CN110991846B - Service personnel task allocation method, device, equipment and storage medium - Google Patents
Service personnel task allocation method, device, equipment and storage medium Download PDFInfo
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- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
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- G—PHYSICS
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- 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
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention discloses a service personnel task allocation method, a device, equipment and a storage medium, wherein the service personnel task allocation method is used for acquiring order information and service personnel information, establishing a service personnel selection model based on a greedy algorithm according to the order information and the service personnel information, inputting the order information and the service personnel information into the service personnel selection model, and allocating tasks to appointed service personnel through the service personnel selection model; the technical problems of low efficiency and unbalanced service personnel workload caused by manual distribution of service personnel task distribution in the prior art are solved, and an automatic, reasonable and efficient service personnel task distribution method is provided.
Description
Technical Field
The present invention relates to the field of personnel task scheduling technologies, and in particular, to a method, an apparatus, a device, and a storage medium for distributing a service personnel task.
Background
With the rapid development of social economy and the continuous improvement of national living standard, the service requirements of people on various aspects in work and life are also continuously improved, for example, the transportation service industries such as aviation transportation, railway transportation and the like are also continuously subjected to technical reform and service upgrading, and the best service quality is provided for consumers.
As the requirements of people on services are continuously improved, airport honored guest halls and the like are increasingly favored by consumer groups pursuing individuation and comfort, and as consumers are continuously increased, higher service efficiency requirements are provided for the running efficiency of the honored guest halls.
Because task allocation of service personnel in a honored guest hall still stays in relying on a pipeline operator to carry out manual allocation, the problems of incomplete data acquisition and unbalanced workload of the service personnel are caused, and the problems of inefficiency of task allocation and poor consumer experience are caused.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. For this purpose, the invention provides an automatic, reasonable and efficient service personnel task allocation method.
In a first aspect, an embodiment of the present invention provides a service person task allocation method, including:
acquiring order information and service personnel information;
establishing a service person selection model according to the order information and the service person information based on a greedy algorithm;
and inputting the order information and the attendant information into the attendant selection model, and distributing tasks to designated attendant through the attendant selection model.
The service personnel task allocation method provided by the embodiment of the invention has at least the following beneficial effects:
according to the service personnel task allocation method, an order information and service personnel information are obtained, a service personnel selection model is built according to the order information and the service personnel information based on a greedy algorithm, the order information and the service personnel information are input into the service personnel selection model, and tasks are allocated to appointed service personnel through the service personnel selection model; the technical problems of low efficiency and unbalanced service personnel workload caused by manual distribution of service personnel task distribution in the prior art are solved, and an automatic, reasonable and efficient service personnel task distribution method is provided.
According to further embodiments of the present invention, the order information includes at least one service item, and a service person number for each of the service items.
According to other embodiments of the present invention, the service personnel task allocation method includes: the service personnel ID, the service statistics corresponding to the service personnel ID, the current service quantity in progress corresponding to the service personnel ID and the service upper limit quantity corresponding to the service personnel ID at the same time.
According to other embodiments of the present invention, the method for assigning service personnel tasks based on a greedy algorithm, for creating a service personnel selection model according to the order information and the service personnel information, specifically includes:
establishing an objective function:
the constraints of the objective function include a first constraint:
Sr_c jt ≤Sr_l ij (2)
second constraint:
wherein Od is ni The number of service personnel for the ith service item in the nth order; sr (Sr) ij The service statistics of the jth service personnel in the ith service item;
average workload for service personnel for the ith service item; sr_c jt The current service quantity of the jth attendant at the t moment is obtained; sr_l ij The upper number of services for the jth attendant; ar (Ar) ni The attendant ID is arranged for the ith service in the nth order.
According to other embodiments of the present invention, the inputting the order information and the attendant information into the attendant selection model, and assigning the task to the designated attendant through the attendant selection model specifically includes:
obtaining the service personnel ID with the minimum service statistics according to the objective function, namely obtaining a target service personnel ID;
acquiring the current service quantity corresponding to the target service personnel ID according to the target service personnel ID, namely acquiring the current service quantity of the target service personnel;
acquiring the service upper limit number corresponding to the target service personnel ID according to the target service personnel ID, namely acquiring the service upper limit number of the target service personnel;
judging whether the current service quantity of the target service personnel meets or not more than the service upper limit quantity of the target service personnel;
if not, eliminating the target service personnel ID from the task allocation of the service personnel, and repeatedly acquiring the target service personnel ID;
if yes, distributing tasks to the target service personnel ID, and updating the current service quantity and the service statistics corresponding to the target service personnel ID;
judging whether the number of the IDs of the target service personnel allocated with the tasks meets the number of the service personnel or not;
if not, returning to continuously acquire the ID of the target service person;
and if so, outputting the target service personnel ID.
In a second aspect, an embodiment of the present invention provides a service person task allocation apparatus, including:
the information acquisition module is used for acquiring order information and service personnel information;
the model building module is used for building a service person selection model according to the order information and the service person information based on a greedy algorithm;
and the task distribution module is used for inputting the order information and the attendant information into the attendant selection model, and distributing the task to the appointed attendant through the attendant selection model.
In a third aspect, an embodiment of the present invention provides a service person task allocation apparatus, including:
at least one processor, and,
a memory communicatively coupled to the at least one processor, wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the attendant task allocation method.
In a fourth aspect, one embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the attendant task allocation method.
Drawings
FIG. 1 is a flowchart of a method for distributing tasks among service personnel according to an embodiment of the present invention;
FIG. 2 is a flowchart of another embodiment of a method for distributing tasks among service personnel according to an embodiment of the present invention;
FIG. 3 is a block diagram of an embodiment of a service person task allocation apparatus according to an embodiment of the present invention.
Detailed Description
The conception and the technical effects produced by the present invention will be clearly and completely described in conjunction with the embodiments below to fully understand the objects, features and effects of the present invention. It is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and that other embodiments obtained by those skilled in the art without inventive effort are within the scope of the present invention based on the embodiments of the present invention.
In the description of the embodiments of the present invention, if "several" is referred to, it means more than one, if "multiple" is referred to, it is understood that the number is not included if "greater than", "less than", "exceeding", and it is understood that the number is included if "above", "below", "within" is referred to. If reference is made to "first," "second," "third," etc., it is to be understood as being used for distinguishing between technical features and not as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
Embodiment one:
referring to fig. 1, in an embodiment of the present invention, a service person task allocation method includes the steps of:
s100, order information and service personnel information are acquired;
in some embodiments, the order information is a service requirement from a consumer, one order information includes one or more service items, the order information also includes a corresponding number of service personnel in each service item, the determined number of service personnel is for better and more reasonable arrangement according to the current service personnel information, for example, the unreasonable task allocation situation is eliminated, the number of service personnel of one service item is 3, and the number of service personnel capable of allocating tasks in the current acquired service personnel information is only two, in this case, the task allocation of service personnel can be omitted, and the task allocation efficiency is improved because the task allocation is required to be performed after waiting for acquiring more service personnel information.
In the embodiment of the invention, the service personnel information comprises service personnel IDs, service statistics corresponding to the service personnel IDs, current service quantity corresponding to the service personnel IDs and service upper limit quantity corresponding to the service personnel IDs at the same time. The service personnel ID corresponds to one service personnel one by one, the number of service personnel capable of distributing tasks at present can be obtained from the service personnel ID, the service statistics comprise the sum of the number of orders completed by the service personnel on the same day and the current service number, when tasks are distributed to one service personnel, the service statistics are compared with the set service upper limit number, if the service statistics are larger than the service upper limit number, the task distribution is canceled, the task distribution is performed again, other service personnel are designated, and the aim of preventing excessive tasks from being distributed to the same service personnel and causing excessive workload of the service personnel is achieved.
S200, a service personnel selection model is established according to order information and service personnel information based on a greedy algorithm;
in the embodiment of the invention, a task personnel selection model is established based on a greedy algorithm. The task allocation problem of the service personnel is actually based on the optimal strategy for assigning the task to the service personnel in the current resource state, and the task allocation problem has high randomness and high real-time requirement, so that the implementation condition of a greedy algorithm is met. The greedy algorithm means that the overall optimal solution of the problem sought can be achieved by a series of locally optimal choices, i.e., greedy choices. This is the first basic element that the greedy algorithm is viable and is the main difference between greedy and dynamic programming algorithms. Greedy selection is a sequential selection from top to bottom in an iterative manner, each time greedy selection is made, the problem is reduced to a sub-problem of smaller size, and for a particular problem, to determine whether it has greedy selection properties, we must demonstrate that greedy selection made at each step ultimately yields the best solution to the problem. It is generally possible to first prove that an overall optimal solution to the problem starts with greedy selection and, after greedy selection, the original problem is reduced to a similar sub-problem of smaller scale. Then, the mathematical induction method proves that through greedy selection of each step, an overall optimal solution of the problem can be finally obtained.
In the embodiment of the invention, after order information and service personnel information are obtained, a service personnel selection model is established based on a greedy algorithm, and the method specifically comprises the following steps:
establishing an objective function:
the constraints of the objective function include a first constraint:
Sr_c jt ≤Sr_l ij (2)
second constraint:
wherein Od is ni The number of service personnel for the ith service item in the nth order; sr (Sr) ij Service statistics for the jth attendant in the ith service item;
for the average workload of the ith service item service personnel, the obtained service statistics of each service personnel can be used for obtaining the related information of each service item completed by each service personnel, and the average workload of the ith service item service personnel can be obtained through the service statistics>
Sr_c jt In the embodiment of the invention, when the task allocation of the service personnel is performed, the current service quantity of the service personnel needs to be comprehensively considered, if the current service quantity of the service personnel is too large and the task is allocated to the service personnel, the order is required to be ordered, and the time is too long, so that the order requirement cannot be timely solved. Sr_l ij A service upper limit number for the jth attendant; ar (Ar) ni Attendant IDs arranged for the ith service item in the nth order.
S300, inputting order information and attendant information into an attendant selection model, and distributing tasks to designated attendant through the attendant selection model.
Referring to FIG. 2, in some embodiments, order information is entered in a attendant selection model, the order information including the service items and the number of attendant for each service item (i.e., od ni ) And inputting attendant information, including: service statistics (i.e., sr) corresponding to the attendant ID ij ) Service person ID pair and corresponding current number of servicesQuantity (i.e. Sr_c) jt ) The upper limit number of service corresponding to the attendant ID (i.e., sr_l ij ). And traversing each service item in the order information when task allocation is carried out, and gradually allocating service personnel for each service item. Specifically, according to the obtained order information and service personnel information, outputting the service statistic Sr with the minimum corresponding value ij And marked as a target attendant ID; obtaining the corresponding current service quantity of the target service personnel according to the ID of the target service personnel, obtaining the corresponding service upper limit quantity of the target service personnel, judging and comparing the current service quantity of the target service personnel with the service upper limit quantity of the target service personnel, namely judging whether the condition Sr_c is met or not jt ≤Sr_l ij If the constraint condition is met, outputting the target service personnel ID, distributing service items in an order to the target service personnel ID, if the constraint condition is not met, eliminating the target service personnel ID, then re-acquiring the service personnel ID of the minimum service statistics, comparing the service statistics with the set service upper limit number when distributing tasks to a certain service personnel, if the service statistics are larger than the service upper limit number, canceling the task distribution, re-conducting the task distribution, and designating other service personnel, wherein the aim is to prevent excessive tasks from being distributed to the same service personnel (namely the same service personnel ID), and the workload is excessive. After the task is distributed to the target service personnel ID, the service statistics Sr corresponding to the target service personnel ID is distributed ij Update to prevent service statistics Sr from being updated in subsequent orders or service item assignments ij Thereby resulting in its actual service statistics Sr ij Greater than recorded service statistics Sr ij Meanwhile, judging whether the number of the target service personnel IDs meets the number of the service personnel of the service item, namely judging whether the condition is met
If the constraint condition is not met, returning to continuously acquire the ID of the other target service person, and if the constraint condition is met, outputting the ID of the target service person, wherein the output target service person is at the momentThe attendant IDs are output in the form of a list (there may be one or more target attendant IDs).
Additionally, in some embodiments, since the output corresponds to the smallest service statistic Sr ij Since there may be multiple instances of the same service statistic, multiple minimum service statistics Sr may be obtained ij At this time, a service person ID is randomly selected from the service person IDs as a target service person ID by a random selection method, so that the situation that the distribution flow cannot be continued due to a plurality of results is avoided. In the above embodiment, if the service person task allocation method is applied to service person task allocation in the airport honored guest hall, the service items may include guide, baggage handling, hall, tea, restaurant, delivery, etc. Obviously, the service personnel task allocation method provided by the embodiment of the invention is not only applied to the airport honored guest hall, but also applicable to service personnel task allocation in other scenes.
In summary, according to the service person task allocation method provided by the embodiment of the invention, the service person selection model is built according to the order information and the service person information based on the greedy algorithm by acquiring the order information and the service person information, then the order information and the service person information are input into the service person selection model, and the task is allocated to the appointed service person through the service person selection model; the technical problems of low efficiency and unbalanced service personnel workload caused by manual distribution of service personnel task distribution in the prior art are solved, and an automatic, reasonable and efficient service personnel task distribution method is provided.
Embodiment two:
referring to fig. 3, an embodiment of the present invention provides a service person task allocation apparatus, which includes:
the information acquisition module is used for acquiring order information and service personnel information;
the model building module is used for building a service person selection model according to the order information and the service person information based on a greedy algorithm;
and the task distribution module is used for inputting the order information and the attendant information into the attendant selection model, and distributing the task to the appointed attendant through the attendant selection model.
In some embodiments, the order information acquired by the information acquisition module includes one or more service items, and the order information also includes a corresponding number of service personnel in each service item, where the service personnel information acquired by the information acquisition module includes a service personnel ID, a service statistic corresponding to the service personnel ID, a current service number corresponding to the service personnel ID, and a service upper limit number corresponding to the service personnel ID at the same time.
In some embodiments, after obtaining the order information and the attendant information, the model building module builds an attendant selection model based on a greedy algorithm, which specifically includes:
establishing an objective function:
the constraints of the objective function include a first constraint:
Sr_c jt ≤Sr_l ij (2)
second constraint:
wherein Od is ni The number of service personnel for the ith service item in the nth order; sr (Sr) ij Service statistics for the jth attendant in the ith service item;
average workload for service personnel for the ith service item; sr_c jt The current service quantity of the jth attendant at the t moment is obtained; sr_l ij For the jth attendantUpper limit number of services; ar (Ar) ni Attendant IDs arranged for the ith service item in the nth order.
In some embodiments, the task allocation module allocates tasks to specified service personnel according to a service personnel selection model specifically includes:
obtaining the service personnel ID with minimum service statistics according to the objective function, namely obtaining the objective service personnel ID;
acquiring the current service quantity corresponding to the target service personnel ID according to the target service personnel ID, namely acquiring the current service quantity of the target service personnel;
acquiring the service upper limit number corresponding to the target service personnel ID according to the target service personnel ID, namely acquiring the service upper limit number of the target service personnel;
judging whether the current service quantity of the target service personnel is smaller than or equal to the service upper limit quantity of the target service personnel;
if not, eliminating the target service personnel ID from the task allocation of the service personnel, and repeatedly acquiring the target service personnel ID;
if yes, distributing the task to the target service personnel ID, and updating the current service quantity and the service statistic corresponding to the target service personnel ID;
judging whether the number of the IDs of the service personnel allocated with the tasks to the targets meets the number of the service personnel or not;
if not, returning to continuously acquire the ID of the target service person;
if so, outputting the ID of the target service person.
The process principle implemented by the service personnel task allocation device in the embodiment of the present invention may correspond to the process principle implemented by the service personnel task allocation method in the first embodiment by referring to each other, and will not be described herein.
According to the service personnel task distribution device, the information acquisition module, the model building module and the task distribution module are arranged, so that tasks in order information are distributed to appointed service personnel after the order information and the service personnel information are obtained, and the automatic, reasonable and efficient service personnel task distribution device is provided.
Embodiment III:
the embodiment of the invention provides service personnel task allocation equipment, which comprises the following steps:
the server task allocation method comprises at least one processor and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the server task allocation method as described in embodiment one.
Embodiment four:
an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the attendant task allocation method as described in embodiment one.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of one of ordinary skill in the art without departing from the spirit of the present invention. Furthermore, embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Claims (5)
1. A method for distributing service personnel tasks, comprising:
acquiring order information and service personnel information; wherein the order information comprises at least one service item, and the number of service personnel for each service item; wherein the attendant information includes: service personnel ID, service statistics corresponding to the service personnel ID, current service quantity in progress corresponding to the service personnel ID, and service upper limit quantity corresponding to the service personnel ID at the same time;
and establishing a service personnel selection model based on a greedy algorithm according to the order information and the service personnel information, wherein the service personnel selection model specifically comprises the following steps:
establishing an objective function:
the constraints of the objective function include a first constraint:
Sr_c jt ≤Sr_l ij (2)
second constraint:
wherein Od is ni The number of service personnel for the ith service item in the nth order; sr (Sr) ij The service statistics of the jth service personnel in the ith service item;
average workload for service personnel for the ith service item; sr_c jt The current service quantity of the jth attendant at the t moment is obtained; sr_l ij The upper number of services for the jth attendant; ar (Ar) ni The attendant ID arranged for the ith service item in the nth order;
and inputting the order information and the attendant information into the attendant selection model, and distributing tasks to designated attendant through the attendant selection model.
2. The service person task allocation method according to claim 1, wherein the inputting the order information and the service person information into the service person selection model, and allocating tasks to specified service persons through the service person selection model specifically includes:
obtaining the service personnel ID with the minimum service statistics according to the objective function, namely obtaining a target service personnel ID;
acquiring the current service quantity corresponding to the target service personnel ID according to the target service personnel ID, namely acquiring the current service quantity of the target service personnel;
acquiring the service upper limit number corresponding to the target service personnel ID according to the target service personnel ID, namely acquiring the service upper limit number of the target service personnel;
judging whether the current service quantity of the target service personnel meets or not more than the service upper limit quantity of the target service personnel;
if not, eliminating the target service personnel ID from the task allocation of the service personnel, and repeatedly acquiring the target service personnel ID;
if yes, distributing tasks to the target service personnel ID, and updating the current service quantity and the service statistics corresponding to the target service personnel ID;
judging whether the number of the IDs of the target service personnel allocated with the tasks meets the number of the service personnel or not;
if not, returning to continuously acquire the ID of the target service person;
and if so, outputting the target service personnel ID.
3. A service person task allocation apparatus, comprising:
the information acquisition module is used for acquiring order information and service personnel information; wherein the order information comprises at least one service item, and the number of service personnel for each service item; wherein the attendant information includes: service personnel ID, service statistics corresponding to the service personnel ID, current service quantity in progress corresponding to the service personnel ID, and service upper limit quantity corresponding to the service personnel ID at the same time;
the model building module is used for building a service person selection model based on a greedy algorithm according to the order information and the service person information, and specifically comprises the following steps:
establishing an objective function:
the constraints of the objective function include a first constraint:
Sr_c jt ≤Sr_l ij (2)
second constraint:
wherein Od is ni The number of service personnel for the ith service item in the nth order; sr (Sr) ij The service statistics of the jth service personnel in the ith service item;
average workload for service personnel for the ith service item; sr_c jt The current service quantity of the jth attendant at the t moment is obtained; sr_l ij The upper number of services for the jth attendant; ar (Ar) ni The attendant ID arranged for the ith service item in the nth order;
and the task distribution module is used for inputting the order information and the attendant information into the attendant selection model, and distributing the task to the appointed attendant through the attendant selection model.
4. A service person task allocation apparatus, comprising:
at least one processor, and,
a memory communicatively coupled to the at least one processor, wherein,
the memory stores instructions executable by at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 2.
5. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method of any one of claims 1 to 2.
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| CN112101714B (en) * | 2020-08-06 | 2023-12-29 | 长沙市到家悠享家政服务有限公司 | Task allocation method, device, equipment and storage medium |
| CN111815304A (en) * | 2020-09-14 | 2020-10-23 | 江苏开博科技有限公司 | Realization method of non-directional approval mechanism based on workload balancing election algorithm |
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