CN117557337A - Method and device for dispatching degree - Google Patents
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Abstract
The application discloses a dispatching method and device. One embodiment of the method comprises the following steps: acquiring a service order of a user; determining a mapping matrix of a service party meeting service requirements of a user according to the service order, wherein the mapping matrix is used for representing service data of the service party; under the constraint of preset constraint conditions, a target service side for processing the service order is determined by adopting a simulated annealing algorithm according to the mapping matrix. The dispatching method for the service orders is provided based on the simulated annealing algorithm, so that the dependence of dispatching process on manpower and manual experience is reduced, and the efficiency and accuracy of dispatching process for the service orders are improved.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to the technical field of optimization algorithms and artificial intelligence, and especially relates to a monotone dispatching method, a monotone dispatching device, a computer readable medium and electronic equipment.
Background
With the wider and wider variety of commodities sold in the e-commerce platform, the method is not limited to physical commodities at present, and also comprises various virtual commodities, wherein various service commodities exist, such as services of television maintenance, furniture installation and the like, so that the concept of the service platform is derived. Numerous service parties hosting the service platform can provide the same service to consumers. When a plurality of service parties can provide the same service, the service platform needs to make a decision to determine that a specific service party is at a specific time point to provide corresponding service for consumers. At present, the number of service orders in the service platform is large, professional staff is required to send monotone, a large amount of manpower is wasted, and the rationality of personnel scheduling can also directly influence the experience of consumers.
Disclosure of Invention
The embodiment of the application provides a dispatching method, a dispatching device, a computer readable medium and electronic equipment.
In a first aspect, an embodiment of the present application provides a dispatch method, including: acquiring a service order of a user; determining a mapping matrix of a service party meeting service requirements of a user according to the service order, wherein the mapping matrix is used for representing service data of the service party; under the constraint of preset constraint conditions, a target service side for processing the service order is determined by adopting a simulated annealing algorithm according to the mapping matrix.
In some examples, determining the target service party for processing the service order by using the simulated annealing algorithm according to the mapping matrix under the constraint of the preset constraint condition includes: under the constraint of a preset constraint condition, determining an initial service party according to a mapping matrix of the service party meeting the service requirement of a user to obtain an initial solution; and adopting a simulated annealing algorithm to perform optimal solution exploration on the basis of the initial solution, and determining a target server.
In some examples, the above-mentioned adopting the simulated annealing algorithm performs the optimal solution exploration based on the initial solution, and determines the target service side, including: initializing a simulated annealing model according to the data complexity of the initial solution; iteratively executing the following operations until reaching a preset end condition, and determining a target server: for a plurality of service orders processed simultaneously, selecting a target service order from the service orders; transforming the current solution corresponding to the target service order to generate a neighborhood solution of the current solution corresponding to the target service order; determining an evaluation value of a current solution and an evaluation value of a neighborhood solution through a preset evaluation function; determining an updated current solution corresponding to the target service order from the current solution and the neighborhood solution according to the evaluation value of the current solution and the evaluation value of the neighborhood solution; and updating parameters of the simulated annealing model.
In some examples, determining the updated current solution corresponding to the target service order from the current solution and the neighborhood solution according to the evaluation value of the current solution and the evaluation value of the neighborhood solution includes: in response to determining that the evaluation value of the neighborhood solution is greater than the evaluation value of the current solution, the neighborhood solution is determined to correspond to the updated current solution of the target service order.
In some examples, determining the updated current solution corresponding to the target service order from the current solution and the neighborhood solution according to the evaluation value of the current solution and the evaluation value of the neighborhood solution includes: in response to determining that the evaluation value of the neighborhood solution is less than or equal to the evaluation value of the current solution, determining a difference between the evaluation value of the neighborhood solution and the evaluation value of the current solution; determining a neighborhood solution as a probability value corresponding to the updated current solution of the target service order according to the difference value and the temperature of the simulated annealing model; and determining an updated current solution corresponding to the target service order according to the probability value and the neighborhood solution.
In some examples, before determining the target service party for processing the service order by using the simulated annealing algorithm according to the mapping matrix under the constraint of the preset constraint condition, the method further includes: determining the priority of each constraint condition in preset constraint conditions according to the current service time period; and the evaluation function in the dispatch of the single degree process is constructed by the following modes: and constructing an evaluation function according to the preset constraint condition and the priority.
In some examples, determining the mapping matrix of the service party meeting the service requirement of the user according to the service order includes: determining a service party and service data of the service party meeting the service requirement of a user from a data storage system according to the service order; and constructing a mapping matrix according to the service data.
In some examples, the above method further comprises: and updating the data storage system according to the service data after the target service side processes the service order of the user.
In a second aspect, an embodiment of the present application provides a dispatch unit, including: an acquisition unit configured to acquire a service order of a user; a first determining unit configured to determine a mapping matrix of a service party satisfying a service requirement of a user according to a service order, wherein the mapping matrix is used for characterizing service data of the service party; and the second determining unit is configured to determine a target service party for processing the service order by adopting a simulated annealing algorithm according to the mapping matrix under the constraint of the preset constraint condition.
In some examples, the second determining unit is further configured to: under the constraint of a preset constraint condition, determining an initial service party according to a mapping matrix of the service party meeting the service requirement of a user to obtain an initial solution; and adopting a simulated annealing algorithm to perform optimal solution exploration on the basis of the initial solution, and determining a target server.
In some examples, the second determining unit is further configured to: initializing a simulated annealing model according to the data complexity of the initial solution; iteratively executing the following operations until reaching a preset end condition, and determining a target server: for a plurality of service orders processed simultaneously, selecting a target service order from the service orders; transforming the current solution corresponding to the target service order to generate a neighborhood solution of the current solution corresponding to the target service order; determining an evaluation value of a current solution and an evaluation value of a neighborhood solution through a preset evaluation function; determining an updated current solution corresponding to the target service order from the current solution and the neighborhood solution according to the evaluation value of the current solution and the evaluation value of the neighborhood solution; and updating parameters of the simulated annealing model.
In some examples, the second determining unit is further configured to: in response to determining that the evaluation value of the neighborhood solution is greater than the evaluation value of the current solution, the neighborhood solution is determined to correspond to the updated current solution of the target service order.
In some examples, the second determining unit is further configured to: in response to determining that the evaluation value of the neighborhood solution is less than or equal to the evaluation value of the current solution, determining a difference between the evaluation value of the neighborhood solution and the evaluation value of the current solution; determining a neighborhood solution as a probability value corresponding to the updated current solution of the target service order according to the difference value and the temperature of the simulated annealing model; and determining an updated current solution corresponding to the target service order according to the probability value and the neighborhood solution.
In some examples, the apparatus further comprises: a third determination unit configured to: under the constraint of preset constraint conditions, determining the priority of each constraint condition in the preset constraint conditions according to the current service time period before determining a target service side for processing a service order by adopting a simulated annealing algorithm according to a mapping matrix; and a construction unit configured to: the evaluation function in the dispatch process is constructed by the following steps: and constructing an evaluation function according to the preset constraint condition and the priority.
In some examples, the first determining unit is further configured to: determining a service party and service data of the service party meeting the service requirement of a user from a data storage system according to the service order; and constructing a mapping matrix according to the service data.
In some examples, the apparatus further comprises: an updating unit configured to: and updating the data storage system according to the service data after the target service side processes the service order of the user.
In a third aspect, embodiments of the present application provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements a method as described in any of the implementations of the first aspect.
In a fourth aspect, an embodiment of the present application provides an electronic device, including: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method as described in any of the implementations of the first aspect.
The dispatching method and the dispatching device provided by the embodiment of the application are used for acquiring the service order of the user; determining a mapping matrix of a service party meeting service requirements of a user according to the service order, wherein the mapping matrix is used for representing service data of the service party; under the constraint of preset constraint conditions, a target service side for processing a service order is determined by adopting a simulated annealing algorithm according to a mapping matrix, so that a dispatching method for the service order is provided based on the simulated annealing algorithm, the dependence of dispatching process on manpower and manual experience is reduced, and the efficiency and accuracy of dispatching process for the service order are improved.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings, in which:
FIG. 1 is an exemplary system architecture diagram in which an embodiment of the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a dispatch method according to the present application;
FIG. 3 is a schematic diagram of the structure and stored data of a data storage system according to the present application;
FIG. 4 is a schematic illustration of a computational flow of a simulated annealing algorithm according to the present application;
fig. 5 is a schematic diagram of an application scenario of the dispatch method according to the present embodiment;
FIG. 6 is a flow chart of yet another embodiment of a dispatch scheduling method according to the present application;
FIG. 7 is a block diagram of one embodiment of a dispatch scheduler in accordance with the present application;
FIG. 8 is a schematic diagram of a computer system suitable for use in implementing embodiments of the present application.
Detailed Description
The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
It should be noted that, in the technical solution of the present disclosure, the related aspects of collecting, updating, analyzing, processing, using, transmitting, storing, etc. of the personal information of the user all conform to the rules of the related laws and regulations, and are used for legal purposes without violating the public order colloquial. Necessary measures are taken for the personal information of the user, illegal access to the personal information data of the user is prevented, and the personal information security, network security and national security of the user are maintained.
FIG. 1 illustrates an exemplary architecture 100 in which the dispatch method and apparatus of the present application may be employed.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The communication connection between the terminal devices 101, 102, 103 constitutes a topology network, the network 104 being the medium for providing the communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The terminal devices 101, 102, 103 may interact with the server 105 through the network 104 to receive or transmit data or the like. The terminal devices 101, 102, 103 may be hardware devices or software supporting network connections for data interaction and data processing. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices supporting network connection, information acquisition, interaction, display, processing, etc., including, but not limited to, smartphones, car-mounted computers, tablet computers, electronic book readers, laptop and desktop computers, etc. When the terminal devices 101, 102, 103 are software, they can be installed in the above-listed electronic devices. It may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention is not particularly limited herein.
The server 105 may be a server that provides various services, for example, a background processing server that obtains a plurality of service orders sent by the terminal devices 101, 102, 103, and determines target service parties corresponding to the plurality of service orders by using a simulated annealing algorithm under the constraint of a preset constraint condition. As an example, the server 105 may be a cloud server.
The server may be hardware or software. When the server is hardware, the server may be implemented as a distributed server cluster formed by a plurality of servers, or may be implemented as a single server. When the server is software, it may be implemented as a plurality of software or software modules (e.g., software or software modules for providing distributed services), or as a single software or software module. The present invention is not particularly limited herein.
It should also be noted that, the dispatch method provided by the embodiments of the present application may be executed by a server, or may be executed by a terminal device, or may be executed by the server and the terminal device in cooperation with each other. Accordingly, each part (for example, each unit) included in the dispatch unit may be all provided in the server, all provided in the terminal device, or provided in the server and the terminal device, respectively.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. When the electronic device on which the dispatch method is operating does not require data transmission with other electronic devices, the system architecture may include only the electronic device (e.g., server or terminal device) on which the dispatch method is operating.
With continued reference to FIG. 2, a flow 200 of one embodiment of a dispatch method is shown, comprising the steps of:
step 201, a service order of a user is obtained.
In this embodiment, the execution subject of the dispatch method (such as the terminal device or the server in fig. 1) may acquire the service order of the user from a remote location or from a local location through a wired network connection or a wireless network connection.
The service order is a virtual commodity order placed by the user through the service platform and representing the service requirements of the user, including but not limited to user information, order type, order requirements and the like.
It should be noted that, the executing body may acquire multiple service orders of multiple users at the same time.
Step 202, determining a mapping matrix of the service side meeting the service requirement of the user according to the service order.
In this embodiment, the executing body may determine, according to the service order, a mapping matrix of the service party that meets the service requirement of the user. Wherein the mapping matrix is used to characterize the service data of the service party.
As an example, the data storage system stores mapping matrices corresponding to all the service parties in the service platform. Firstly, the execution main body analyzes a service order of a user and determines the service requirement of the user; and then, screening all the service parties according to the service request of the user to obtain the service party meeting the service requirement of the user, and further determining the mapping matrix of the service party meeting the service requirement of the user.
With continued reference to FIG. 3, a schematic diagram of the structure and stored data of a data storage system 300 is shown. The data storage system 300 includes a service contract data module 301, a service area information module 302, and a historical performance information module 303. The service contract data module 301, the service area information module 302, and the history performance information module 303 store real-time service contract data, history performance data, and service area information data of corresponding service parties (service providers and service staff), respectively, for matching screening of service providers or service staff, algorithm data matrix construction, and the like. Accordingly, the data in each module of the data storage system 300 can be finely divided into various types of data in fig. 3.
In some optional implementations of this embodiment, the executing body may execute the step 202 as follows:
first, service data of a service party and a service party meeting service requirements of a user are determined from a data storage system according to a service order.
As an example, after receiving the front-end service order request, the executing body first queries the service contract data module for the service provider ID that can provide the service according to the information such as the merchant ID (Identity document, identity), the service ID, etc. in the service order; then, inquiring information such as regional quotations and the like corresponding to the type of service in a service regional information module through the service ID; finally, the history performance information of the screened service side (service provider or service person) is obtained through a history performance information module.
Second, a mapping matrix is constructed from the service data.
In this implementation manner, the execution body may determine feature data of the service party from the service data, so as to construct a mapping matrix between the service ID and the feature data of the service party.
In the implementation manner, a construction manner of the mapping matrix is provided, and when the information processing flow of the server side is triggered, the mapping matrix can be flexibly and rapidly constructed according to the service data of the server side stored in the data storage system.
Step 203, determining a target service party for processing the service order by adopting a simulated annealing algorithm according to the mapping matrix under the constraint of the preset constraint condition.
In this embodiment, the execution body may determine, according to the mapping matrix, a target service party for processing the service order by using a simulated annealing algorithm under the constraint of a preset constraint condition.
For the dispatch of service orders, it can be abstracted into a mathematical combination optimization problem with multiple objectives as follows:
1. service side historical performance factor
Through the historical orders served by the service side (service provider or service personnel), historical behavior data such as order receiving time efficiency, finishing time efficiency, gate-on accuracy, poor evaluation rate and the like of each service side can be counted, and the service side with the optimal historical behavior is selected, so that the service experience of a user is improved.
2. Cost factor of service platform
Different service parties may provide the same type of service, but the service offers of different service parties may not be exactly the same. Therefore, in dispatching a single-service decision, a lower-price service party among the performable service parties needs to be selected, so that the operation cost of the service platform is reduced.
3. Service efficiency and user experience factors
When the user has multiple service demands at the same time, the user is provided with services through fewer service parties as much as possible, the number of the service personnel to get on the door is reduced, more services are provided through one-time process of getting on the door as much as possible, the working efficiency of the service personnel is improved, and the service experience of the user is improved.
4. Service capability factor of service provider
There is an upper threshold for the number of services available on a single day by a service provider or the number of orders available on a single day by a service person, there are constraints on the types of services available, the time range over which the services can be provided, and the like, and each service person cannot provide a plurality of services in a single time period, and these constraints need to be satisfied when scheduling the service person.
By referring to the solution thought of abstracting the dispatch model problem into the mathematical combination optimization problem, corresponding constraint conditions can be respectively constructed in the aspects of service side historical performance factors, service platform cost factors, service efficiency and user experience factors, service provider service capability factors and the like, so that preset constraint conditions corresponding to the dispatch model problem of the service order are obtained.
In this embodiment, the execution body may use the mapping matrix of the service party that meets the service requirement of the user as an input of a simulated annealing model that adopts a simulated annealing algorithm, and determine, from the service parties that meet the service requirement of the user, the target service party for processing the service order of the user through the simulated annealing model under the constraint of a preset constraint condition.
In some optional trial manners of this embodiment, the execution body may execute the step 203 as follows:
first, under the constraint of preset constraint conditions, determining an initial service party according to a mapping matrix of the service party meeting the service requirement of a user, and obtaining an initial solution.
As an example, for a service party that meets the service requirement of the user, the execution body may further filter the service party under the constraint of the preset constraint condition, determine the service party that meets the preset constraint condition as an initial service party of the user, and obtain an initial solution corresponding to the service order of the user.
Secondly, adopting a simulated annealing algorithm to perform optimal solution exploration on the basis of the initial solution, and determining a target server.
In this implementation manner, the execution body may take the mapping matrix of the initial service party as input of the simulated annealing model that adopts the simulated annealing algorithm, so that the simulated annealing model performs optimal solution exploration on the basis of the initial solution, and determines the target service party.
In the implementation mode, the accuracy of the finally determined target service side is improved while the calculation speed is improved based on the mode that the initial solution is determined first and then the optimal solution is explored.
With continued reference to FIG. 4, a schematic of a computational flow diagram of a simulated annealing algorithm is shown.
In some optional implementations of this embodiment, the executing body may execute the second step by:
and 2.1, initializing a simulated annealing model according to the data complexity of the initial solution.
The executing body needs to analyze the complexity of service order data, such as the number of orders, the total number of optional service providers or service staff, the maximum number of orders that can be completed by a single service provider or service staff, and the like, and determine the data complexity of the initial solution; furthermore, related parameters of the simulated annealing model such as initial temperature, cooling coefficient, maximum iteration number and the like are dynamically initialized according to the data complexity, so that the balance between the accuracy and decision efficiency of the simulated annealing algorithm under different data complexity is ensured.
2.2, iteratively executing the following operations until a preset end condition is reached, and determining a target server:
2.21, selecting a target service order from a plurality of service orders processed simultaneously.
In this implementation manner, the executing body may randomly select one service order from the plurality of service orders processed at the same time as the target service order.
And 2.22, transforming the current solution corresponding to the target service order, and generating a neighborhood solution of the current solution corresponding to the target service order.
As an example, values on the current solution of the target service order are randomly replaced according to a mapping matrix of the selectable service side corresponding to the target service order, thereby generating a corresponding new neighborhood solution on the current solution.
2.23, determining the evaluation value of the current solution and the evaluation value of the neighborhood solution through a preset evaluation function.
The preset evaluation function is used for evaluating the quality of the current solution and guiding the searching process of the simulated annealing algorithm to be conducted towards a better direction. The evaluation function is typically designed according to the specific requirements of the problem, aiming at enabling the algorithm to find the optimal solution or near optimal solution of the problem.
In this implementation manner, the execution subject may use the following evaluation function:
wherein S is a solution of the combined optimization problem, e is a weight of each service efficiency influence factor, x is a numerical representation of each efficiency influence factor, h is a weight of each service provider or service person 'S historical performance influence factor, y is a numerical representation of each service provider or service person' S historical performance, p is a weight of each price cost influence factor, and z is a numerical representation of each price cost influence factor.
The execution subject can respectively determine the evaluation value of the current solution and the evaluation value of the neighborhood solution through a preset evaluation function.
And 2.24, determining an updated current solution corresponding to the target service order from the current solution and the neighborhood solution according to the evaluation value of the current solution and the evaluation value of the neighborhood solution.
The execution body may compare the evaluation value of the current solution with the evaluation value of the neighborhood solution, and determine, according to the comparison result, an updated current solution corresponding to the target service order from the current solution and the neighborhood solution.
And 2.25, updating parameters of the simulated annealing model.
In this implementation, the parameters of the updated simulated annealing model include parameters such as temperature, iteration number, and preset end conditions.
For temperature, the algorithm is typically set to a higher value at the beginning and then gradually decreases during each iteration. The temperature update may use different strategies such as linear cooling, exponential cooling, etc. The specific temperature update strategy can be adjusted according to the nature and requirements of the problem. For the number of iterations, in a simulated annealing algorithm, the number of iterations for each temperature value is typically a fixed value. The execution body can adjust the iteration times according to the characteristics and requirements of the problem. Increasing the number of iterations may allow the algorithm to more fully search the solution space, but may also increase the runtime of the algorithm. For a preset end condition, the end condition of the simulated annealing algorithm is usually taken as the termination algorithm when none of a number of new domain solutions are accepted. In addition, the preset end condition may be adjusted according to the characteristics and requirements of the problem, for example, the maximum iteration number is set, a certain target value is reached, and the like as the preset end condition.
In the implementation manner, a specific manner for solving the dispatching problem of the service order by the simulated annealing algorithm is provided, and the speed and accuracy of the calculation process are improved.
In some optional implementations of this embodiment, the executing body may execute the step 2.24 as follows:
in response to determining that the evaluation value of the neighborhood solution is greater than the evaluation value of the current solution, the neighborhood solution is determined to correspond to the updated current solution of the target service order.
ΔE(S)＝E(S new )-E(S)
Wherein E (S) new ) Characterizing the evaluation value of the neighborhood solution, E (S) characterizing the evaluation value of the current solution, and DeltaE (S) characterizing the difference between the evaluation value of the neighborhood solution and the evaluation value of the current solution.
When Δe (S) is greater than 0, the neighborhood solution is determined to correspond to the updated current solution for the target service order, i.e., the current solution for the target service order during the next iterative calculation.
In some optional implementations of this embodiment, the executing body may execute the step 2.24 as follows:
first, in response to determining that the evaluation value of the neighborhood solution is equal to or less than the evaluation value of the current solution, a difference between the evaluation value of the neighborhood solution and the evaluation value of the current solution is determined.
In this implementation, the execution subject may determine the difference Δe (S) by the formula.
Then, a probability value is determined for determining the neighborhood solution as the updated current solution corresponding to the target service order based on the difference and the temperature of the simulated annealing model.
As an example, the executive described above may determine a neighborhood solution as a probability value corresponding to the updated current solution of the target service order by:
wherein T represents the temperature of the simulated annealing model, and DeltaE (S) represents the difference between the evaluation value of the neighborhood solution and the evaluation value of the current solution.
Finally, an updated current solution corresponding to the target service order is determined based on the probability value and the neighborhood solution.
Receiving new domain solutions with a certain probability means selecting by a certain probability under the current solution state SWhether or not to accept a worse field solution S than the current solution S new . This probability generally decreases gradually over time, so the probability of accepting a worse solution early is higher, while over time the probability of accepting a worse solution decreases gradually.
This mechanism of accepting poor solutions with a certain probability is to avoid the simulated annealing algorithm from prematurely sinking into the locally optimal solution. If the simulated annealing algorithm only accepts solutions better than the current solution during the search process, it may search repeatedly around a locally optimal solution and not jump out of the locally optimal solution. By accepting a worse solution with a certain probability, the simulated annealing algorithm has the opportunity to jump out of the locally optimal solution to explore a wider solution space, thereby finding a globally optimal solution.
With continued reference to fig. 5, fig. 5 is a schematic diagram 500 of an application scenario of the dispatch method according to the present embodiment. In the application scenario of fig. 5, a plurality of users 501 send service orders to a service platform 503 through respective mobile terminals 502. After obtaining a service order of a user, the service platform 503 firstly determines a mapping matrix of a service party meeting service requirements of the user from a data storage system according to the service order, wherein the mapping matrix is used for representing service data of the service party; then, under the constraint of preset constraint conditions, a target service party for processing the service order is determined by adopting a simulated annealing algorithm according to the mapping matrix.
The method provided by the embodiment of the application comprises the steps of obtaining a service order of a user; determining a mapping matrix of a service party meeting service requirements of a user according to the service order, wherein the mapping matrix is used for representing service data of the service party; under the constraint of preset constraint conditions, a target service side for processing a service order is determined by adopting a simulated annealing algorithm according to a mapping matrix, so that a dispatching method for the service order is provided based on the simulated annealing algorithm, the dependence of dispatching process on manpower and manual experience is reduced, and the efficiency and accuracy of dispatching process for the service order are improved.
In some optional implementations of this embodiment, before the executing body executes the step 203, the following operations may be further performed: and determining the priority of each constraint condition in the preset constraint conditions according to the current service time period.
In this implementation manner, the priorities of the constraint conditions in terms of order quotation, historical performance, service efficiency and the like are ordered according to the current service time period, or whether certain factors are considered as constraint conditions, for example, if the service side reaches the order receiving threshold, the constraint conditions related to the capacity of the service side need to be added in the constraint conditions, so that the weight value of each constraint condition is generated.
As an example, the service period may be divided into a promotion period, a general period. The priority of each constraint condition corresponding to the promotion time period is different from that of the ordinary time period. Of course, the execution body may further divide the service period in more detail according to actual needs.
In this implementation manner, the execution body may construct the evaluation function in the dispatch process in the following manner: and constructing an evaluation function according to the preset constraint condition and the priority.
Continuing taking the formula corresponding to the evaluation function as an example, the priority of each preset condition can be represented by the weight e of each service efficiency influence factor, the weight h of each service provider or service personnel history performance influence factor and the weight p of each price cost influence factor; furthermore, preset constraint conditions and priorities are collected, and an evaluation function is constructed.
In the implementation manner, the priority information of the preset constraint condition is introduced into the evaluation function, so that the evaluation function can reflect the requirements of different time periods on different priorities of the preset condition, and the accuracy of the evaluation function is further improved.
In some optional implementations of this embodiment, the foregoing execution body may further perform the following operations:
and updating the data storage system according to the service data after the target service side processes the service order of the user.
After determining the target service party of the user, the executing body may feed back the service order of the user to the target service party and feed back the target service party information to the user. After the target service side processes the service order of the user, the execution body may acquire service data of the target service side for the processed service order, thereby updating the data storage system. In addition, the related data change carried out by the service provider or the service personnel is also carried out in real-time data synchronization in the data storage system.
With continued reference to fig. 6, there is shown an illustrative flow 600 in accordance with yet another embodiment of the dispatch method of the present application, including the steps of:
step 601, a service order for a user is obtained.
Step 602, determining a mapping matrix of a service party meeting service requirements of a user according to a service order.
Wherein the mapping matrix is used to characterize the service data of the service party.
Step 603, determining an initial server according to the mapping matrix of the server meeting the service requirement of the user under the constraint of the preset constraint condition, and obtaining an initial solution.
Step 604, initializing a simulated annealing model according to the data complexity of the initial solution.
Step 605, iteratively executing the following operations until reaching a preset end condition, and determining a target server:
in step 6051, for a plurality of service orders processed simultaneously, a target service order is selected therefrom.
Step 6052, transforming the current solution corresponding to the target service order, and generating a neighborhood solution of the current solution corresponding to the target service order.
Step 6053, determining the evaluation value of the current solution and the evaluation value of the neighborhood solution through a preset evaluation function.
Step 6054, determining an updated current solution corresponding to the target service order from the current solution and the neighborhood solution according to the evaluation value of the current solution and the evaluation value of the neighborhood solution.
In step 6055, parameters of the simulated annealing model are updated.
As can be seen from this embodiment, compared with the embodiment corresponding to fig. 2, the flowchart 600 of the dispatch method in this embodiment specifically illustrates the calculation process of the simulated annealing model, which further improves the efficiency and accuracy of the dispatch process for service orders.
With continued reference to fig. 7, as an implementation of the method shown in the foregoing figures, the present application provides an embodiment of a dispatch device, which corresponds to the method embodiment shown in fig. 2, and which may be specifically applied to various electronic devices.
As shown in fig. 7, the dispatch unit 700 includes: an acquisition unit 701 configured to acquire a service order of a user; a first determining unit 702 configured to determine, according to the service order, a mapping matrix of the service party satisfying the service requirement of the user, wherein the mapping matrix is used for characterizing service data of the service party; the second determining unit 703 is configured to determine, under the constraint of the preset constraint condition, a target service party for processing the service order by using a simulated annealing algorithm according to the mapping matrix.
In some optional implementations of this embodiment, the second determining unit 703 is further configured to: under the constraint of a preset constraint condition, determining an initial service party according to a mapping matrix of the service party meeting the service requirement of a user to obtain an initial solution; and adopting a simulated annealing algorithm to perform optimal solution exploration on the basis of the initial solution, and determining a target server.
In some optional implementations of this embodiment, the second determining unit 703 is further configured to: initializing a simulated annealing model according to the data complexity of the initial solution; iteratively executing the following operations until reaching a preset end condition, and determining a target server: for a plurality of service orders processed simultaneously, selecting a target service order from the service orders; transforming the current solution corresponding to the target service order to generate a neighborhood solution of the current solution corresponding to the target service order; determining an evaluation value of a current solution and an evaluation value of a neighborhood solution through a preset evaluation function; determining an updated current solution corresponding to the target service order from the current solution and the neighborhood solution according to the evaluation value of the current solution and the evaluation value of the neighborhood solution; and updating parameters of the simulated annealing model.
In some optional implementations of this embodiment, the second determining unit 703 is further configured to: in response to determining that the evaluation value of the neighborhood solution is greater than the evaluation value of the current solution, the neighborhood solution is determined to correspond to the updated current solution of the target service order.
In some optional implementations of this embodiment, the second determining unit 703 is further configured to: in response to determining that the evaluation value of the neighborhood solution is less than or equal to the evaluation value of the current solution, determining a difference between the evaluation value of the neighborhood solution and the evaluation value of the current solution; determining a neighborhood solution as a probability value corresponding to the updated current solution of the target service order according to the difference value and the temperature of the simulated annealing model; and determining an updated current solution corresponding to the target service order according to the probability value and the neighborhood solution.
In some optional implementations of this embodiment, the apparatus further includes: a third determination unit (not shown in the figure) configured to: under the constraint of preset constraint conditions, determining the priority of each constraint condition in the preset constraint conditions according to the current service time period before determining a target service side for processing a service order by adopting a simulated annealing algorithm according to a mapping matrix; and a construction unit (not shown in the figure) configured to: the evaluation function in the dispatch process is constructed by the following steps: and constructing an evaluation function according to the preset constraint condition and the priority.
In some optional implementations of this embodiment, the first determining unit 702 is further configured to: determining a service party and service data of the service party meeting the service requirement of a user from a data storage system according to the service order; and constructing a mapping matrix according to the service data.
In some optional implementations of this embodiment, the apparatus further includes: an updating unit (not shown in the figure) configured to: and updating the data storage system according to the service data after the target service side processes the service order of the user.
In this embodiment, an acquiring unit in the dispatch center device acquires a service order of a user; the first determining unit determines a mapping matrix of a service party meeting the service requirement of the user according to the service order, wherein the mapping matrix is used for representing service data of the service party; the second determining unit determines a target service party for processing the service order by adopting a simulated annealing algorithm according to the mapping matrix under the constraint of the preset constraint condition, therefore, the dispatching method and the dispatching device for the service orders are provided based on the simulated annealing algorithm, the dependence of dispatching process on manpower and manual experience is reduced, and the efficiency and accuracy of dispatching process for the service orders are improved.
Referring now to FIG. 8, there is illustrated a schematic diagram of a computer system 800 suitable for use in implementing the apparatus of embodiments of the present application (e.g., apparatus 101, 102, 103, 105 illustrated in FIG. 1). The apparatus shown in fig. 8 is merely an example, and should not be construed as limiting the functionality and scope of use of the embodiments herein.
As shown in fig. 8, the computer system 800 includes a processor (e.g., CPU, central processing unit) 801 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data required for the operation of the system 800 are also stored. The processor 801, the ROM802, and the RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.
The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, mouse, etc.; an output portion 807 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 808 including a hard disk or the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. The drive 810 is also connected to the I/O interface 805 as needed. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as needed so that a computer program read out therefrom is mounted into the storage section 808 as needed.
In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section 809, and/or installed from the removable media 811. The above-described functions defined in the methods of the present application are performed when the computer program is executed by the processor 801.
It should be noted that the computer readable medium of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present application may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the client computer, partly on the client computer, as a stand-alone software package, partly on the client computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the client computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware. The described units may also be provided in a processor, for example, described as: a processor includes an acquisition unit, a first determination unit, and a second determination unit. The names of these units do not constitute a limitation on the unit itself in some cases, for example, the second determining unit may also be described as "a unit for determining a target service party for processing a service order using a simulated annealing algorithm according to a mapping matrix under the constraint of a preset constraint condition".
As another aspect, the present application also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the computer device to: acquiring a service order of a user; determining a mapping matrix of a service party meeting service requirements of a user according to the service order, wherein the mapping matrix is used for representing service data of the service party; under the constraint of preset constraint conditions, a target service side for processing the service order is determined by adopting a simulated annealing algorithm according to the mapping matrix.
The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the invention referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or equivalents thereof is possible without departing from the spirit of the invention. Such as the above-described features and technical features having similar functions (but not limited to) disclosed in the present application are replaced with each other.
Claims (11)
1. A dispatch method comprising:
acquiring a service order of a user;
determining a mapping matrix of a service party meeting the service requirement of the user according to the service order, wherein the mapping matrix is used for representing service data of the service party;
and under the constraint of a preset constraint condition, determining a target service side for processing the service order by adopting a simulated annealing algorithm according to the mapping matrix.
2. The method of claim 1, wherein the determining, under the constraint of the preset constraint condition, the target service party for processing the service order using a simulated annealing algorithm according to the mapping matrix comprises:
Under the constraint of the preset constraint condition, determining an initial service party according to a mapping matrix of the service party meeting the service requirement of the user to obtain an initial solution;
and adopting the simulated annealing algorithm to perform optimal solution exploration on the basis of the initial solution, and determining the target server.
3. The method of claim 2, wherein the employing the simulated annealing algorithm to perform optimal solution exploration based on the initial solution, determining the target server comprises:
initializing a simulated annealing model according to the data complexity of the initial solution;
iteratively executing the following operations until reaching a preset end condition, and determining the target server:
for a plurality of service orders processed simultaneously, selecting a target service order from the service orders;
transforming the current solution corresponding to the target service order to generate a neighborhood solution of the current solution corresponding to the target service order;
determining an evaluation value of the current solution and an evaluation value of the neighborhood solution through a preset evaluation function;
determining an updated current solution corresponding to the target service order from the current solution and the neighborhood solution according to the evaluation value of the current solution and the evaluation value of the neighborhood solution;
And updating parameters of the simulated annealing model.
4. The method of claim 3, wherein the determining an updated current solution corresponding to the target service order from the current solution and the neighborhood solution based on the evaluation value of the current solution and the evaluation value of the neighborhood solution comprises:
in response to determining that the evaluation value of the neighborhood solution is greater than the evaluation value of the current solution, the neighborhood solution is determined to correspond to the updated current solution of the target service order.
5. The method of claim 3, wherein the determining an updated current solution corresponding to the target service order from the current solution and the neighborhood solution based on the evaluation value of the current solution and the evaluation value of the neighborhood solution comprises:
determining a difference value between the evaluation value of the neighborhood solution and the evaluation value of the current solution in response to determining that the evaluation value of the neighborhood solution is less than or equal to the evaluation value of the current solution;
determining the neighborhood solution as a probability value corresponding to the updated current solution of the target service order according to the difference value and the temperature of the simulated annealing model;
and determining an updated current solution corresponding to the target service order according to the probability value and the neighborhood solution.
6. A method according to claim 3, wherein prior to said determining a target service party for processing said service order using a simulated annealing algorithm in accordance with said mapping matrix under the constraints of preset constraints, further comprising:
determining the priority of each constraint condition in the preset constraint conditions according to the current service time period; and
the evaluation function in the dispatch process is constructed in the following way:
and constructing the evaluation function according to the preset constraint condition and the priority.
7. The method of claim 1, wherein the determining a mapping matrix of a service party that meets the service requirements of the user from the service order comprises:
determining a service party meeting the service requirement of the user and service data of the service party from a data storage system according to the service order;
and constructing the mapping matrix according to the service data.
8. The method of claim 7, further comprising:
and updating the data storage system according to the service data of the target service side after the service order of the user is processed.
9. A dispatch scheduler comprising:
An acquisition unit configured to acquire a service order of a user;
a first determining unit configured to determine a mapping matrix of a service party satisfying a service requirement of the user according to the service order, wherein the mapping matrix is used for characterizing service data of the service party;
and the second determining unit is configured to determine a target server for processing the service order by adopting a simulated annealing algorithm according to the mapping matrix under the constraint of a preset constraint condition.
10. A computer readable medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any of claims 1-8.
11. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-8.
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