service quality, and how to effectively integrate the business attributes of the store (such as visit frequency, store grade and sales capability) into the route planning so as to improve the overall operation efficiency of the enterprise.
In the prior art, CN116432886a provides an intelligent route planning method, which comprises selecting a store which must be visited on the day of a wire arrangement date, calculating visit frequency specific gravity values of all stores, selecting a reference store according to the visit frequency specific gravity values of all stores, selecting alternative stores, selecting a specific scheme according to actual needs to calculate store position specific gravity values of all alternative stores, calculating wire arrangement weights of the alternative stores according to the visit frequency specific gravity values and the store position specific gravity values, and completing intelligent route planning according to the wire arrangement weights.
Chinese patent CN112166423a provides a real estate searching or comparing method based on commute time. The method effectively processes public transportation and real estate data to calculate travel time between real estate and vehicle stops. These times are stored. Since the times of these portions are calculated and stored in advance, the method can determine the commute time to each real estate in a scalable manner.
The CN111161102A provides a scenic spot service facility layout analysis method based on GIS, which comprises the following steps of 1) importing acquired data into a GIS platform, 2) selecting a real-time population big data sampling point and a nearest service facility through GIS and calculating the actual distance between the two, 3) calculating the coupling degree between the real-time population sampling point and the service facility, and 4) graphically expressing the coupling degree so as to output the final scenic spot facility layout analysis result. The method can reflect the coupling relation between the service facilities and the population distribution in the scenic spot range, thereby providing basis for the layout of the service facilities.
These prior art drawbacks are mainly manifested in: while the prior art provides a Geographic Information System (GIS) based route planning solution, these methods typically only place emphasis on geographic location factors, ignoring business attributes of supermarket stores, such as store service demand frequency, importance level and sales performance, resulting in planned routes that may be inefficient in actual operation and unable to meet complex business demands.
Disclosure of Invention
In order to solve the technical problems, the invention provides a store route clustering planning method, a store route clustering planning system, electronic equipment and a storage medium.
The invention discloses a store route clustering planning method, which comprises the following steps:
Step S1, calculating comprehensive weight of the store according to visit frequency, predefined grade and sales capacity value of the store;
S2, clustering the stores to be clustered according to the distance between the stores to be clustered and the store of the clustering center and the comprehensive weight of the stores to be clustered to obtain a plurality of clustering groups;
And S3, carrying out route planning according to the road distance between adjacent stores in the cluster group to obtain an optimal access path.
According to the method of the first aspect of the present invention, in the step S1, the calculating the comprehensive weight of the store according to the visit frequency, the predefined level and the sales capability value of the store includes:
wherein, Represent the firstComprehensive weight of the store; Represent the first Visit frequency of the store; Represent the first Predefined grades of home stores; Represent the first Sales capability value of the store; Represent the first The weight coefficient of visit frequency of the store; Represent the first The weight coefficient of the predefined level of the store; Represent the first Weight coefficient of sales capability value of the store.
According to the method of the first aspect of the present invention, in the step S2, clustering the stores to be clustered according to the distance between the stores to be clustered and the central store of the cluster and the comprehensive weight of the stores, to obtain a plurality of cluster groups includes:
wherein, Representing clustered targets; Represent the first A cluster group; Representing the number of cluster groups; is traversed by the first A store at home is provided with a function of,Is the firstShop at homeAnd clustering central storeRoad distance between; Represent the first Comprehensive weight of the store; representing the comprehensive weight of the store in the clustering center; Representing the comprehensive evaluation function, the evaluation function calculates a reference value according to the input parameters, and the reference value represents the first The likelihood that the stores and cluster center stores are grouped into a group,The specific implementation of the function is flexible, and a common normalization function or other similar functions can be used.
According to the method of the first aspect of the present invention, in the step S3, the performing route planning according to the road distance between adjacent stores in the cluster group includes:
wherein, Representing a specific access path within the cluster group, including a sequential arrangement from one store to another; And Respectively the firstAnd (b)The stores traversed by the cluster groups,Representing access pathsUpper adjacent storeAndRoad distance between; Representing an objective function, i.e. an access path The sum of all the road distances between adjacent stores in the upper cluster group.
The second aspect of the invention discloses a store route clustering planning system, which comprises:
a first processing module configured to calculate an integrated weight for the store based on the store's visit frequency, the predefined level, and the sales capability value;
The second processing module is configured to cluster the stores to be clustered according to the distance between the stores to be clustered and the clustering center and the comprehensive weight of the stores to be clustered to obtain a plurality of clustering groups;
and the third processing module is configured to conduct route planning according to the road distance between adjacent stores in the cluster group so as to obtain an optimal access path.
According to the system of the second aspect of the present invention, the calculating the comprehensive weight of the store according to the visit frequency, the predefined level and the sales capability value of the store includes:
wherein, Represent the firstComprehensive weight of the store; Represent the first Visit frequency of the store; Represent the first Predefined grades of home stores; Represent the first Sales capability value of the store; Represent the first The weight coefficient of visit frequency of the store; Represent the first The weight coefficient of the predefined level of the store; Represent the first Weight coefficient of sales capability value of the store.
According to the system of the second aspect of the present invention, the clustering the stores to be clustered according to the distance between the stores to be clustered and the central store and the comprehensive weight of the stores to be clustered, to obtain a plurality of cluster groups includes:
wherein, Representing clustered targets; Represent the first A cluster group; Representing the number of cluster groups; is traversed by the first A store at home is provided with a function of,Is the firstShop at homeAnd clustering central storeRoad distance between; Represent the first Comprehensive weight of the store; representing the comprehensive weight of the store in the clustering center; Representing the comprehensive evaluation function, the entries are respectively:
Store shop To a cluster center storeRoad distance between;
Represent the first Comprehensive weight of the store;
is a cluster center store;
the comprehensive evaluation function may give a final reference result based on the input parameters.
According to the system of the second aspect of the present invention, the route planning according to the road distance between adjacent stores in the cluster group includes:
wherein, Representing a specific access path within the cluster group, including a sequential arrangement from one store to another; And Respectively the firstAnd (b)The stores traversed by the cluster groups,Representing access pathsUpper adjacent storeAndRoad distance between; Representing an objective function, i.e. an access path The sum of all the road distances between adjacent stores in the upper cluster group.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory storing a computer program and a processor implementing the steps in a store route cluster planning method of any one of the first aspects of the present disclosure when the processor executes the computer program.
A fourth aspect of the invention discloses a computer-readable storage medium. A computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps in a store route cluster planning method of any one of the first aspects of the present disclosure.
In summary, the proposal provided by the invention can fully utilize the combination of business and geographic information, coordinate the service route among stores more effectively, and adopt advanced mathematical models and algorithms, thereby guaranteeing the precision and efficiency of route optimization results and having obvious improvement effect on the actual operation of industry.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a store route clustering programming method in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of 100 stores according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of 100 stores clustered according to an embodiment of the invention;
FIG. 4 is a block diagram of a store route cluster planning system according to an embodiment of the invention;
fig. 5 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention discloses a store route clustering planning method. Fig. 1 is a flowchart of a store route clustering planning method according to an embodiment of the present invention, as shown in fig. 1, the method includes:
Step S1, calculating comprehensive weight of the store according to visit frequency, predefined grade and sales capacity value of the store;
S2, clustering the stores to be clustered according to the distance between the stores to be clustered and the store of the clustering center and the comprehensive weight of the stores to be clustered to obtain a plurality of clustering groups;
And S3, carrying out route planning according to the road distance between adjacent stores in the cluster group to obtain an optimal access path.
In step S1, the comprehensive weight of the store is calculated from the frequency of store visits, the predefined level and the sales capability value.
In some embodiments, in the step S1, calculating the comprehensive weight of the store according to the visit frequency, the predefined level, and the sales capability value of the store includes:
the method is a standardized function, and is used for outputting a value range [0,1] of a result and converting attribute values of different scales into scores which can be compared and accumulated, so that the balance and fairness among the attribute values are ensured;
Normalization function: Ginseng radix The value range of the parameter is [0, ++ infinity ], and the value range of the parameter is [0,1];
wherein, Represent the firstComprehensive weight of the store; Represent the first The visit frequency of the store, namely the number of normal visits required in a certain time period; the visit frequency reflects the activity degree of the store and the demand intensity of the service; the higher the store visit frequency, the larger the visit frequency value; Represent the first A predefined level of stores, which is a rating assigned according to sales, traffic and other key business indicators of stores, reflecting the importance of stores in a supermarket network; the higher the store predefined level, the greater the store level value; Represent the first The sales capacity value of the store is measured by sales line or average transaction amount, and reflects the sales performance of the store; the higher the store sales, the greater the sales capability value; Represent the first The weight coefficient of visit frequency of the store; Represent the first The weight coefficient of the predefined level of the store; Represent the first Weight coefficient of sales capability value of the store.
In step S2, the stores to be clustered are clustered according to the distance between the stores to be clustered and the store of the clustering center and the comprehensive weight of the stores to be clustered, so that a plurality of clustering groups are obtained.
Direct optimization of overall route costs is a highly complex problem due to the diversity of store geography and business attributes. To simplify this problem and improve the computational efficiency, the present embodiment employs a clustering strategy. The strategy groups stores which are geographically adjacent and have similar business requirements into the same cluster, and takes the stores as a basic unit for planning a route.
In some embodiments, in the step S2, clustering the stores to be clustered according to the distance between the stores to be clustered and the central store of the cluster and the comprehensive weight of the stores, to obtain a plurality of cluster groups includes:
wherein, Representing clustered targets; Represent the first A cluster group; Representing the number of cluster groups; is traversed by the first A store at home is provided with a function of,Is the firstShop at homeAnd clustering central storeRoad distance between; Represent the first Comprehensive weight of the store; Representing the comprehensive weight of the store in the clustering center; representing the comprehensive evaluation function, the evaluation function calculates a reference value according to the input parameters, and the reference value represents the first The likelihood that the stores and cluster center stores are grouped into a group,The specific implementation of the function is flexible, and a common normalization function or other similar functions can be used; Representing the comprehensive evaluation function, the entries are respectively:
Store shop To a cluster center storeRoad distance between;
Represent the first Comprehensive weight of the store;
is a cluster center store;
the comprehensive evaluation function may give a final reference result based on the input parameters.
In step S3, route planning is performed according to the road distance between adjacent stores in the cluster group, so as to obtain an optimal access path.
After determining the clustered groupings of routes, a specific access order within each group then needs to be planned. The present embodiment achieves this optimization by minimizing the intra-cluster path length.
In some embodiments, in the step S3, the performing route planning according to the road distance between adjacent stores in the cluster group includes:
wherein, Representing a specific access path within the cluster group, including a sequential arrangement from one store to another; And respectively the first And (b)The stores traversed by the cluster groups,Representing access pathsUpper adjacent storeAndRoad distance between; Representing an objective function, i.e. an access path The sum of all the road distances between adjacent stores in the upper cluster group.
Example 1
Step 1, as shown in fig. 2, randomly generating 100 stores, and calculating comprehensive weights of the stores according to visit frequencies, predefined grades and sales capability values of the stores;
Step 2, as shown in fig. 3, a clustering algorithm is applied to divide 100 stores into 6 designated clusters;
and 3, connecting the stores in series according to a path optimization algorithm.
In summary, the proposal provided by the invention can fully utilize the combination of business and geographic information, coordinate the service route among stores more effectively, and adopt advanced mathematical models and algorithms, thereby guaranteeing the precision and efficiency of route optimization results and having obvious improvement effect on the actual operation of industry.
The second aspect of the invention discloses a store route clustering planning system. FIG. 4 is a block diagram of a store route cluster planning system according to an embodiment of the invention; as shown in fig. 4, the system 100 includes:
a first processing module 101 configured to calculate an integrated weight of the store according to the visit frequency, the predefined level, and the sales capability value of the store;
the second processing module 102 is configured to cluster the stores to be clustered according to the distance between the stores to be clustered and the central store and the comprehensive weight of the stores to be clustered to obtain a plurality of cluster groups;
And the third processing module 103 is configured to perform route planning according to the road distance between adjacent stores in the cluster group, so as to obtain an optimal access path.
According to the system of the second aspect of the present invention, the first processing module 101 is specifically configured to calculate the comprehensive weight of the store according to the visit frequency, the predefined level and the sales capability value of the store, including:
the method is a standardized function, and is used for outputting a value range [0,1] of a result and converting attribute values of different scales into scores which can be compared and accumulated, so that the balance and fairness among the attribute values are ensured;
Normalization function: Ginseng radix The value range of the parameter is [0, ++ infinity ], and the value range of the parameter is [0,1];
wherein, Represent the firstComprehensive weight of the store; Represent the first The visit frequency of the store, namely the number of normal visits required in a certain time period; the visit frequency reflects the activity degree of the store and the demand intensity of the service; the higher the store visit frequency, the larger the visit frequency value; Represent the first A predefined level of stores, which is a rating assigned according to sales, traffic and other key business indicators of stores, reflecting the importance of stores in a supermarket network; the higher the store predefined level, the greater the store level value; Represent the first The sales capacity value of the store is measured by sales line or average transaction amount, and reflects the sales performance of the store; the higher the store sales, the greater the sales capability value; Represent the first The weight coefficient of visit frequency of the store; Represent the first The weight coefficient of the predefined level of the store; Represent the first Weight coefficient of sales capability value of the store.
According to the system of the second aspect of the present invention, the second processing module 102 is specifically configured to perform clustering on the stores to be clustered according to the distance between the stores to be clustered and the central store of the clustering and the comprehensive weight of the stores, so as to obtain a plurality of cluster groups, where:
wherein, Representing clustered targets; Represent the first A cluster group; Representing the number of cluster groups; is traversed by the first A store at home is provided with a function of,Is the firstShop at homeAnd clustering central storeRoad distance between; Represent the first Comprehensive weight of the store; representing the comprehensive weight of the store in the clustering center; Representing the composite evaluation function.
According to the system of the second aspect of the present invention, the third processing module 103 is specifically configured to perform route planning according to a road distance between adjacent stores in the cluster group, including:
wherein, Representing a specific access path within the cluster group, including a sequential arrangement from one store to another; And Respectively the firstAnd (b)Stores traversed by the cluster groups, representing access pathsUpper adjacent storeAndRoad distance between; Representing an objective function, i.e. an access path The sum of all the road distances between adjacent stores in the upper cluster group.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps in a store route clustering planning method according to any one of the first aspects of the disclosure when executing the computer program.
Fig. 5 is a block diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 5, the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the electronic device is used for conducting wired or wireless communication with an external terminal, and the wireless communication can be achieved through WIFI, an operator network, near Field Communication (NFC) or other technologies. The display screen of the electronic equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the electronic equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of a portion related to the technical solution of the present disclosure, and does not constitute a limitation of the electronic device to which the technical solution of the present disclosure is applied, and a specific electronic device may include more or less components than those shown in the drawings, or may combine some components, or have different component arrangements.
A fourth aspect of the invention discloses a computer-readable storage medium. A computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of a store route cluster planning method of any one of the first aspects of the present disclosure.
It can be seen that the present invention has obvious technical differences from the prior art, for example, compared with the chinese patent CN116432886a, the latter emphasizes that the most suitable store is selected to the route according to various weights of stores, and stores may exist on different routes at the same time. The method mainly emphasizes that the designated stores are classified on different routes according to various weights of the stores and reasonably arranged, and the stores only exist on one route, so that the pertinence and the accuracy are stronger; CN116432886a uses standard store matching weight calculation to achieve the goal of planning a route, so that more data is needed as support for analyzing the planned route, and the calculation mode is mainly based on the reference value of the distance from the store to the standard store or the actual distance angle. The invention uses the K-means clustering algorithm based on the optimized version to realize the route planning target in cooperation with the weight, and has the advantages that reference stores are not needed to be used as references, and the center point of the class is continuously adjusted through multiple rounds of calculation of the clustering algorithm until the center point of the class is not changed.
Note that the technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be regarded as the scope of the description. The foregoing examples merely illustrate a few embodiments of the present application, which are described in greater detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
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| US11361361B2 (en) * | 2018-02-20 | 2022-06-14 | Grzegorz Malewicz | Method and an apparatus for searching or comparing sites using routes or route lengths between sites and places within a transportation system |
| CN111161102B (en) * | 2019-12-31 | 2022-04-05 | 华侨大学 | A GIS-based Layout Analysis Method for Service Facilities in Scenic Spots |
| CN112348601B (en) * | 2020-12-04 | 2024-11-08 | 上海晶确科技有限公司 | Business Development Roadmap Planning Method |
| CN116432886B (en) * | 2023-06-13 | 2023-08-29 | 北京纷扬科技有限责任公司 | An Intelligent Route Planning Method |
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