CN116187728A - Customer service scheduling optimization method, customer service scheduling optimization device, customer service scheduling optimization equipment and storage medium - Google Patents

Customer service scheduling optimization method, customer service scheduling optimization device, customer service scheduling optimization equipment and storage medium Download PDF

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CN116187728A
CN116187728A CN202310483804.0A CN202310483804A CN116187728A CN 116187728 A CN116187728 A CN 116187728A CN 202310483804 A CN202310483804 A CN 202310483804A CN 116187728 A CN116187728 A CN 116187728A
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customer service
index data
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苏立伟
姚蓝霓
覃浩
张勇军
康峰
陶飞达
陈海燕
伍广斌
吴海波
曾晓锋
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Guangdong Power Grid Co Ltd
Customer Service Center of Guangdong Power Grid Co Ltd
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Customer Service Center of Guangdong Power Grid Co Ltd
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Abstract

The application discloses a customer service scheduling optimization method, a customer service scheduling optimization device, customer service scheduling optimization equipment and a storage medium, wherein business volume prediction information and business processing information of customer service personnel are obtained; iterating the preset scheduling optimization model according to the traffic prediction information and the traffic processing information by using a preset particle swarm optimization algorithm to obtain a customer service scheduling scheme of each iteration; calculating an objective function of customer service personnel under a customer service scheduling scheme based on a preset fatigue index model and a preset fairness index model, wherein the objective function comprises fatigue index data and fairness index data; and if the objective function reaches the minimum value, determining the corresponding customer service scheduling scheme when the objective function reaches the minimum value as a target customer service scheduling scheme. Therefore, the computer is utilized to optimize the customer service scheduling through an optimization algorithm, the scheduling efficiency is improved, fatigue and fairness are considered in an optimization model to quantify the workload of customer service staff, and the scheduling result is more reasonable.

Description

Customer service scheduling optimization method, customer service scheduling optimization device, customer service scheduling optimization equipment and storage medium
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a customer service scheduling optimization method, apparatus, device, and storage medium.
Background
With the increase of service types, the service range is continuously expanded, the communication between enterprises and clients is increased, the customer service demands are greatly improved, and the work tasks of a customer service center are heavy, so that more customer service personnel and a more efficient and reasonable scheduling scheme are required. However, the traditional scheduling mode is mainly manual scheduling of management staff, the scheduling mode is low in efficiency, and mobility of customer service staff can cause scheduling pressure of the management staff; moreover, manual scheduling cannot quantify the workload of customer service staff, so that scheduling results are unreasonable, and if a temporary customer service peak is encountered, scheduling is difficult to adjust in time, so that the customer service scheduling is disordered.
Disclosure of Invention
The application provides a customer service scheduling optimization method, device, equipment and storage medium, which are used for solving the technical problems that the traditional scheduling mode is low in efficiency and unreasonable in scheduling result.
In order to solve the technical problem, in a first aspect, the present application provides a customer service scheduling optimization method, including:
acquiring service volume prediction information and service processing information of customer service personnel;
iterating a preset scheduling optimization model by using a preset particle swarm optimization algorithm according to the traffic prediction information and the traffic processing information to obtain a customer service scheduling scheme of each iteration;
calculating an objective function of the customer service personnel under the customer service scheduling scheme based on a preset fatigue index model and a preset fairness index model, wherein the objective function comprises fatigue index data and fairness index data;
and if the objective function reaches the minimum value, determining the corresponding customer service scheduling scheme when the objective function reaches the minimum value as a target customer service scheduling scheme.
In some implementations, the obtaining traffic prediction information and service processing information of customer service personnel includes:
predicting the traffic prediction information within a future target time period based on a traffic prediction model;
and calling the service processing information of the customer service personnel to a customer service center, wherein the service processing information comprises the class type, the on-duty time period, the number of the customer service personnel and the average processing service duration.
In some implementations, the calculating, based on a preset fatigue index model and a preset fairness index model, an objective function of the customer service personnel under the customer service scheduling scheme, the objective function including fatigue index data and fairness index data includes:
determining fatigue index data of the customer service personnel under the customer service scheduling scheme based on the preset fatigue index model;
determining fairness index data of the customer service personnel under the customer service scheduling scheme based on the preset fairness index model;
calculating the objective function according to the fatigue index data and the fairness index data, wherein the expression of the objective function is as follows:
Figure SMS_1
；/>
wherein ,
Figure SMS_2
for average fatigue index data, < >>
Figure SMS_3
For fairness index data, ++>
Figure SMS_4
and
Figure SMS_5
Is a weight coefficient.
In some implementations, the determining fatigue index data of the customer service personnel under the customer service scheduling scheme based on the preset fatigue index model includes:
based on the service processing information of the customer service personnel, calculating shift regularity index data, busyness index data and shift influence index data of the customer service personnel under the customer service scheduling scheme;
based on the preset fatigue index model, calculating the fatigue index data according to the shift regularity index data, the busyness index data and the shift impact index data, wherein the expression of the preset fatigue index model is as follows:
Figure SMS_6
；
wherein ,
Figure SMS_7
indicating fatigue index data->
Figure SMS_8
Index data representing the regularity of shifts, +.>
Figure SMS_9
Indicating shift impact index data, < >>
Figure SMS_10
Indicating +.>
Figure SMS_11
Busyness index data of day, +.>
Figure SMS_12
and
Figure SMS_13
Representing the weight coefficient.
In some implementations, the shift regularity index data has a calculation formula:
Figure SMS_14
；
the calculation formula of the busyness index data is as follows:
Figure SMS_15
；
the calculation formula of the shift impact index data is as follows:
Figure SMS_16
；
wherein ,
Figure SMS_17
the interval duration of the start time of the duty of two adjacent workdays;
Figure SMS_18
The average interval duration of the start time of the duty of two adjacent days in a week;
Figure SMS_19
Traffic accepted by customer service personnel;
Figure SMS_20
The time length of the duty of the customer service personnel;
Figure SMS_21
Is the coincidence duration of the duty time period and the normal rest time period.
In some implementations, the expression of the preset fairness index model is:
Figure SMS_22
；
wherein ,
Figure SMS_23
for fairness index data, ++>
Figure SMS_24
For average fatigue index data, < >>
Figure SMS_25
Is->
Figure SMS_26
Comprehensive fatigue index data of individual customer service personnel, < >>
Figure SMS_27
Representing the number of customer service personnel.
In some implementations, the customer service shift scheme includes a workday shift matrix and a rest day matrix, wherein the workday shift matrix is:
Figure SMS_28
；
wherein ,
Figure SMS_29
is->
Figure SMS_30
Workday shift matrix of individual customer service personnel,/->
Figure SMS_31
Is->
Figure SMS_32
Personal customer service staff at->
Figure SMS_33
Shift type for each workday.
In a second aspect, the present application further provides a customer service shift optimizing apparatus, including:
the acquisition module is used for acquiring the service volume prediction information and the service processing information of the customer service personnel;
the iteration module is used for iterating the preset scheduling optimization model according to the traffic prediction information and the traffic processing information by using a preset particle swarm optimization algorithm to obtain a customer service scheduling scheme of each iteration;
the calculation module is used for calculating an objective function of the customer service personnel under the customer service scheduling scheme based on a preset fatigue index model and a preset fairness index model, wherein the objective function comprises fatigue index data and fairness index data;
and the determining module is used for determining the corresponding customer service scheduling scheme as a target customer service scheduling scheme when the objective function reaches the minimum value if the objective function reaches the minimum value.
In a third aspect, the present application further provides a computer device, comprising a processor and a memory for storing a computer program, which when executed by the processor implements the customer service scheduling optimization method according to the first aspect.
In a fourth aspect, the present application further provides a computer readable storage medium storing a computer program, which when executed by a processor implements the customer service shift optimization method according to the first aspect.
Compared with the prior art, the application has the following beneficial effects:
acquiring service volume prediction information and service processing information of customer service personnel; iterating the preset scheduling optimization model according to the traffic prediction information and the traffic processing information by using a preset particle swarm optimization algorithm to obtain a customer service scheduling scheme of each iteration; calculating an objective function of customer service personnel under a customer service scheduling scheme based on a preset fatigue index model and a preset fairness index model, wherein the objective function comprises fatigue index data and fairness index data; and if the objective function reaches the minimum value, determining the corresponding customer service scheduling scheme when the objective function reaches the minimum value as a target customer service scheduling scheme. Therefore, the computer is utilized to optimize the customer service scheduling through an optimization algorithm, the scheduling efficiency is improved, fatigue and fairness are considered in an optimization model to quantify the workload of customer service staff, and the scheduling result is more reasonable. Meanwhile, based on the setting of fairness scheduling between fatigue indexes of customer service personnel and the customer service personnel, the method and the device for scheduling the customer service center work efficiency and the customer service personnel work enthusiasm are guaranteed, so that the business targets of optimizing the customer service scheduling, improving the customer service personnel work efficiency and customer service satisfaction are achieved.
Drawings
Fig. 1 is a flow chart of a customer service scheduling optimization method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of traffic on a workday according to an embodiment of the present application;
FIG. 3 is a schematic diagram of traffic volume for holidays according to an embodiment of the present application;
FIG. 4 is a schematic diagram of integrated fatigue levels shown in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a customer service shift optimizing device according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Referring to fig. 1, fig. 1 is a schematic flow chart of a customer service scheduling optimization method according to an embodiment of the present application. The customer service scheduling optimization method can be applied to computer equipment, wherein the computer equipment comprises, but is not limited to, smart phones, notebook computers, tablet computers, desktop computers, physical servers, cloud servers and the like. As shown in fig. 1, the customer service scheduling optimization method of the present embodiment includes steps S101 to S104, which are described in detail below:
step S101, obtaining service volume prediction information and service processing information of customer service personnel.
In this step, the service is the traffic received by the customer service center, and the related information may send a request to the customer service center for calling.
In some embodiments, the step S101 includes:
predicting the traffic prediction information within a future target time period based on a traffic prediction model;
and calling the service processing information of the customer service personnel to a customer service center, wherein the service processing information comprises the class type, the on-duty time period, the number of the customer service personnel and the average processing service duration.
In the embodiment, predicting the traffic data of 7 days in the future according to the existing traffic prediction model of the customer service center; the existing class type, the on-duty time period, the number of customer service people and the average service processing time length of the customer service center are collected.
Illustratively, a customer service center has 30 customer service personnel to participate in the scheduling, and each customer service personnel has a rest for two days per week; the duty time periods of the early shift, the middle shift and the night shift are respectively 0:00 to 8:00, 8:00 to 16:00 and 16:00 to 24:00, and traffic curves of monday to friday working days and Saturday and sunday are respectively drawn, as shown in fig. 2 and 3. As can be seen from the figure, the peak traffic hours of the workday are mainly two periods of 9:00-12:00 and 15:00-18:00.
And step S102, iterating a preset scheduling optimization model by utilizing a preset particle swarm optimization algorithm according to the traffic volume prediction information and the traffic processing information to obtain a customer service scheduling scheme of each iteration.
In this step, the preset scheduling optimization model includes a customer service scheduling scheme, an objective function, and constraint conditions. Optionally, the expression of the objective function is:
Figure SMS_34
；
wherein ,
Figure SMS_35
for average fatigue index data, < >>
Figure SMS_36
For fairness index data, ++>
Figure SMS_37
and
Figure SMS_38
Is a weight coefficient.
Optionally, the constraint is a call completing rate constraint:
Figure SMS_39
；
wherein ,
Figure SMS_40
traffic call completion rate for day j:
Figure SMS_41
；
wherein ,
Figure SMS_42
customer queuing times exceeding 15 minutes are considered to be connection failure for successfully connected traffic and incoming total traffic, respectively.
Optionally, a particle swarm optimization algorithm is adopted to carry out optimization solution and set particlesThe total number of the sub-numbers is
Figure SMS_43
The number of iteration is->
Figure SMS_44
The location of each particle is a potential solution. In the particle swarm iteration process, under different customer service scheduling schemes corresponding to each particle, calculating a comprehensive objective function, and updating individual extremum and global extremum of each particle so as to update the position and speed of each particle; and (5) readjusting a working day shift matrix and a rest day matrix of the customer service personnel, and finding an optimal scheme of shift arrangement of the customer service personnel through repeated iteration.
Step S103, calculating an objective function of the customer service personnel under the customer service scheduling scheme based on a preset fatigue index model and a preset fairness index model, wherein the objective function comprises fatigue index data and fairness index data.
In this step, optionally, the expression of the preset fairness index model is:
Figure SMS_45
；
wherein ,
Figure SMS_46
for fairness index data, also fatigue bias, +.>
Figure SMS_47
Smaller means more fair scheduling;
Figure SMS_48
For average fatigue index data, < >>
Figure SMS_49
Is->
Figure SMS_50
Comprehensive fatigue index data of individual customer service personnel, < >>
Figure SMS_51
Representing the number of customer service personnel.
Optionally, the expression of the preset fatigue index model is:
Figure SMS_52
；
wherein ,
Figure SMS_53
indicating fatigue index data->
Figure SMS_54
Index data representing the regularity of shifts, +.>
Figure SMS_55
Indicating shift impact index data, < >>
Figure SMS_56
Indicating +.>
Figure SMS_57
Busyness index data of day, +.>
Figure SMS_58
and
Figure SMS_59
Representing the weight coefficient.
In some embodiments, the step S103 includes:
determining fatigue index data of the customer service personnel under the customer service scheduling scheme based on the preset fatigue index model;
determining fairness index data of the customer service personnel under the customer service scheduling scheme based on the preset fairness index model;
and calculating the objective function according to the fatigue index data and the fairness index data.
In this embodiment, optionally, based on the service processing information of the customer service personnel, shift regularity index data, busyness index data and shift impact index data of the customer service personnel under the customer service scheduling scheme are calculated; and calculating the fatigue degree index data according to the shift regularity index data, the busyness index data and the shift influence index data based on the preset fatigue degree index model.
The shift regularity index data are used for measuring the distribution condition of shift arrangement results of single customer service personnel in one week, and the more irregular the distribution is, the larger the influence on the work and rest of the customer service personnel is; the business busyness index data is used for measuring the working intensity of customer service personnel.
Optionally, the calculation formula of the shift regularity index data is:
Figure SMS_60
；
the calculation formula of the busyness index data is as follows:
Figure SMS_61
；
the calculation formula of the shift impact index data is as follows:
Figure SMS_62
；
wherein ,
Figure SMS_63
the interval duration of the start time of the duty of two adjacent workdays;
Figure SMS_64
The average interval duration of the start time of the duty of two adjacent days in a week;
Figure SMS_65
Traffic accepted by customer service personnel;
Figure SMS_66
The time length of the duty of the customer service personnel;
Figure SMS_67
For the duration of the overlap of the on-duty time period and the normal rest time period, the normal rest time period may be 22:00-6:00.
Illustratively, the present embodiment analyzes fairness and customer service fatigue of the optimized shift. Figure 4 plots the overall fatigue of each customer service person after optimization. The results in fig. 4 show that most of the customer service personnel's fatigue remained at similar levels. Table 1 shows the deviation distribution of the integrated fatigue level from the average value of the integrated fatigue level for all customer service personnel. The results in table 1 show that the overall fatigue of the vast majority of customers after optimization deviates from the average by within 20%, up to 80% and that the customers within 50% deviate by 93.3%. Therefore, by implementing the method, the fatigue degree of the customer service personnel can be well balanced, so that fair scheduling is effectively realized, and the method is beneficial to maintaining the work enthusiasm of the customer service personnel.
TABLE 1
Deviation range (%) <20 30 to 50 >50
People number (human) 24 4 2
Duty cycle (%) 80.0 13.3 6.7
And step S104, if the objective function reaches the minimum value, determining the corresponding customer service scheduling scheme when the objective function reaches the minimum value as a target customer service scheduling scheme.
In this step, optionally, the customer service shift scheme includes a workday shift matrix and a rest day matrix, where the workday shift matrix is:
Figure SMS_68
；
wherein ,
Figure SMS_69
is->
Figure SMS_70
Workday shift matrix of individual customer service personnel,/->
Figure SMS_71
Is->
Figure SMS_72
Personal customer service staff at->
Figure SMS_73
The class type for each workday, which may be:
Figure SMS_74
。
optionally, the holiday matrix is:
Figure SMS_75
i.e. the customer service personnel rest for 2 days a week.
Illustratively, to
Figure SMS_76
，
Figure SMS_77
，
Figure SMS_78
The results of the one-week shift of the customer service personnel are shown in table 2, and the traffic on failure rate of one week of the customer service center is shown in table 3.
TABLE 2
Figure SMS_79
TABLE 3 Table 3
Figure SMS_80
The results in table 2 show that the distribution characteristics of the number of customer service personnel on monday to friday are approximately 'more in shift, less in night shift of early shift', and approximately correspond to the traffic distribution condition in fig. 2. After the method is optimally implemented, the traffic completing rate of each day is more than 85%, wherein the completing rate of four days is 100%, which indicates that the method can scientifically arrange the shifts of a week of customer service and effectively meet the traffic demands of customers.
In order to execute the customer service scheduling optimization method corresponding to the method embodiment, corresponding functions and technical effects are realized. Referring to fig. 5, fig. 5 shows a block diagram of a customer service shift optimizing device provided in an embodiment of the present application. For convenience of explanation, only the portion relevant to the present embodiment is shown, and the customer service shift optimizing apparatus provided in the embodiment of the present application includes:
an obtaining module 501, configured to obtain traffic prediction information and service processing information of customer service personnel;
the iteration module 502 is configured to iterate the preset scheduling optimization model according to the traffic prediction information and the traffic processing information by using a preset particle swarm optimization algorithm, so as to obtain a customer service scheduling scheme of each iteration;
a calculating module 503, configured to calculate an objective function of the customer service personnel under the customer service scheduling scheme based on a preset fatigue index model and a preset fairness index model, where the objective function includes fatigue index data and fairness index data;
and the determining module 504 is configured to determine, as the target customer service scheduling scheme, a customer service scheduling scheme corresponding to the minimum objective function if the objective function reaches the minimum.
In some embodiments, the obtaining module 501 is specifically configured to:
predicting the traffic prediction information within a future target time period based on a traffic prediction model;
and calling the service processing information of the customer service personnel to a customer service center, wherein the service processing information comprises the class type, the on-duty time period, the number of the customer service personnel and the average processing service duration.
In some embodiments, the computing module 503 includes:
the first determining unit is used for determining fatigue index data of the customer service personnel under the customer service scheduling scheme based on the preset fatigue index model;
the second determining unit is used for determining fairness index data of the customer service personnel under the customer service scheduling scheme based on the preset fairness index model;
the calculating unit is used for calculating the objective function according to the fatigue index data and the fairness index data, and the expression of the objective function is as follows:
Figure SMS_81
；
wherein ,
Figure SMS_82
for average fatigue index data, < >>
Figure SMS_83
For fairness index data, ++>
Figure SMS_84
and
Figure SMS_85
Is a weight coefficient.
In some embodiments, the first determining unit is specifically configured to:
based on the service processing information of the customer service personnel, calculating shift regularity index data, busyness index data and shift influence index data of the customer service personnel under the customer service scheduling scheme;
based on the preset fatigue index model, calculating the fatigue index data according to the shift regularity index data, the busyness index data and the shift impact index data, wherein the expression of the preset fatigue index model is as follows:
Figure SMS_86
；/>
wherein ,
Figure SMS_87
indicating fatigue index data->
Figure SMS_88
Index data representing the regularity of shifts, +.>
Figure SMS_89
Indicating shift impact index data, < >>
Figure SMS_90
Indicating +.>
Figure SMS_91
Busyness index data of day, +.>
Figure SMS_92
and
Figure SMS_93
Representing the weight coefficient.
In some embodiments, the shift regularity index data has a calculation formula:
Figure SMS_94
；
the calculation formula of the busyness index data is as follows:
Figure SMS_95
；
the calculation formula of the shift impact index data is as follows:
Figure SMS_96
；
wherein ,
Figure SMS_97
the interval duration of the start time of the duty of two adjacent workdays;
Figure SMS_98
The average interval duration of the start time of the duty of two adjacent days in a week;
Figure SMS_99
Traffic accepted by customer service personnel;
Figure SMS_100
The time length of the duty of the customer service personnel;
Figure SMS_101
Is the coincidence duration of the duty time period and the normal rest time period.
In some embodiments, the preset fairness index model has an expression:
Figure SMS_102
；
wherein ,
Figure SMS_103
for fairness index data, ++>
Figure SMS_104
For average fatigue index data, < >>
Figure SMS_105
Is->
Figure SMS_106
Comprehensive fatigue index data of individual customer service personnel, < >>
Figure SMS_107
Representing the number of customer service personnel.
In some embodiments, the customer service shift scheme includes a workday shift matrix and a rest day matrix, wherein the workday shift matrix is:
Figure SMS_108
；
wherein ,
Figure SMS_109
is->
Figure SMS_110
Workday shift matrix of individual customer service personnel,/->
Figure SMS_111
Is->
Figure SMS_112
Personal customer service staff at->
Figure SMS_113
Shift type for each workday.
The customer service scheduling optimization device can implement the customer service scheduling optimization method of the method embodiment. The options in the method embodiments described above are also applicable to this embodiment and will not be described in detail here. The rest of the embodiments of the present application may refer to the content of the method embodiments described above, and in this embodiment, no further description is given.
Fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present application. As shown in fig. 6, the computer device 6 of this embodiment includes: at least one processor 60 (only one is shown in fig. 6), a memory 61 and a computer program 62 stored in the memory 61 and executable on the at least one processor 60, the processor 60 implementing the steps in any of the method embodiments described above when executing the computer program 62.
The computer device 6 may be a smart phone, a tablet computer, a desktop computer, a cloud server, or the like. The computer device may include, but is not limited to, a processor 60, a memory 61. It will be appreciated by those skilled in the art that fig. 6 is merely an example of computer device 6 and is not intended to be limiting of computer device 6, and may include more or fewer components than shown, or may combine certain components, or different components, such as may also include input-output devices, network access devices, etc.
The processor 60 may be a central processing unit (Central Processing Unit, CPU), the processor 60 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 61 may in some embodiments be an internal storage unit of the computer device 6, such as a hard disk or a memory of the computer device 6. The memory 61 may in other embodiments also be an external storage device of the computer device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the computer device 6. The memory 61 is used for storing an operating system, application programs, boot loader (BootLoader), data, other programs, etc., such as program codes of the computer program. The memory 61 may also be used for temporarily storing data that has been output or is to be output.
In addition, the embodiment of the present application further provides a computer readable storage medium, where a computer program is stored, where the computer program is executed by a processor to implement the steps in any of the above-mentioned method embodiments.
The present embodiments provide a computer program product which, when run on a computer device, causes the computer device to perform the steps of the method embodiments described above.
In several embodiments provided herein, it will be understood that 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.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device to perform all or part of the steps of the method described in the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing embodiments have been provided for the purpose of illustrating the objects, technical solutions and advantages of the present application in further detail, and it should be understood that the foregoing embodiments are merely examples of the present application and are not intended to limit the scope of the present application. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art, which are within the spirit and principles of the present application, are intended to be included within the scope of the present application.

Claims (10)

1. The customer service scheduling optimization method is characterized by comprising the following steps of:
acquiring service volume prediction information and service processing information of customer service personnel;
iterating a preset scheduling optimization model by using a preset particle swarm optimization algorithm according to the traffic prediction information and the traffic processing information to obtain a customer service scheduling scheme of each iteration;
calculating an objective function of the customer service personnel under the customer service scheduling scheme based on a preset fatigue index model and a preset fairness index model, wherein the objective function comprises fatigue index data and fairness index data;
and if the objective function reaches the minimum value, determining the corresponding customer service scheduling scheme when the objective function reaches the minimum value as a target customer service scheduling scheme.
2. The customer service shift optimization method as claimed in claim 1, wherein the acquiring the traffic prediction information and the traffic processing information of the customer service personnel comprises:
predicting the traffic prediction information within a future target time period based on a traffic prediction model;
and calling the service processing information of the customer service personnel to a customer service center, wherein the service processing information comprises the class type, the on-duty time period, the number of the customer service personnel and the average processing service duration.
3. The customer service shift optimization method according to claim 1, wherein the calculating an objective function of the customer service person under the customer service shift scheme based on a preset fatigue index model and a preset fairness index model, the objective function including fatigue index data and fairness index data comprises:
determining fatigue index data of the customer service personnel under the customer service scheduling scheme based on the preset fatigue index model;
determining fairness index data of the customer service personnel under the customer service scheduling scheme based on the preset fairness index model;
calculating the objective function according to the fatigue index data and the fairness index data, wherein the expression of the objective function is as follows:
Figure QLYQS_1
；
wherein ,
Figure QLYQS_2
for average fatigue index data, < >>
Figure QLYQS_3
For fairness index data, ++>
Figure QLYQS_4
and
Figure QLYQS_5
Is a weight coefficient.
4. The customer service shift optimization method of claim 3, wherein the determining fatigue index data of the customer service personnel under the customer service shift scheme based on the preset fatigue index model comprises:
based on the service processing information of the customer service personnel, calculating shift regularity index data, busyness index data and shift influence index data of the customer service personnel under the customer service scheduling scheme;
based on the preset fatigue index model, calculating the fatigue index data according to the shift regularity index data, the busyness index data and the shift impact index data, wherein the expression of the preset fatigue index model is as follows:
Figure QLYQS_6
；
wherein ,
Figure QLYQS_7
indicating fatigue index data->
Figure QLYQS_8
Index data representing the regularity of shifts, +.>
Figure QLYQS_9
Indicating shift impact index data, < >>
Figure QLYQS_10
Indicating +.>
Figure QLYQS_11
Busyness index data of day, +.>
Figure QLYQS_12
and
Figure QLYQS_13
Representing the weight coefficient.
5. The customer service shift optimization method of claim 4, wherein the shift regularity index data has a calculation formula:
Figure QLYQS_14
；/>
the calculation formula of the busyness index data is as follows:
Figure QLYQS_15
；
the calculation formula of the shift impact index data is as follows:
Figure QLYQS_16
；
wherein ,
Figure QLYQS_17
the interval duration of the start time of the duty of two adjacent workdays;
Figure QLYQS_18
The average interval duration of the start time of the duty of two adjacent days in a week;
Figure QLYQS_19
Traffic accepted by customer service personnel;
Figure QLYQS_20
The time length of the duty of the customer service personnel;
Figure QLYQS_21
Is the coincidence duration of the duty time period and the normal rest time period.
6. The customer service shift optimization method of claim 3, wherein the preset fairness index model has an expression of:
Figure QLYQS_22
；
wherein ,
Figure QLYQS_23
in order for the fairness index data to be relevant,
Figure QLYQS_24
for average fatigue index data, < >>
Figure QLYQS_25
Is->
Figure QLYQS_26
Comprehensive fatigue index data of individual customer service personnel, < >>
Figure QLYQS_27
Representing the number of customer service personnel.
7. The customer service shift optimization method of claim 1, wherein the customer service shift scheme comprises a workday shift matrix and a rest day matrix, wherein the workday shift matrix is:
Figure QLYQS_28
；
wherein ,
Figure QLYQS_29
is->
Figure QLYQS_30
Workday shift matrix of individual customer service personnel,/->
Figure QLYQS_31
Is->
Figure QLYQS_32
Personal customer service staff at->
Figure QLYQS_33
Shift type for each workday.
8. A customer service shift optimizing apparatus, comprising:
the acquisition module is used for acquiring the service volume prediction information and the service processing information of the customer service personnel;
the iteration module is used for iterating the preset scheduling optimization model according to the traffic prediction information and the traffic processing information by using a preset particle swarm optimization algorithm to obtain a customer service scheduling scheme of each iteration;
the calculation module is used for calculating an objective function of the customer service personnel under the customer service scheduling scheme based on a preset fatigue index model and a preset fairness index model, wherein the objective function comprises fatigue index data and fairness index data;
and the determining module is used for determining the corresponding customer service scheduling scheme as a target customer service scheduling scheme when the objective function reaches the minimum value if the objective function reaches the minimum value.
9. A computer device comprising a processor and a memory for storing a computer program which when executed by the processor implements the customer service shift optimization method of any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the customer service shift optimization method according to any one of claims 1 to 7.
CN202310483804.0A 2023-05-04 2023-05-04 Customer service scheduling optimization method, customer service scheduling optimization device, customer service scheduling optimization equipment and storage medium Pending CN116187728A (en)

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