CN112396274A - Task scheduling method - Google Patents
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Abstract
The application discloses a task scheduling method, which comprises the following steps: acquiring the task quantity of a plurality of task time periods with first time granularity; determining a first task carrying capacity of each task carrying party in a unit task time period; determining a first efficiency mapping relation from a plurality of task carrying time periods with second time granularity to each task time period; and determining a first corresponding relation between the task bearer and the task bearer time period at least according to the task amount, the first task bearer amount and the first efficiency mapping relation. By adopting the processing mode, the task scheduling can be compatible with the task requirements of different time granularities, such as the class level of a coarser time granularity and the hour level of a fine time granularity, so as to adapt to the service scenes of fine operation such as e-commerce customer service, new retail and the like, and the early-stage manual setting and the later-stage manual adjustment can be reduced as much as possible; therefore, the reasonability of task scheduling can be effectively improved, and the labor force management performance is improved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a task scheduling method.
Background
The service industry (such as customer service, retail enterprises, etc.) is under pressure of increasing task load on one hand and increasing labor cost on the other hand, and maintaining reasonable profit and per-person task load is a challenge for each service enterprise. Therefore, the service industry pays attention to the utilization rate of the provided staff and the scientificity of scheduling, and the labor management efficiency of arrangement is improved.
From the perspective of task processing, the problem of staff scheduling is the task scheduling problem. Currently, a commonly used task scheduling method is based on a shift assignment method, that is, the time granularity of both a person (task bearer) and a task is a shift (e.g., a late shift, an early shift, etc.). The shift assignment based approach may specifically employ pattern repetition, history following, rules engine, and the like. The technical features and disadvantages of these conventional embodiments will be briefly described below.
1. And (4) a task scheduling mode of mode repetition. The mode has the characteristics that: setting a simple model for repeating, such as: morning-noon-evening-holiday. The disadvantages of this approach are: needs manual setting and is difficult to support complex scenes.
2. History continues to use the task scheduling approach. The mode has the characteristics that: the shift table of the previous cycle is copied. The disadvantages of this approach are: the varying requirements cannot be supported and the workload of post-adjustment can be significant.
3. And (4) a task scheduling mode of the rule engine. The mode has the characteristics that: and (4) screening proper personnel in sequence by using various rules to distribute tasks. The disadvantages of this approach are: it is difficult to take all the factors into account, often resulting in some hard constraint violations.
However, in the process of implementing the invention, the inventor finds that more and more enterprises can estimate the task demand amount in a future period of time based on big data in the service scenes of fine operation such as e-commerce customer service, new retail and the like, and the estimated granularity can reach one hour or even half an hour according to the requirements of fine operation. However, on the personnel supply side, the time granularity of human resources is still in shift units, spanning several hours, taking into account the management characteristics of personnel. In addition, when arranging personnel, hard legal regulations such as working time, rest and the like, and soft humanized management and personalized requirements such as shift fairness, shift switching preference and the like need to be comprehensively considered.
In summary, on one hand, in a service scene of fine operation, the time granularity of the task demand is small, and the time granularity of the human resources is class level; on the other hand, the existing method based on the assignment of the shift requires that the time granularity of the personnel and the tasks is the shift, so that the existing task scheduling method cannot adapt to the task requirement of the fine time granularity, and the rationality of task scheduling in a service scene of fine operation is reduced. With the increase of service volume and the rise of labor cost, how to design a task scheduling system suitable for a fine operation service scene to achieve the purposes of improving the utilization rate of human resources and the service level and obtaining better user experience with less cost is the core problem of labor scheduling in the service industry.
Disclosure of Invention
The application provides a task scheduling method to solve the problem that task scheduling reasonableness is low in the prior art.
The application provides a task scheduling method, which comprises the following steps:
determining a task volume for a plurality of task time segments at a first time granularity;
determining a first task carrying capacity of a plurality of task bearers in a unit task time period; determining a first efficiency mapping relation from a plurality of task carrying time periods with second time granularity to each task time period;
and determining a first corresponding relation between the task bearer and the task bearer time period at least according to the task amount, the first task bearer amount and the first efficiency mapping relation.
Optionally, the first corresponding relationship is determined as follows:
determining a second task carrying capacity of each task carrying party group in a unit task time period according to the first task carrying capacity and the task carrying party group information;
determining a second efficiency mapping relation from a plurality of task carrying time period groups to each task time period according to the first efficiency mapping relation;
determining a second corresponding relation between the task bearer group and the task bearer time period group according to the task quantity, the second task bearer quantity and the second efficiency mapping relation;
and determining a second corresponding relation between the task carrying party and the task carrying time period as the first corresponding relation according to the second corresponding relation.
Optionally, the second performance mapping relationship is determined by the following method:
determining task carrying time period group information according to the starting time and the ending time of the task carrying time period;
and aiming at each task carrying time period group, determining a second efficiency mapping relation from the task carrying time period group to each task time period according to a first efficiency mapping relation from each task carrying time period to each task time period in the task carrying time period group.
Optionally, the task carrying time period group information is determined in the following manner:
and merging a plurality of pieces of task carrying time period information with the same starting time and ending time into task carrying time period group information.
Optionally, the weighted average of the first performance mapping relationship is used as the second performance mapping relationship.
Optionally, acquiring a task scheduling rule;
and determining a first corresponding relation which accords with the task scheduling rule at least according to the task amount, the first task carrying capacity and the first efficiency mapping relation.
Optionally, the task scheduling rule includes at least one of the following rules:
the task receivers in the same group correspond to the task receiving time periods with the same or similar starting time and ending time;
a task taking days threshold, a non-task taking days threshold, a task taking hours upper limit threshold and/or a task taking hours lower limit threshold of the month/week dimension;
the difference of the first shift quantity of each task bearer group is smaller than a difference threshold value;
the first shift is not adjacent to the second shift.
Optionally, the first time granularity includes hours and the second time granularity includes shifts.
Optionally, the first corresponding relationship is determined at least according to the task amount, the first task carrying amount, and the first efficiency mapping relationship by an optimization solver or a heuristic algorithm.
Optionally, the determining the task amount of the plurality of task time periods of the first time granularity includes:
and determining a task quantity predicted value of each task time period in each day in the target time period according to the historical task quantity data.
The present application also provides a computer-readable storage medium having stored therein instructions, which when run on a computer, cause the computer to perform the various methods described above.
The present application also provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the various methods described above.
Compared with the prior art, the method has the following advantages:
according to the task scheduling method provided by the embodiment of the application, the task quantity of a plurality of task time periods with the first time granularity is obtained; determining a first task carrying capacity of each task carrying party in a unit task time period; determining a first efficiency mapping relation from a plurality of task carrying time periods with second time granularity to each task time period; determining a first corresponding relation between a task bearer and a task bearer time period at least according to the task amount, the first task bearer and the first efficiency mapping relation; in the processing mode, by introducing the first efficiency mapping relation, the task scheduling can be compatible with the task requirements of different time granularities, such as the class level of a coarse time granularity and the hour level of a fine time granularity, so as to adapt to the service scenes of fine operation such as e-commerce customer service, new retail and the like, and the early manual setting and the later manual adjustment can be reduced as much as possible; therefore, the reasonability of task scheduling can be effectively improved, and the labor force management performance is improved.
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FIG. 1 is a flow chart of an embodiment of a task scheduling method provided herein;
fig. 2 is a specific flowchart of an embodiment of a task scheduling method provided in the present application.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of implementation in many different ways than those herein set forth and of similar import by those skilled in the art without departing from the spirit of this application and is therefore not limited to the specific implementations disclosed below.
First embodiment
Please refer to fig. 1, which is a flowchart illustrating an embodiment of a task scheduling method according to the present application, wherein an execution main body of the method includes a task scheduling device. The task scheduling method provided by the application comprises the following steps:
step S101: a task volume for a plurality of task time segments at a first time granularity is determined.
The task scheduling method provided by the embodiment of the application does not limit the task carrying time period and the time granularity of the task time period, the task time period can be in an hour level or a shift level, and the time granularity of the task carrying time period can also be in an hour level or a shift level, that is: the method provided by the embodiment of the application can be compatible with task requirements of different time granularities, so that the method not only can adapt to the service scenes of non-fine operation of traditional enterprises, but also can adapt to the service scenes of fine operation such as e-commerce customer service, new retail and the like.
The time granularity of the task time period is referred to as a first time granularity, and the time granularity of the task accepting time period is referred to as a second time granularity.
In one example, the business scenario is a business scenario in which the e-commerce customer service provides a user consultation volume refinement operation for the user regarding shopping consultation. In this case, one consultation from one user is one task, the time management granularity (first time granularity) of the user consultation amount is on the order of hours (e.g., half an hour, 1 hour, or 2 hours, etc.), and the time granularity (second time granularity) of the customer service schedule is a shift (e.g., morning shift, afternoon shift, night shift, etc.).
In specific implementation, step S101 can be implemented as follows: based on historical user advisory volumes and/or timeliness data (e.g., weather, season, promotional activity, etc.), a predicted task volume value for each task time segment on each day within a target time segment (e.g., 7 days, 1 month, etc. in the future) is determined. For example, firstly, according to the log data of the last 3 months of user consultation, the average task amount of each task time period is counted; then, the task amount of each task time period in the future 7 days is predicted by combining the weather condition or season of the future 7 days and other factors, if the weather condition is extremely hot in the future 7 days, the consultation amount of the air conditioner is increased rapidly, if the future 7 days are the mature seasons of the litchi, the consultation amount of the litchi is increased rapidly, or if the future 7 days are 11 days, the consultation amount of all commodities in the future 7 days is increased rapidly, and the like. Table 1 shows the predicted value of the task amount of the present embodiment.
| 2019/7/1 | 2019/7/9 | … | 2019/7/31 | |
| … | ||||
| 8 to 9 points | 23 | 197 | 27 | |
| 9 to 10 points | 10 | 154 | 12 | |
| … | ||||
| Point 21 to point 22 | 45 | 62 | 38 | |
| … |
TABLE 1 task amount prediction Table
As can be seen from table 1, the first time granularity of the present embodiment is 1 hour, so that 1 day is divided into 24 task time segments, and the task amount of each time segment is predicted separately, and the task amounts of different time segments may be different. Since the task is finely operated, that is, the task amount of the task time period of the first time granularity is predicted, which belongs to the mature prior art, the detailed description is omitted here.
In another example, the business scenario is a business scenario of a non-task fine operation of a plant personnel shift. In this case, the product yield is taken as the task volume, the time management granularity (first time granularity) of which is usually of the class level, e.g. 8 hours for a class, the number of products in the working time of a class is determined; similarly, the time granularity at which workers are scheduled (the second time granularity) is also the shift (e.g., morning shift, afternoon shift, night shift, etc.). Since the conventional operation of performing non-refinement on tasks belongs to the mature prior art, the detailed description is omitted here.
After determining the task quantities of a plurality of task time periods with the first time granularity, entering the next step, and determining the first task carrying capacity of each task carrying party in a plurality of task carrying party groups in a unit task time period; and determining a first efficiency mapping relation from a plurality of task accepting time periods with the second time granularity to each task time period.
Step S103: determining a first task carrying capacity of a plurality of task bearers in a unit task time period; and determining a first efficiency mapping relation from a plurality of task accepting time periods with the second time granularity to each task time period.
1) A first task carry over amount for a unit task time period for a plurality of task bearers is determined.
The unit task time period refers to a time unit of the task time period. For example, the time unit of the task time period of 8-9 points and 10-11 points is 1 hour, and thus the unit task time period of the staff shift task is 1 hour. The unit task time period may be half an hour or two hours, etc., depending on the actual situation.
The first task capacity includes the task capacity of the task bearer in a unit task time period, such as the maximum tolerable task capacity is 30.
2) A first efficiency mapping relation from a plurality of task accepting time periods with second time granularity to each task time period is determined.
In the method provided in this embodiment, a first performance mapping table shown in table 2 is introduced, and a performance function (the first performance mapping table) is used to represent the mapping relationship from the task receiving time period to the granularity of the unit task time period.
TABLE 2 first Performance mapping Table
As shown in table 2, if the working time of the task taking period 7a is 8 am to 5 pm and the rest time is 10 pm to 11 pm, a vector may be used to represent the first performance mapping relationship from the task taking period to the time slot (task period): u. of7a＝[…0,1,1,0,1,1,1,1,1,1,0,…]Where 1 represents work and 0 represents rest, the vector dimension may be the total number of slots in a day, and if the task period granularity is 1 hour, 1 day may include 24 slots. Similarly, for the task taking-over time period 7b with the working time from 8 am to 5 pm and the rest time from 11 pm to 12 pm, the mapping relationship can be expressed as: u. of7b＝[…0,1,1,1,0,1,1,1,1,1,0,…]。
After the first task carrying capacity and the first efficiency mapping relation are determined, the next step can be carried out to determine task scheduling information of task carrying time periods which enable the same group of task carrying parties to be scheduled to the same or similar task carrying time period from the starting working time to the ending working time according to the task capacity, the first task carrying capacity and the first efficiency mapping relation.
Step S105: and determining a first corresponding relation between the task bearer and the task bearer time period according to the task amount, the first task bearer and the first efficiency mapping relation.
In one example, the task bearers are directly used as a scheduling unit, and the task bearer time period is directly allocated to each task bearer according to the task amount, the first task bearer and the first efficiency mapping relation. However, even if the working time of one task receiving time period is limited, different starting times and rest time slots still generate a plurality of task receiving time periods with different modes, which results in huge problem scale, large consumption of computing resources and storage resources, and even no direct solution in serious cases (such as too many task receiving time periods), and thus the practicability is low.
Please refer to fig. 2, which is a flowchart illustrating an embodiment of a task scheduling method according to the present invention. In order to reduce the problem size and improve the practicability of the method, in this embodiment, step S105 may include the following sub-steps:
step S1051: and determining the second task carrying capacity of each task carrying party group in the unit task time period according to the first task carrying capacity and the task carrying party group information.
In a large-scale personnel scheduling scene, such as an e-commerce customer service scene, the number of service personnel is as many as hundreds of people at all. For the convenience of management, the business usually divides the personnel into several groups (personnel groups), and the working hours of the personnel in the same personnel group are as close as possible. In this case, it is required that the staff scheduling result obtained by executing the method provided by the embodiment of the present application is to schedule the same group of staff to the task accepting time period with the same or similar start working time and end working time, rather than assigning the staff to any one task accepting time period without any limitation, and at the same time, avoiding that the basic staff management principle is violated to cause confusion to the staff scheduling of the enterprise.
In this embodiment, the human resources in the same group are aggregated, and the employee group is regarded as a shift scheduling unit to participate in the allocation algorithm for solving, so that the same group of employees work at the same time as much as possible. Let e be the performance per unit time (first task throughput) of task bearer iiThe performance per unit time (second task throughput) of the task recipient group g is eg＝∑i∈gei。
Step S1053: and determining a second efficiency mapping relation from the task carrying time period groups to each task time period according to the first efficiency mapping relation.
In actual business, there are often more task receiving time periods with the same start-stop time but different rest times, and this is aggregated. As shown in table 3, the start and end times of the task engaging time segment 7a and the task engaging time segment 7b are the same, the working hours are all from 8 am to 5 pm, and the difference is only that the rest time is different in the middle, wherein the rest time of the task engaging time segment 7a is 10-11 o 'clock, and the rest time of the task engaging time segment 7b is 11-12 o' clock. The method provided by the application aggregates the task carrying time periods with the same start time and the same stop time but different rest time periods, and takes the aggregation result as a second efficiency mapping relation of the task carrying time periods to the time slots.
In specific implementation, the weighted vector may be used to represent the second performance mapping relationship from the aggregated task carrying time segment group to the time slot, and if the respective weights of the two task carrying time segments are both 0.5, the task carrying time segment group u is then used7＝[…0,1,1,0.5,0.5,1,1,1,1,1,0,…]。
Table 3, task accepting time period aggregation table
Similarly, a second performance mapping relationship from all aggregated task accepting time period groups to each task time period can be obtained, as shown in table 4.
Table 4, second performance mapping table
In this embodiment, similar task accepting time periods are aggregated into a task accepting time period group, and efficiency vectors are introduced to weight the efficiency of the task accepting time period group in different time periods, so as to provide an efficiency value mapping relationship from the task accepting time period group to the time slots, and this transformation can support human resources of task accepting time period granularity to cover task requirements of fine time granularity.
Step S1055: and determining a second corresponding relation between the task bearer group and the task bearer time period group according to the task quantity, the second task bearer quantity and the second efficiency mapping relation.
In one example, the crew groups may be used as a scheduling unit, and the task taking time period group may be directly allocated to each crew group according to the task amount, the first task taking amount and the first efficiency mapping relation, without considering a scheduling rule. The mathematical model of this approach can be expressed as:
wherein x represents a task scheduling result (first correspondence),
is a variable from 0 to 1, indicating whether the task accepting group g is assigned to the task accepting time period group s on d days;
representing the performance value of the task taking period set s in time slot t, qdtIs the input task amount predicted value of the time slot; the method comprises the following steps that (1) each task bearer group is limited to work for at most one task bearer time period group every day; equation (2) indicates that the total performance value of the job bearers allocated to a particular time slot needs to be greater than the amount of jobs in that time slot. Target value
Is a function of the task-accepted time period allocation situation x, and may indicate that the coverage of the task requirement is the largest, the labor cost is the lowest, and so on, wherein,
and (4) representing the cost of the task accepting group g to be allocated to the task accepting time period s in d days, such as labor cost, preference cost and the like.
In specific implementation, the optimization model may be solved by various methods, such as solving using an optimization solver, or solving using a heuristic algorithm (e.g., a genetic algorithm), and the like. Since the model solution belongs to the mature prior art, it is not described here again.
However, the above processing method does not consider the policy law, such as: the number of days worked, the number of days rested, the upper and lower hours worked, etc. for the month/week dimension; the factors such as humanized management and personnel personalized preference are not comprehensively considered, such as: the task receiving time periods of the task receivers in the same group are as close as possible, the number of the later shifts of each task receiver group is as fair as possible, and the earlier shifts are not connected behind the later shifts; therefore, the task scheduling is less reasonable, and the utility is low due to problems such as violation of legal regulations.
In order to solve the above problem, the method provided in the embodiment of the present application may further include the following steps: acquiring a task scheduling rule; accordingly, step S1055 can be implemented as follows: and determining a first corresponding relation which accords with the task scheduling rule between the task bearer group and the task bearer time period group according to the task quantity, the second task bearer quantity and the second efficiency mapping relation.
The task scheduling rule can be read from a task scheduling rule configuration file or can be acquired from a database. The task scheduling rules include, but are not limited to, at least one of the following: the same group of personnel is scheduled to the task carrying time period with the same or similar starting working time and ending working time; a work days threshold, a rest days threshold, a hours worked upper threshold and/or a lower threshold for the month/week dimension; the difference of the number of the night shifts of each employee group is smaller than a difference threshold value; the night shift is not followed by the morning shift, and so on.
In this case, the mathematical model that assigns the set of task bearers to the set of task-accepting time periods may be expressed as:
h(x)＝0 (3)
the formula (3) represents the task scheduling rule limit of the scenario, including all hard rules, such as the rules of policy and regulation, and some soft rules, such as the task carrying time periods of the staff groups being as close as possible.
In this embodiment, a set of modeling languages may be defined in advance for equation (3), and whether to add the modeling languages to the mathematical model may be determined by a configuration file as shown in table 5.
TABLE 5 task scheduling rules configuration File
The connection solving method of the optimization model can be flexibly selected, for example, an optimization solver is used for solving, or a heuristic algorithm such as a genetic algorithm is adopted, and the scheme is not specifically formulated. The obtained solution result is the distribution relationship from the employee group to the task undertaking time period group, as shown in table 6.
| 2019/7/1 | 2019/7/9 | 2019/7/10 | 2019/7/11 | 2019/7/12 | |
| Group of people 1 | Class group 7 | Class group 8 | Class group 10 | Class group 5 | |
| Group of people 2 | Class group 11 | Class group 6 | Class group 8 | Class group 9 | |
| … |
Table 6, assignment scheme table of task accepting party group (personnel group) -task accepting time period group (shift group)
In this embodiment, by aggregating the efficacy values of the employee team, the employee team is used as a shift scheduling unit to model the assignment problem from the employee team to the shift team, and hard rules such as legal regulations and soft rules such as shift fairness and preference can be comprehensively considered. The model is flexible and expandable, and is convenient for fast adapting to different service scenes and requirements.
Step S1057: and determining a second corresponding relation between the task carrying party and the task carrying time period as the first corresponding relation according to the second corresponding relation.
And further solving to obtain the employee-shift distribution relation based on the employee group-shift group distribution relation obtained in the previous stage, namely, dividing the aggregated shift group into shifts corresponding to each employee. Specifically, specific shift assignment within the group can be performed based on the efficacy values of the employees, the assignment relationship from each employee to the specific shift is obtained, and finally the executable shift table is obtained. Assuming that a certain shift group is formed by combining R specific shifts, and considering the efficiency values (first task capacity) of the employees, the employees in the employee group are divided into R groups, so that the sum of the efficiency values of the employees in each group is as close as possible, which can be expressed by the following mathematical model:
wherein x isirIs a variable from 0 to 1 indicating whether employee i is in the r < th > subgroup; formula (4) is the total efficacy of the personnel of the r-th team; equation (5) limits the ability of the employees to be assigned to only one team. In this manner, a specific shift type corresponding to each employee can be obtained, and an execution shift table is generated, as shown in table 7.
| 2019/7/1 | 2019/7/9 | 2019/7/10 | 2019/7/11 | 2019/7/12 | |
| Group of people 1 | Class group 7 | Class group 8 | Class group 10 | Class group 5 | |
| Person 1 | Shift 7a | Shift 8a | Shift 10a | Shift 5 | |
| Person 2 | Shift 7a | Shift 8a | Shift 10b | Shift 5 | |
| Person 3 | Shift 7a | Shift 8b | Shift 10b | Number of shifts5 | |
| Person 4 | Shift 7b | Shift 8b | Shift 10c | Shift 5 | |
| Person 5 | Shift 7b | Shift 8b | Shift 10c | Shift 5 | |
| Group of people 2 | Class group 11 | Class group 6 | Class group 8 | Class group 9 | |
| Person 6 | … | ||||
| Person 7 | … | ||||
| … |
TABLE 7 executive shift list for employee group
In summary, the present embodiment implements flexible task scheduling in stages through the steps S1051-1057. Specifically, in the first stage, people can be aggregated into a group of people through an efficiency function, and the shift is aggregated into a group of shifts; in the second stage, the personnel groups are distributed to the shift groups to cover the task scale; and in the third stage, the distribution of the personnel group to the shift group is disassembled to obtain an execution shift table of the personnel to the specific shift. By adopting the processing mode, the task requirement of fine time granularity can be adapted, the complexity of problem solving can be effectively reduced, and the solving quality can be ensured as much as possible, so that hundreds of service personnel can reasonably arrange work and rest time, the task requirement can be completely covered as much as possible, and the large-scale scheduling scene can be adapted. Meanwhile, various rule configurations can be considered, so that the satisfaction degree of workers is improved.
As can be seen from the foregoing embodiments, in the task scheduling method provided in the embodiments of the present application, the task amounts of a plurality of task time periods of the first time granularity are obtained; determining a first task carrying capacity of each task carrying party in a unit task time period; determining a first efficiency mapping relation from a plurality of task carrying time periods with second time granularity to each task time period; determining a first corresponding relation between a task bearer and a task bearer time period at least according to the task amount, the first task bearer and the first efficiency mapping relation; in the processing mode, by introducing the first efficiency mapping relation, the task scheduling of the class can be compatible with the task requirements of different time granularities, such as the class level of a coarse time granularity and the hour level of a fine time granularity, so as to adapt to the service scenes of fine operation of e-commerce customer service, new retail and the like, and the early manual setting and the later manual adjustment can be reduced as much as possible; therefore, the reasonability of task scheduling can be effectively improved, and the labor force management performance is improved.
Although the present application has been described with reference to the preferred embodiments, it is not intended to limit the present application, and those skilled in the art can make variations and modifications without departing from the spirit and scope of the present application, therefore, the scope of the present application should be determined by the claims that follow.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
1. Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (transient media), such as modulated data signals and carrier waves.
2. As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Claims (10)
1. A method for task scheduling, comprising:
determining a task volume for a plurality of task time segments at a first time granularity;
determining a first task carrying capacity of a plurality of task bearers in a unit task time period; determining a first efficiency mapping relation from a plurality of task carrying time periods with second time granularity to each task time period;
and determining a first corresponding relation between the task bearer and the task bearer time period at least according to the task amount, the first task bearer amount and the first efficiency mapping relation.
2. The method of claim 1, wherein the first correspondence is determined as follows:
determining a second task carrying capacity of each task carrying party group in a unit task time period according to the first task carrying capacity and the task carrying party group information;
determining a second efficiency mapping relation from a plurality of task carrying time period groups to each task time period according to the first efficiency mapping relation;
determining a second corresponding relation between the task bearer group and the task bearer time period group according to the task quantity, the second task bearer quantity and the second efficiency mapping relation;
and determining a second corresponding relation between the task carrying party and the task carrying time period as the first corresponding relation according to the second corresponding relation.
3. The method of claim 2, wherein the second performance mapping is determined as follows:
determining task carrying time period group information according to the starting time and the ending time of the task carrying time period;
and aiming at each task carrying time period group, determining a second efficiency mapping relation from the task carrying time period group to each task time period according to a first efficiency mapping relation from each task carrying time period to each task time period in the task carrying time period group.
4. The method of claim 3, wherein the task joining time period group information is determined as follows:
and merging a plurality of pieces of task carrying time period information with the same starting time and ending time into task carrying time period group information.
5. The method of claim 3,
and taking the weighted average of the first efficiency mapping relation as the second efficiency mapping relation.
6. The method of claim 1,
acquiring a task scheduling rule;
and determining a first corresponding relation which accords with the task scheduling rule at least according to the task amount, the first task carrying capacity and the first efficiency mapping relation.
7. The method of claim 6, wherein the task scheduling rules comprise at least one of the following rules:
the task receivers in the same group correspond to the task receiving time periods with the same or similar starting time and ending time;
a task taking days threshold, a non-task taking days threshold, a task taking hours upper limit threshold and/or a task taking hours lower limit threshold of the month/week dimension;
the difference of the first shift quantity of each task bearer group is smaller than a difference threshold value;
the first shift is not adjacent to the second shift.
8. The method of claim 1, wherein the first time granularity comprises hours and the second time granularity comprises shifts.
9. The method of claim 1,
and determining the first corresponding relation at least according to the task quantity, the first task carrying capacity and the first efficiency mapping relation through an optimization solver or a heuristic algorithm.
10. The method of claim 1, wherein determining the task volume for the plurality of task time segments for the first time granularity comprises:
and determining a task quantity predicted value of each task time period in each day in the target time period according to the historical task quantity data.
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