CN116433245B - Customer visit plan generation method and system - Google Patents
Customer visit plan generation method and system Download PDFInfo
- Publication number
- CN116433245B CN116433245B CN202310679235.7A CN202310679235A CN116433245B CN 116433245 B CN116433245 B CN 116433245B CN 202310679235 A CN202310679235 A CN 202310679235A CN 116433245 B CN116433245 B CN 116433245B
- Authority
- CN
- China
- Prior art keywords
- client
- visit
- visited
- daily
- plan
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 13
- 238000004590 computer program Methods 0.000 claims description 6
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 230000002354 daily effect Effects 0.000 description 34
- 239000004973 liquid crystal related substance Substances 0.000 description 10
- 230000006870 function Effects 0.000 description 4
- 239000003016 pheromone Substances 0.000 description 4
- 238000005457 optimization Methods 0.000 description 3
- 241000257303 Hymenoptera Species 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007635 classification algorithm Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000003064 k means clustering Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000009827 uniform distribution Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/01—Customer relationship services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/109—Time management, e.g. calendars, reminders, meetings or time accounting
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides a method and a system for generating a customer visit plan, wherein the method comprises the following steps: for each salesman, acquiring client data required to be visited by the salesman; clustering the clients to be visited based on the client data to obtain at least one cluster; determining a starting point client, selecting one client which has the highest priority and can be visited from a cluster where the starting point client is located, adding a daily visit plan, and repeating the steps until the requirement of daily working time is met; and calculating an optimal visit path meeting the daily working time requirement by adopting a path planning algorithm for all clients in the current daily visit plan. The method can dynamically arrange the number of daily visiting clients according to daily working time, can enable the plurality of visiting clients of the same client to be evenly distributed in a monthly plan, enables the visiting clients to be distributed in a concentrated mode, and reduces the time consumption of the journey.
Description
Technical Field
The application relates to the technical field of computers, in particular to a method and a system for generating a customer visit plan.
Background
Customer visit is an important work in the quick-elimination field, and according to the importance degree of customers, each business person can have tens to hundreds of different customers which are responsible for visiting each month, and the frequency of each customer visit each month is different. Planning daily visit routes is a heavy work, and if the planning is not reasonable, the customer visit is unbalanced, the route is not reasonable, and a large amount of working time is wasted on the visit route. There is therefore a need in the industry for a customer visit plan planning scheme that improves the efficiency of a business person's visit.
Disclosure of Invention
One or more embodiments of the present specification describe a method and apparatus for generating a customer visit plan, which can partially solve the above-mentioned problems existing in the prior art.
According to a first aspect, there is provided a method of generating a customer visit plan, the method comprising the steps of:
s101, aiming at each salesman, acquiring client data required to be visited by the salesman;
s102, clustering the clients to be visited based on the client data to obtain at least one cluster;
s103, selecting the client with the largest number of times to be visited from all clients to be visited of each salesman as a starting point;
s104, judging whether the client serving as the starting point is accessible or not, and if the client serving as the starting point is not accessible, reselecting the client with the largest number of times of waiting for access from the rest clients to be accessed as the starting point; if the client can be visited, selecting one client which has the highest priority and can be visited from the cluster where the starting client is located, and adding the client into a daily visit plan;
s105, judging whether the working time required by the daily visit plan meets the daily working time requirement; if yes, executing step S106; if not, taking the newly added client as a starting point, selecting one client with the highest priority and capable of being visited from a cluster where the starting point client is located to add a daily visit plan, and then repeatedly executing the step S105;
s106, calculating an optimal visit path and corresponding working time length for all clients in the current daily visit plan by adopting a path planning algorithm; if the working time length corresponding to the optimal visit path meets the requirement of daily working time length, ending the step and outputting a daily visit plan; if not, taking the last client of the optimal visit path as a starting point, selecting one client which has the highest priority and can be visited from the cluster where the client of the starting point is located to join the daily visit plan, and then repeatedly executing the step S106.
As an optional implementation manner of the method of the first aspect, the method for determining whether a client is accessible specifically includes: judging whether the client meets the visit condition or not, wherein the client meeting the visit condition is a visiting client; the visit conditions are as follows:
；
wherein, the liquid crystal display device comprises a liquid crystal display device,indicating the last visit date or last planned visit date,/-for>Indicating that the client has not been visited or has not been visited, a +.>Indicates the current date,/->Indicates the number of days of the month, < > and->Indicating the frequency of visits by the customer.
As an optional implementation manner of the method of the first aspect, the calculating manner of the priority is:
；
wherein, the liquid crystal display device comprises a liquid crystal display device,representing priority coefficients, ++>The larger the value of (c) is, the higher the priority is, +.>Representing a priority weighting factor,/->Representing the distance between the current client and the origin client, < >>Representing intermediate parameters, when->Is greater than->When (I)>Has a value of 1, otherwise->The value of (2) is 0.
As an optional implementation manner of the method of the first aspect, the working time required by the daily visit plan is: and the sum of the visit duration of each client and the time consumption of the journey to each client in the daily visit plan.
As an optional implementation of the method according to the first aspect, the method further comprises:
if the starting point client is not in the cluster in which the starting point client is located, selecting the client which has the highest priority and can be visited from the rest clients to be visited, and adding the client to the daily visit plan.
As an optional implementation of the method according to the first aspect, the method further comprises:
s101 to S106 are executed for each weekday of each month, and a day visit plan for each weekday is obtained; and sequencing the daily visit plans according to the time sequence to obtain a month visit plan.
According to a second aspect, there is provided a customer visit plan generation system comprising:
the data acquisition module is configured to acquire the data of the salesman and the client data required to be visited by the salesman;
the visit calendar generation module is configured to generate a customer visit calendar based on the visit date selected by the user;
and the visit plan generation module is configured to generate a customer visit plan by adopting the customer visit plan generation method based on the salesman data, the customer data required to be visited by the salesman and the customer visit calendar.
As an alternative implementation of the system according to the second aspect, the client data includes a client location, a visit frequency, a last visit date.
According to a third aspect, there is provided a computer readable storage medium storing a computer program which when executed by a processor implements the above-described customer visit plan generation method.
According to a fourth aspect, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the above-mentioned client call plan generation method when executing the program.
The beneficial effects are that: the embodiment of the specification provides a method and a system for generating a customer visit plan, wherein the method can be used for
The number of daily visits to the customer is dynamically arranged according to the daily work time. And multiple visits of the same customer can be uniformly distributed in a monthly plan, so that the visit is more reasonable. The daily visit client positions can be intensively distributed, and the journey time consumption is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present description, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of day visit plan generation as referred to in one embodiment of the present description;
fig. 2 is a block diagram of a client visit plan generation device according to an embodiment of the present specification.
Description of the embodiments
The existing method for generating the visit plan generally does not consider the time interval between multiple visits of the same customer when the visit customer is selected, and does not consider whether the time consumption of the journey of the same customer visited by the service staff on the same day is reasonable or not. Meanwhile, as the distance between each client and the visiting task are different, the number of the clients visited every day is also changed continuously and the service staff takes the leave, the visiting clients selected by fixed number randomly or according to weight can not meet the requirement of the service visiting.
In order to solve the above problems, an embodiment of the present disclosure provides a method and an apparatus for generating a customer visit plan.
For the purposes of making the objects, technical solutions and advantages of the present specification more apparent, the technical solutions of the present specification will be clearly and completely described below with reference to specific embodiments of the present specification and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present specification. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present disclosure.
Fig. 1 is a flowchart of a method for generating a customer visit plan according to an embodiment of the present disclosure, where the method includes the following steps:
s101, aiming at each salesman, acquiring client data required to be visited by the salesman.
In the embodiment of the present specification, the client data may specifically include a location of the client, a visit frequency, a last visit date, and the like.
S102, clustering the clients to be visited based on the client data to obtain at least one cluster.
In the embodiment of the specification, the clients responsible for each salesman can be clustered according to the positions by using a classification algorithm, for example, the clients to be visited are clustered by using a k-means clustering algorithm, and the specific steps are as follows:
s1021, randomly selecting N clients from all the clients to be visited as the centers of N clustering clusters;
s1022, calculating the distance between the longitude and latitude of each client and the longitude and latitude of the center of the N clusters, and dividing the client into clusters closest to each client;
s1023, in each cluster, calculating the average longitude and latitude of all clients in the current cluster, and selecting the client closest to the average longitude and latitude in the cluster as a new center of the current cluster;
s1024, repeating the step S1022 and the step S1023 until the central client of each cluster is not changed or the set repetition times are reached;
s1025, outputting N clusters and clients to which the N clusters belong.
S103, selecting the client with the largest number of to-be-visited times from all to-be-visited clients of each salesman as a starting point.
In this step S103, the clients that each salesman needs to visit may be sorted in descending order according to the number of times to visit, and the client with the largest number of times to visit may be selected.
S104, judging whether the client serving as the starting point is accessible or not, and if the client serving as the starting point is not accessible, reselecting the client with the largest number of times of waiting for access from the rest clients to be accessed as the starting point; if the client can be visited, selecting the client with the highest priority and the highest visiting priority from the cluster where the starting client is located, and adding the client to the daily visit plan.
In this step S104, it is determined whether a client is available for visit, and it is necessary to determine whether the client satisfies the following visit conditions:
；
wherein, the liquid crystal display device comprises a liquid crystal display device,indicating the last visit date or last planned visit date,/-for>Indicating that the client has not been visited or has not been visited, a +.>Indicates the current date,/->Indicates the number of days of the current month, < > 10 >>Indicating the visit frequency of the client, the visit frequency can be set according to the requirement, and if the client needs to visit 4 times per month, n is set as 4.
The priority function may be designed to:
；
wherein, the liquid crystal display device comprises a liquid crystal display device,representing the priority coefficient of each client, the larger the priority is, the smaller the interval between two visits of the same client is, the +.>The smaller the value of (c) thereby ensuring a uniform distribution of multiple visits by the same customer. />Representing a priority weighting factor,/->Representing the distance between the current client and the origin client, < >>Representing intermediate parameters, when->Is greater than->When (I)>Has a value of 1, otherwise->The value of (2) is 0.
Setting the working time required by the daily visit plan as T, and updating the value of T after a client joins the daily visit plan as follows:
；
wherein, the liquid crystal display device comprises a liquid crystal display device,time consuming for showing new added client visit task +.>Representing the time taken to travel from the starting customer to the newly added customer.
S105, judging whether the working time required by the daily visit plan meets the daily working time requirement; if yes, executing step S106; if not, taking the newly added client as a starting point, selecting one client with the highest priority and capable of being visited from the cluster where the starting point client is located to add a daily visit plan, and then repeatedly executing step S105.
In this step S105, the daily operation time period may be set to beJudging what is needed by the daily visit planWhether the working time meets the requirement of daily working time, namely judging whether the working time meets +.>。
If no client in the cluster where the starting point is located meets the visit condition, the priority of all clients in other clusters is calculated, and one client which has the highest priority and can be visited is selected to join in the daily visit plan.
S106, calculating an optimal visit path and corresponding working time length for all clients in the current daily visit plan by adopting a path planning algorithm; if the working time length corresponding to the optimal visit path meets the requirement of daily working time length, ending the step and outputting a daily visit plan; if not, taking the last client of the optimal visit path as a starting point, selecting one client which has the highest priority and can be visited from the cluster where the client of the starting point is located to join the daily visit plan, and then repeatedly executing the step S106.
In this step S106, the path optimization algorithm may be used to recalculate the optimal visit route and the working time period T for the to-be-visited clients in the daily visit plan. The path optimization algorithm can adopt an ant colony algorithm, a particle swarm algorithm, a genetic algorithm and other optimization algorithms, and takes the ant colony algorithm as an example, the process of calculating the optimal path is as follows:
s1061, calculating the distance between every two adjacent visiting clients according to the visiting client data of the current working day.
S1062, initializing ant colony algorithm parameters including ant colony scale, pheromone factor, heuristic function factor, pheromone volatilization factor, pheromone constant, maximum iteration number and the like,
s1063, randomly placing ants at different departure points, and calculating the next visiting client for each ant until all clients are visited by the ants. And calculating the path length of each ant, recording the shortest path under the current iteration times, and updating the pheromone concentration on the path.
S1064, judging whether the iteration times are reached, if not, repeating the step S1063; if yes, the step is ended, and the calculated shortest path and the visit sequence of each customer on the shortest path are output.
By adopting the method, a daily visit plan of each working day can be generated. And sequencing the daily visit plans according to a time sequence to obtain a monthly visit plan.
The embodiment of the specification also provides a device for generating the customer visit plan, which is shown in fig. 2 and comprises:
the data acquisition module is configured to acquire the data of the salesman and the client data required to be visited by the salesman;
the visit calendar generation module is configured to generate a customer visit calendar based on the visit date selected by the user;
and the visit plan generation module is configured to generate a customer visit plan based on the salesman data, the customer data required to be visited by the salesman and the customer visit calendar, and comprises a daily visit plan and a month visit plan.
In order to achieve the above-mentioned customer visit plan generation method, the embodiment of the present disclosure further provides an electronic device, which includes a processor and a memory. The memory stores a computer program that is executed by the processor to implement the client call plan generation method of the embodiments of the present disclosure.
Specifically, the memory is used as a non-transitory computer readable storage medium, and can be used for storing a non-transitory software program, a non-transitory computer executable program and a module, such as a program instruction/module corresponding to the client visit plan generation method in the embodiment of the application. The processor may implement the client call plan generation method in embodiments of the present disclosure by running non-transitory software programs, instructions, and modules stored in memory. The memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include memory located remotely from the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor may be a general-purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a hardware chip, or any combination thereof; it may also be a digital signal processor (Digital Signal Processing, DSP), application specific integrated circuit (Application SpecificIntegrated Circut, ASIC), programmable logic device (programmable logic device, PLD), or a combination thereof. The PLD may be a complex programmable logic device (complex programmable logic device, CPLD), a field-programmable gate array (field-programmable gate array, FPGA), general-purpose array logic (genericarray logic, GAL), or any combination thereof.
The disclosed embodiments also provide a computer readable storage medium, such as a memory, including program code executable by a processor to perform the client call plan generation method of the above embodiments. For example, the computer readable storage medium may be Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), compact disc Read-Only Memory (CDROM), magnetic tape, floppy disk, optical data storage device, etc.
The disclosed embodiments also provide a computer program product comprising one or more program codes stored in a computer-readable storage medium. The processor of the electronic device reads the program code from the computer-readable storage medium and executes the program code to perform the steps of implementing the client call plan generation method provided in the above-described embodiment.
It should be understood that the structures illustrated in the embodiments of the present specification do not constitute a particular limitation on the apparatus of the embodiments of the present specification. In other embodiments of the specification, the apparatus may include more or less components than illustrated, or certain components may be combined, or certain components may be split, or different arrangements of components. The components illustrated may be in hardware, software
Or a combination of software and hardware.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, see the method embodiments for relevant points
And the like.
Those skilled in the art will appreciate that in one or more of the examples described above, the functions described in the present application may be implemented in hardware, software, a pendant, or any combination thereof. When implemented in software, these functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present application in further detail, and are not to be construed as limiting the scope of the application, but are merely intended to cover any modifications, equivalents, improvements, etc. based on the teachings of the application.
Claims (9)
1. A method of generating a customer visit plan, the method comprising:
s101, aiming at each salesman, acquiring client data required to be visited by the salesman, wherein the client data comprises the position of a client, the visit frequency and the last visit date;
s102, clustering the clients to be visited based on the client data to obtain at least one cluster;
s103, selecting the client with the largest number of times to be visited from all clients to be visited of each salesman as a starting point;
s104, judging whether the client serving as the starting point meets the visit condition, and if not, reselecting the client with the largest number of times to be visited from the rest clients to be visited as the starting point; if so, selecting one client which has the highest priority and can be visited from the cluster where the starting client is located to join in a daily visit plan;
the visit conditions are as follows:
wherein t is 0 Indicating the last visit date or last planned visit date, t 0 =0 indicates that the client has not been visited or has not been scheduled for a visit, t indicates the current date, M indicates the number of days of the month, and n indicates the frequency of the visit of the client;
s105, judging whether the working time required by the daily visit plan meets the daily working time requirement; if yes, executing step S106; if not, taking the newly added client as a starting point, selecting one client with the highest priority and capable of being visited from a cluster where the starting point client is located to add a daily visit plan, and then repeatedly executing the step S105;
s106, calculating an optimal visit path and corresponding working time length for all clients in the current daily visit plan by adopting a path planning algorithm; if the working time length corresponding to the optimal visit path meets the requirement of daily working time length, ending the step and outputting a daily visit plan; if not, taking the last client of the optimal visit path as a starting point, selecting one client which has the highest priority and can be visited from the cluster where the client of the starting point is located to join the daily visit plan, and then repeatedly executing the step S106.
2. The method according to claim 1, wherein the priority is calculated by:
wherein P represents a priority coefficient, the larger the value of P represents a higher priority, the ratio represents a priority weight coefficient, d represents the distance between the current client and the starting client, delta represents an intermediate parameter, and when t-t 0 Greater thanThe value of δ is 1 when, otherwise, the value of δ is 0.
3. The method of claim 1, wherein the time period required for the daily call plan is: and the sum of the visit duration of each client and the time consumption of the journey to each client in the daily visit plan.
4. The method as recited in claim 1, further comprising:
if the starting point client is not in the cluster in which the starting point client is located, selecting the client which has the highest priority and can be visited from the rest clients to be visited, and adding the client to the daily visit plan.
5. The method as recited in claim 1, further comprising:
s101 to S106 are executed for each weekday of each month, and a day visit plan for each weekday is obtained; and sequencing the daily visit plans according to the time sequence to obtain a month visit plan.
6. A customer visit plan generation system, comprising:
the data acquisition module is configured to acquire the data of the salesman and the client data required to be visited by the salesman;
the visit calendar generation module is configured to generate a customer visit calendar based on the visit date selected by the user;
a call plan generation module configured to generate a customer call plan using the method of any one of claims 1 to 5 based on the attendant data, customer data for the attendant's required call, and the customer call calendar.
7. The system of claim 6, wherein the customer data includes a location of the customer, a frequency of visits, a last date of visit.
8. A computer readable storage medium storing a computer program which, when executed by a processor, implements the method of any one of the preceding claims 1-5.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any of the preceding claims 1-5 when executing the program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310679235.7A CN116433245B (en) | 2023-06-09 | 2023-06-09 | Customer visit plan generation method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310679235.7A CN116433245B (en) | 2023-06-09 | 2023-06-09 | Customer visit plan generation method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116433245A CN116433245A (en) | 2023-07-14 |
| CN116433245B true CN116433245B (en) | 2023-09-15 |
Family
ID=87089377
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310679235.7A Active CN116433245B (en) | 2023-06-09 | 2023-06-09 | Customer visit plan generation method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116433245B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7363126B1 (en) * | 2002-08-22 | 2008-04-22 | United Parcel Service Of America | Core area territory planning for optimizing driver familiarity and route flexibility |
| KR101912590B1 (en) * | 2017-09-25 | 2018-10-29 | 남현명 | Method and System for optimizing the route for visiting customers |
| CN110363323A (en) * | 2018-11-20 | 2019-10-22 | 南京掌控网络科技有限公司 | A kind of Intelligent planning method, system and the equipment of periodicity customer visit route |
| CN110837917A (en) * | 2019-10-01 | 2020-02-25 | 榕知科技(武汉)有限公司 | Customer visit management system, method, terminal equipment and storage medium |
| CN112132547A (en) * | 2020-09-27 | 2020-12-25 | 杭州核盛网络科技有限公司 | Visit plan generation method and device, computer equipment and storage medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150371155A1 (en) * | 2014-06-24 | 2015-12-24 | Philippe Saint-Just | Method, compupter program, and system for planning, reserving, and purchasing travel accommodations from calendar events |
| JP2021047733A (en) * | 2019-09-19 | 2021-03-25 | 富士ゼロックス株式会社 | Information processing system and program |
-
2023
- 2023-06-09 CN CN202310679235.7A patent/CN116433245B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7363126B1 (en) * | 2002-08-22 | 2008-04-22 | United Parcel Service Of America | Core area territory planning for optimizing driver familiarity and route flexibility |
| KR101912590B1 (en) * | 2017-09-25 | 2018-10-29 | 남현명 | Method and System for optimizing the route for visiting customers |
| CN110363323A (en) * | 2018-11-20 | 2019-10-22 | 南京掌控网络科技有限公司 | A kind of Intelligent planning method, system and the equipment of periodicity customer visit route |
| CN110837917A (en) * | 2019-10-01 | 2020-02-25 | 榕知科技(武汉)有限公司 | Customer visit management system, method, terminal equipment and storage medium |
| CN112132547A (en) * | 2020-09-27 | 2020-12-25 | 杭州核盛网络科技有限公司 | Visit plan generation method and device, computer equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| "Mathematical model and exact algorithm for the home care worker scheduling and routing problem with lunch break requirements";Ran Liu 等;《 International Journal of Production Research》;第55卷(第2期);558-575 * |
| "医药代表客户拜访模式比较分析";孟繁瑜 等;《中国药业》;第26卷(第8期);69-72 * |
| 社区居家养老服务的预约调度与路径规划问题研究:基于改善蚁群算法;杨欣潼 等;《系统工程理论与实践》;第39卷(第05期);第1212-1224页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116433245A (en) | 2023-07-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kucharski et al. | Exact matching of attractive shared rides (ExMAS) for system-wide strategic evaluations | |
| Ota et al. | Stars: Simulating taxi ride sharing at scale | |
| CN1956457B (en) | Method and apparatus for arranging mesh work in mesh computing system | |
| US20210326788A1 (en) | On-demand resource scheduling | |
| Ozturk et al. | Extracting priority rules for dynamic multi-objective flexible job shop scheduling problems using gene expression programming | |
| KR102376755B1 (en) | A framework to optimize the selection of projects and the allocation of resources within a structured business organization under time, resource and budget constraints | |
| US20150120600A1 (en) | Time and location based delivery optimization | |
| CN108089921A (en) | Server for cloud big data operation architecture and operation resource optimization method thereof | |
| CN103092683A (en) | Scheduling used for analyzing data and based on elicitation method | |
| CN110826915B (en) | Resource scheduling method, device and computer readable storage medium | |
| CN106202092A (en) | The method and system that data process | |
| CN116934056B (en) | Vehicle scheduling method, device, computer equipment and storage medium | |
| Swathy et al. | Game theoretical approach for load balancing using SGMLB model in cloud environment | |
| CN103699443A (en) | Task distributing method and scanner | |
| Park et al. | Investigating a machine breakdown genetic programming approach for dynamic job shop scheduling | |
| CN106203717A (en) | Tax hall intelligent navigation method based on data analysis | |
| Fuller et al. | Learning-agent-based simulation for queue network systems | |
| CN116433245B (en) | Customer visit plan generation method and system | |
| Jouini et al. | Queueing systems with appointment-driven arrivals, non-punctual customers, and no-shows | |
| Blanghaps et al. | Sojourn time estimation in an M/G/∞ queue with partial information | |
| US11501247B1 (en) | Optimizing delivery routing using machine learning systems | |
| US9432218B2 (en) | Secure message delivery to a transient recipient in a routed network | |
| CN111861397A (en) | Intelligent scheduling platform for client visit | |
| CN110267717B (en) | Method and device for automatically generating automatic scaling call rules according to different independent tenants in multi-tenant environment | |
| CN110689163A (en) | Intelligent prediction method and system for cargo quantity during holidays |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |