WO2019223150A1

WO2019223150A1 - Scheduling method, server, and computer readable storage medium

Info

Publication number: WO2019223150A1
Application number: PCT/CN2018/102214
Authority: WO
Inventors: 万晓辉
Original assignee: 平安科技（深圳）有限公司
Priority date: 2018-05-23
Filing date: 2018-08-24
Publication date: 2019-11-28
Also published as: CN108764699A

Abstract

A scheduling method, a server (2) and a computer readable storage medium, the method comprising: setting rest days and work days of a time interval according to a preset date policy (S400, S500); obtaining historical workload data, and predicting the workload in the time interval according to the historical workload data (S402, S502); calculating required manpower according to the predicted workload and a reference workload for a day of manpower (S404, S504); and carrying out scheduling on the set work days according to the required manpower and preset constraint conditions, and generating a scheduling table (S406, S506), wherein the preset constraint conditions comprise a hard constraint condition and a soft constraint condition. The present scheduling method, server and computer readable storage medium may meet personalized demands of employees in a scheduling process and guarantee scheduling fairness, thereby improving the work efficiency of employees.

Description

Scheduling method, server and computer-readable storage medium

This application claims priority from a Chinese patent application filed with the Chinese Patent Office on May 23, 2018 with the application number 201810502334.7 and the invention name "Scheduling Method, Server, and Computer-readable Storage Medium", the entire contents of which are incorporated by reference In this application.

Technical field

The present application relates to the field of information processing technology, and in particular, to a scheduling method, a server, and a computer-readable storage medium.

Background technique

Most of the existing shift management technology architecture adopts client / server architecture, the system upgrade is difficult, the management process is not perfect, it can not meet the diverse scheduling requirements of the team, nor can it apply to different scheduling requirements in different regions. In addition, the contradiction between fairness and efficiency has become increasingly prominent in the scheduling process, and employee comfort has become an important factor to be considered in the scheduling process. The existing scheduling methods are difficult to balance fairness and efficiency. The holiday factor has a greater impact on employee productivity.

Summary of the Invention

In view of this, this application proposes a scheduling method, a server, and a computer-readable storage medium, which can realize flexible management of various regions and teams, and each team can also flexibly adjust the work schedule according to actual needs. Meet the individual needs of employees, ensure the fairness of scheduling, and improve employee efficiency.

First, in order to achieve the above object, the present application proposes a server. The server includes a memory and a processor. The memory stores a scheduling program that can be run on the processor. The scheduling program is described by the server. The processor executes the following steps:

Set rest days and working days in a time interval according to a preset date policy;

Acquiring historical workload data and predicting the workload in the time interval according to the historical workload data;

Calculate the required manpower based on the predicted workload and a man-day baseline workload; and

Perform scheduling within the set working day according to the required manpower and preset constraints, and generate a scheduling table;

The preset constraint condition includes one of the following conditions or a combination of multiple conditions: the length of the shift, the total number of rest days, the number of consecutive working days, the balance of the total working hours, the time interval between shifts, and the same shift of the same group of personnel.

In addition, in order to achieve the above object, the present application also provides a scheduling method, which is applied to a server, and the scheduling method includes:

Further, in order to achieve the above object, the present application further provides a computer-readable storage medium, where the computer-readable storage medium stores a scheduling program, and the scheduling program may be executed by at least one processor, so that the scheduling program At least one processor performs the steps of the scheduling method as described above.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an optional hardware architecture of a server of the present application;

FIG. 2 is a schematic diagram of a program module of the first embodiment of the scheduling program of the present application; FIG.

3 is a schematic diagram of a program module according to a second embodiment of the scheduling program of the present application;

FIG. 4 is a schematic flowchart of implementation of a first embodiment of a scheduling method of this application; FIG.

FIG. 5 is a schematic flowchart of implementation of a second embodiment of a scheduling method of the present application.

Reference signs:

服务器server	22
存储器Memory	1111
处理器processor	1212
网络接口Network Interface	1313
排班程序 Scheduling procedure	100100
设定模块Setting the module	101101
预测模块 Prediction module	102102
计算模块 Calculation module	103103
生成模块 Generate module	104104
记录模块 Recording module	105105

The implementation, functional characteristics and advantages of the purpose of this application will be further described with reference to the embodiments and the drawings.

Detailed ways

In order to make the purpose, technical solution, and advantages of the present application clearer, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the application, and are not used to limit the application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

It should be noted that the descriptions related to "first", "second", etc. in this application are for descriptive purposes only, and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated . Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but must be based on those that can be realized by a person of ordinary skill in the art. When the combination of technical solutions conflicts or cannot be achieved, such a combination of technical solutions should be considered nonexistent. Is not within the scope of protection claimed in this application.

FIG. 1 is a schematic diagram of an optional hardware architecture of the server 2 of the present application.

In this embodiment, the server 2 may include, but is not limited to, a memory 11, a processor 12, and a network interface 13 which may communicate with each other through a system bus. It should be noted that FIG. 1 only shows the server 2 with components 11-13, but it should be understood that it is not required to implement all the illustrated components, and more or fewer components may be implemented instead.

The server 2 may be a computing device such as a rack server, a blade server, a tower server, or a rack server. The server 2 may be an independent server or a server cluster composed of multiple servers.

The memory 11 includes at least one type of readable storage medium. The readable storage medium includes a flash memory, a hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), a random access memory (RAM), and a static memory. Random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disks, optical disks, etc. In some embodiments, the memory 11 may be an internal storage unit of the server 2, such as a hard disk or a memory of the server 2. In other embodiments, the memory 11 may also be an external storage device of the server 2, such as a plug-in hard disk, a smart memory card (SMC), and a secure digital (Secure) Digital, SD) card, Flash card, etc. Of course, the memory 11 may also include both an internal storage unit of the server 2 and an external storage device thereof. In this embodiment, the memory 11 is generally used to store an operating system and various application software installed on the server 2, such as program codes of the scheduling program 100. In addition, the memory 11 may also be used to temporarily store various types of data that have been output or will be output.

In some embodiments, the processor 12 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or another data processing chip. The processor 12 is generally used to control the overall operation of the server 2. In this embodiment, the processor 12 is configured to run program code or process data stored in the memory 11, for example, to run the scheduling program 100. The front-end interaction of the scheduling program 100 can adopt Browser / Server (BS) architecture and bootstrap front-end display framework.

The network interface 13 may include a wireless network interface or a wired network interface. The network interface 13 is generally used to establish a communication connection between the server 2 and other electronic devices.

So far, the hardware structure and functions of the related equipment of this application have been introduced in detail. Hereinafter, various embodiments of the present application will be made based on the above description.

First, the present application proposes a scheduling procedure 100.

Referring to FIG. 2, it is a program module diagram of the first embodiment of the scheduling program 100 of the present application.

In this embodiment, the scheduling program 100 includes a series of computer program instructions stored in the memory 11. When the computer program instructions are executed by the processor 12, the scheduling operation of the embodiments of the present application can be implemented. In some embodiments, the scheduling program 100 may be divided into one or more modules based on specific operations implemented by the various portions of the computer program instructions. For example, in FIG. 2, the scheduling program 100 may be divided into a setting module 101, a prediction module 102, a calculation module 103, and a generation module 104. among them:

The setting module 101 is configured to set a rest day and a work day in a time interval according to a preset date policy.

In an embodiment, the length of the time interval may be set according to actual needs, for example, the time interval may be one month, two months, three months, or the like. Different regions and different teams may have different preset date strategies. The preset date strategy can set rest days and working days, and can set freely the working days and rest days through the preset date strategy according to the actual needs of the team. The rest days may be a default legal rest day and a selected rest day. The selected rest day may be any selected date, and may be selected by the team according to their own needs when formulating the preset date strategy. Intermediation within each team can be set to a selected day off. A day that is not set as a rest day is regarded as the working day. Through the preset date strategy, it is possible to clearly understand which days are the working days and which days are the rest days, and real-time adjustments can be made. The rest days and working days of the time interval set by the setting module 101 need to be greater than the reference rest days and working days, so as to avoid setting too many or too few working days. For example, if the time interval is one month and the corresponding number of days of the base working day is 20 days, the number of days of the month working day set by the setting module 101 should be greater than 20 days.

The prediction module 102 is configured to obtain historical workload data and predict the workload in the time interval according to the historical workload data.

In an embodiment, the forecasting module 102 obtains historical workload data and calculates a monthly regular line with a dimension of month according to the historical workload data, and according to the monthly regular line and a preset monthly workload increase ratio To predict the workload of the time interval.

In an embodiment, the forecasting module 102 obtains the historical workload data and analyzes the historical workload data to remove abnormal data, and calculates the month based on the historical workload data after removing the abnormal data. Is a monthly regular line of the dimension, and then the workload of the time interval is predicted according to the monthly regular line and a preset monthly workload increase ratio.

For example, the forecasting module 102 can realize the monthly workload forecast through the following steps: Step a1: Add up and sum up the monthly workload data of historical year 1; Step a2: Use the monthly serial number of the year ( 1,2, ... 12) are the dimensions and count the monthly workload data; step a3: average the monthly workload data obtained in step a2; step a4: divide the result value obtained in step a2 by step a3 The obtained value is the proportion of each month in the historical year in that year, and then the monthly regular line can be obtained; step a5: predict the monthly workload based on the monthly regular line and the expected monthly workload increase ratio. The monthly workload can be predicted and calculated by the following companies: Monthly workload = (Σ monthly regular line) / number of days in a month * monthly workload increase ratio.

In order to improve the accuracy of the predicted workload, the prediction module 102 is further configured to obtain the predicted workload and the actual workload in multiple historical time intervals, to obtain the actual workload matrix and the predicted workload matrix, and compare the same time interval. Subtract the predicted workload matrix from the actual workload matrix, average the multiple matrices obtained after the subtraction operation to obtain a coefficient adjustment matrix, and finally modify the predicted workload obtained according to the coefficient adjustment matrix. .

In one embodiment, the prediction module 102 multiplies the transposed matrix of the coefficient adjustment matrix and the workload obtained from the initial prediction to obtain a more accurate forecast workload.

For example, the prediction module 102 can modify the workload obtained from the initial prediction through the following steps: Step b1: Set the time interval to N days and the sampling interval to M (M can be in hours); Step b2: Sampling the actual workload in the history of the previous time interval at the current time to obtain the N × M sampling data matrix of the actual workload; Step b3: Sampling the forecast workload in the previous time interval at the current time to obtain the forecast workload N × M sampling data matrix; step b4: sampling the prediction workload of the next time interval at the current time to obtain the N × M sampling data matrix of the next prediction workload; step b5: comparing the sampling points within the time period T And subtract the results of the actual workload data matrix and the predicted workload data matrix in steps b2 and b3 to obtain a coefficient adjustment matrix; step b6: the next predicted workload obtained in step b4 The transposed matrix of the data matrix multiplied by this coefficient adjustment matrix is used as the revised prediction workload.

In one embodiment, when multiple time intervals are set and each time interval is N days (for example, N days in the first quarter, N days in the second quarter, and N days in the third quarter), the above steps b1- may be repeated respectively. b4. In step b5, a coefficient adjustment matrix corresponding to each time interval can be obtained, and an average of each coefficient adjustment matrix can be obtained to obtain a mean coefficient adjustment matrix. Finally, the prediction can be obtained according to the mean coefficient adjustment matrix. The workload is corrected.

In an embodiment, the prediction module 102 may further obtain the actual employee busy-to-empty ratio and the employee predicted busy-to-free ratio in multiple historical time intervals to obtain multiple actual busy-to-idle ratio matrices and multiple predicted busy-to-idle ratio matrices. Subtract the actual busy-to-empty ratio matrix and the predicted busy-to-free ratio matrix in the same time interval, and average the multiple matrices obtained after the subtraction operation to obtain a coefficient adjustment matrix, and finally adjust the matrix pair according to the coefficients. The predicted workload is revised.

For example, the prediction module 102 can modify the workload obtained from the initial prediction through the following steps: Step b1: Set the time interval to N days and the sampling interval to M (M can be in hours); Step b2: Sampling the actual busy-to-idle ratio history of the previous time interval at the current time to obtain the N × M sampling data matrix of the actual busy-to-idle ratio; Step b3: Sampling the predicted busy-to-idle ratio of the previous time interval at the current time to obtain N × M sampling data matrix for predicting the busy-to-empty ratio; step b4: sampling the prediction workload of the next time interval at the current time to obtain the N × M sampling data matrix for the next prediction workload; step b5: comparing time periods The busy-to-empty ratio data at the sampling points in T, and subtracting the results of the actual busy-to-free ratio data matrix and the predicted busy-to-free ratio data matrix in steps b2 and b3 to obtain a coefficient adjustment matrix; step b6: step b4 The obtained next prediction workload data matrix is multiplied by the transposed matrix of the coefficient adjustment matrix as the revised prediction workload.

The calculation module 103 is configured to calculate the required manpower according to the predicted workload and the manpower day benchmark workload.

In an embodiment, the calculation module 103 may calculate a required manpower by dividing the predicted workload with a manpower day reference workload, and the required manpower may be calculated by the following formula: required manpower = predicted Workload / Human Day benchmark workload.

In one embodiment, the calculation module 103 is configured to calculate the required manpower according to the predicted workload, the manpower daily reference workload, and a desired work efficiency. The benchmark value of work efficiency is 100%. The desired working efficiency may be 110%, 105%, or the like. The required manpower can also be calculated by the following formula: required manpower = predicted workload / (manpower day benchmark workload * expected work efficiency).

The generating module 104 is configured to perform scheduling within the set working day according to the required manpower and preset constraints, and generate a scheduling table. The preset constraint condition includes one of the following conditions or a combination of multiple conditions: the length of the shift, the total number of rest days, the number of consecutive working days, the balance of the total working hours, the time interval between shifts, and the same shift of the same group of personnel.

In an implementation manner, the preset constraint condition may be divided into a hard constraint condition and a soft constraint condition. The hard constraint condition may refer to a constraint condition that must be considered during scheduling, and the soft constraint condition may refer to a constraint condition that can be selected for consideration during scheduling. The hard constraints may include a limit on the total number of days of rest during the scheduling cycle, a rotation rule, a limit on the number of consecutive days of work, a requirement for the total number of working hours in the scheduling cycle, and the number of scheduled personnel in each period must not exceed the total number of scheduled personnel. . The soft constraints may include personalized preferences for scheduled shifts, the balance of brutal classes such as statutory holiday classes, night shifts, the balance of weekend shifts, the balance of weekends, the same team members as above or below, avoiding emergency transfers, etc. .

Through the above-mentioned program modules 101-104, the scheduling program 100 proposed in the present application firstly sets a rest day and a working day in a time interval according to a preset date policy; secondly, it obtains historical workload data and according to the historical work The amount of data is used to predict the workload of the time interval; further, the required manpower is calculated according to the predicted workload and the man-day benchmark workload; finally, according to the required manpower and preset constraints, the setting is performed in the setting. Scheduling and generating schedules within the working day. In this way, flexible management of each region and each team can be achieved, and each team can also flexibly adjust the work arrangement according to actual needs, which is suitable for different team requirements in different regions. During the scheduling process, it can meet the individual needs of employees and ensure the scheduling Fairness improves employees' work efficiency, and the front-end interaction of the scheduling process can give employees a simple and generous sense of use.

Referring to FIG. 3, it is a program module diagram of the second embodiment of the scheduling program 100 of the present application. In this embodiment, the scheduling program 100 includes a series of computer program instructions stored in the memory 11. When the computer program instructions are executed by the processor 12, the scheduling operation of the embodiments of the present application can be implemented. In some embodiments, the scheduling program 100 may be divided into one or more modules based on specific operations implemented by the various portions of the computer program instructions. For example, in FIG. 3, the scheduling program 100 may be divided into a setting module 101, a prediction module 102, a calculation module 103, a generation module 104, and a recording module 105. Each of the program modules 101-104 is the same as the first embodiment of the schedule program 100 of the present application, and a recording module 105 is added on this basis. among them:

For example, the forecasting module 102 can realize the monthly workload forecast through the following steps: Step a1: Add up and sum up the monthly workload data of historical year 1; Step a2: Use the monthly serial number of the year ( 1,2, ... 12) are the dimensions and count the monthly workload data; step a3: average the monthly workload data obtained in step a2; step a4: divide the result value obtained in step a2 by step a3 The obtained value is the proportion of each month in the historical year in that year, and then the monthly regular line can be obtained; step a5: predict the monthly workload based on the monthly regular line and the expected monthly workload increase ratio. The monthly workload mentioned above can be calculated by the following companies: Monthly workload = (Σ monthly regular line) / number of days in a month * monthly workload increase ratio.

For example, the prediction module 102 can modify the workload obtained from the initial prediction through the following steps: Step b1: Set the time interval to N days and the sampling interval to M (M can be in hours); Step b2: Sampling the actual busy-to-idle ratio history of the previous time interval at the current time to obtain an N × M sampling data matrix of the actual busy-to-idle ratio; Step b3: Sampling the predicted busy-to-idle ratio of the previous time interval to the current time N × M sampling data matrix for predicting the busy-to-empty ratio; step b4: sampling the prediction workload of the next time interval at the current time to obtain the N × M sampling data matrix for the next prediction workload; step b5: comparing time periods The busy-to-empty ratio data at the sampling points in T, and subtracting the results of the actual busy-to-free ratio data matrix and the predicted busy-to-free ratio data matrix in steps b2 and b3 to obtain a coefficient adjustment matrix; step b6: step b4 The obtained next prediction workload data matrix is multiplied by the transposed matrix of the coefficient adjustment matrix as the revised prediction workload.

In one embodiment, when multiple time intervals are set and each time interval is N days (for example, N days in the first quarter, N days in the second quarter, and N days in the third quarter), the above steps b1- may be repeated respectively. b4. In step b5, a coefficient adjustment matrix corresponding to each time interval can be obtained, and an average of each coefficient adjustment matrix can be obtained to obtain a mean coefficient adjustment matrix. Finally, the prediction can be obtained according to the mean coefficient adjustment matrix. The workload is corrected. The calculation module 103 is configured to calculate the required manpower according to the predicted workload and the manpower day benchmark workload.

The recording module 105 is configured to record the attendance and work information of each scheduled employee according to the schedule to output a schedule compliance report.

In an implementation manner, the schedule compliance report may be displayed by displaying two icons on the schedule compliance of employees. One of the icons can be used to show the employee's work status during the day, and the other icon can be used to show the employee's attendance exception information. When viewing employee schedule compliance information, you can view it according to site, team, and designated employee methods, and export corresponding report information. The report information may include site, team, and individual daily compliance reports / monthly compliance reports, abnormal attendance reports, and the like.

Through the above-mentioned program modules 101-105, the scheduling program 100 proposed in this application firstly sets a rest day and a working day in a time interval according to a preset date policy; secondly, obtains historical workload data and works according to the historical work The amount of data is used to predict the workload of the time interval; further, the required manpower is calculated according to the predicted workload and the man-day benchmark workload; and further, according to the required manpower and preset constraint conditions in the setting Scheduling is performed within a fixed working day, and a schedule is generated; finally, the attendance and work information of each scheduled employee is recorded according to the schedule to output a schedule compliance report. In this way, flexible management of each region and each team can be achieved, and each team can also flexibly adjust the work arrangement according to actual needs, which is suitable for different team requirements in different regions. During the scheduling process, it can meet the individual needs of employees and ensure the scheduling Fairness improves employees' work efficiency, and the front-end interaction of the scheduling process can give employees a simple and generous sense of use.

In addition, this application also proposes a scheduling method.

Refer to FIG. 4, which is a schematic diagram of an implementation process of a first embodiment of a scheduling method of the present application. In this embodiment, according to different requirements, the execution order of the steps in the flowchart shown in FIG. 4 may be changed, and some steps may be omitted.

Step S400: Set a rest day and a work day in a time interval according to a preset date policy.

In an embodiment, the length of the time interval may be set according to actual needs, for example, the time interval may be one month, two months, three months, or the like. Different regions and different teams may have different preset date strategies. The preset date strategy can set rest days and working days, and can set freely the working days and rest days through the preset date strategy according to the actual needs of the team. The rest days may be a default legal rest day and a selected rest day. The selected rest day may be any selected date, and may be selected by the team according to their own needs when formulating the preset date strategy. Intermediation within each team can be set to a selected day off. A day that is not set as a rest day is regarded as the working day. Through the preset date strategy, it is possible to clearly understand which days are the working days and which days are the rest days, and real-time adjustments can be made. The rest days and working days of the time interval set must be greater than the reference rest days and working days, so as to avoid setting too many or too few working days. For example, if the time interval is one month and the corresponding number of days of the base working day is 20 days, the set number of days of the monthly working day should be greater than 20 days.

Step S402: Obtain historical workload data and predict the workload in the time interval according to the historical workload data.

In an embodiment, historical workload data is obtained and a monthly regular line with a month dimension is calculated based on the historical workload data, and the time is measured according to the monthly regular line and a preset monthly workload increase ratio. Interval workload forecast.

In one embodiment, the historical workload data is acquired and analyzed to analyze the historical workload data to remove abnormal data, and the monthly rule with the dimension of month is calculated based on the historical workload data after removing the abnormal data. And then predict the workload of the time interval according to the monthly regular line and a preset monthly workload increase ratio.

For example, the monthly workload forecast can be achieved by the following steps: Step a1: Add up and sum up the monthly workload data of historical year 1; Step a2: Use the monthly serial number (1,2 ,. ..12) Count the monthly workload data; step a3: average the monthly workload data obtained in step a2; step a4: divide the result value obtained in step a2 by the result value obtained in step a3 to obtain The proportion of each month in the historical year in that year, and then the monthly regular line can be obtained; step a5: predict the monthly workload based on the monthly regular line and the expected monthly workload increase ratio. The monthly workload can be predicted and calculated by the following companies: Monthly workload = (Σ monthly regular line) / number of days in a month * monthly workload increase ratio.

In order to improve the accuracy of the predicted workload, it is also used to obtain the predicted workload and the actual workload in multiple historical time intervals to obtain the actual workload matrix and the predicted workload matrix, and to predict the workload matrix in the same time interval Subtracting from the actual workload matrix, averaging a plurality of matrices obtained after the subtraction operation to obtain a coefficient adjustment matrix, and finally modifying the predicted workload obtained according to the coefficient adjustment matrix.

In one embodiment, the transposed matrix of the coefficient adjustment matrix is multiplied with the workload obtained from the initial prediction, thereby obtaining a more accurate forecast workload.

For example, the workload of the initial prediction can be modified by the following steps: Step b1: Set the time interval to N days and the sampling interval to M (M can be in hours); Step b2: To the previous of the current time Sampling the actual workload of a time interval history to obtain the N × M sampling data matrix of the actual workload; Step b3: Sampling the predicted workload of the previous time interval at the current time to obtain N × M samples of the predicted workload Data matrix; step b4: sampling the prediction workload of the next time interval at the current time to obtain the N × M sampling data matrix of the next prediction workload; step b5: comparing the workload data of the sampling points within the time period T, And subtracting the results of the actual workload data matrix and the predicted workload data matrix in steps b2 and b3 to obtain a coefficient adjustment matrix; step b6: multiplying the next predicted workload data matrix obtained in step b4 by this The transposed matrix of the coefficient adjustment matrix is used as the revised prediction workload.

In an embodiment, the actual busy-to-free ratio of employees and the predicted free-to-free ratio of employees can also be obtained in multiple historical time intervals to obtain multiple actual busy-to-free ratio matrices and multiple predicted busy-to-free ratio matrices, and the same time interval Subtract the actual busy-busy-ratio matrix from the predicted free-busy-ratio matrix, average the multiple matrices obtained after the subtraction operation to obtain a coefficient adjustment matrix, and finally predict the workload obtained from the coefficient adjustment matrix Make corrections.

In step S404, the required manpower is calculated according to the predicted workload and the man-day benchmark workload.

In an embodiment, the predicted workload can be divided by a man-hour base workload to calculate the required manpower, and the required manpower can be calculated by the following formula: demand manpower = predicted workload / manpower day Baseline workload.

In one embodiment, the method is used to calculate the required manpower according to the predicted workload, the manpower daily reference workload, and a desired work efficiency. The benchmark value of work efficiency is 100%. The desired working efficiency may be 110%, 105%, or the like. The required manpower can also be calculated by the following formula: required manpower = predicted workload / (manpower day benchmark workload * expected work efficiency).

In step S406, scheduling is performed within the set working day according to the required manpower and preset constraints, and a scheduling table is generated. The preset constraint condition includes one of the following conditions or a combination of multiple conditions: the length of the shift, the total number of rest days, the number of consecutive working days, the balance of the total working hours, the time interval between shifts, and the same shift of the same group of personnel.

Through the above steps S400-S406, the scheduling method proposed in the present application firstly sets a rest day and a working day of a time interval according to a preset date policy; secondly, obtains historical workload data and according to the historical workload data Predicting the workload in the time interval; further, calculating the required manpower according to the predicted workload and the man-day benchmark workload; finally, according to the required manpower and preset constraints, in the set work Schedule shifts within the day and generate a schedule. In this way, flexible management of each region and each team can be achieved, and each team can also flexibly adjust the work arrangement according to actual needs, which is suitable for different team requirements in different regions. During the scheduling process, it can meet the individual needs of employees and ensure the scheduling Fairness improves employees' work efficiency, and the front-end interaction of the scheduling process can give employees a simple and generous sense of use.

Refer to FIG. 5, which is a schematic diagram of an implementation process of a second embodiment of a scheduling method according to the present application. In this embodiment, according to different requirements, the execution order of the steps in the flowchart shown in FIG. 5 may be changed, and some steps may be omitted.

Step S500: Set a rest day and a work day in a time interval according to a preset date policy.

Step S502: Obtain historical workload data and predict the workload in the time interval according to the historical workload data.

For example, the workload of the initial prediction can be modified by the following steps: Step b1: Set the time interval to N days and the sampling interval to M (M can be in hours); Step b2: To the previous of the current time Sampling the actual workload of a time interval history to obtain the N × M sampling data matrix of the actual workload; Step b3: Sampling the predicted workload of the previous time interval at the current time to obtain N × M samples of the predicted workload Data matrix; step b4: sampling the prediction workload of the next time interval at the current time to obtain the N × M sampling data matrix of the next prediction workload; step b5: comparing the workload data of the sampling points within the time period T And subtracting the results of the actual workload data matrix and the predicted workload data matrix in steps b2 and b3 to obtain a coefficient adjustment matrix; step b6: multiplying the next predicted workload data matrix obtained in step b4 by this The transposed matrix of the coefficient adjustment matrix is used as the revised prediction workload.

In an embodiment, the actual busy-to-free ratio of employees and the predicted free-to-free ratio of employees can also be obtained in multiple historical time intervals to obtain multiple actual busy-to-free ratio matrices and multiple predicted busy-to-free ratio matrices, and the same time interval can be obtained. Subtract the actual busy-busy-ratio matrix from the predicted free-busy-ratio matrix, average the multiple matrices obtained after the subtraction operation to obtain a coefficient adjustment matrix, and finally predict the workload obtained from the coefficient adjustment matrix Make corrections.

For example, the prediction module 102 can modify the workload obtained from the initial prediction through the following steps: Step b1: Set the time interval to N days and the sampling interval to M (M can be in hours); Step b2: Sampling the actual busy-to-idle ratio history of the previous time interval at the current time to obtain the N × M sampling data matrix of the actual busy-to-idle ratio; Step b3: Sampling the predicted busy-to-idle ratio of the previous time interval at the current time to obtain N × M sampling data matrix for predicting the busy-to-empty ratio; step b4: sampling the prediction workload of the next time interval at the current time to obtain the N × M sampling data matrix for the next prediction workload; step b5: comparing time periods The busy-to-empty ratio data at the sampling points in T, and subtracting the results of the actual busy-to-free ratio data matrix and the predicted busy-to-free ratio data matrix in steps b2 and b3 to obtain a coefficient adjustment matrix; step b6: The obtained next prediction workload data matrix is multiplied by the transposed matrix of the coefficient adjustment matrix as the revised prediction workload.

In step S504, the required manpower is calculated according to the predicted workload and the manpower day benchmark workload.

In step S506, according to the required manpower and preset constraints, scheduling is performed within the set working day, and a scheduling table is generated. The preset constraint condition includes one of the following conditions or a combination of multiple conditions: the length of the shift, the total number of rest days, the number of consecutive working days, the balance of the total working hours, the time interval between shifts, and the same shift of the same group of personnel.

Step S508: Record the attendance and work information of each scheduled employee according to the schedule to output a schedule compliance report.

Through the above steps S500-S508, the scheduling method proposed in this application firstly sets a rest day and a working day in a time interval according to a preset date policy; secondly, obtains historical workload data and according to the historical workload data Predict the workload in the time interval; further, calculate the required manpower according to the predicted workload and the man-day benchmark workload; further, based on the required manpower and preset constraints in the set Scheduling is performed within the working day, and a schedule is generated; finally, the attendance and work information of each scheduled employee is recorded according to the schedule to output a schedule compliance report. In this way, flexible management of each region and each team can be achieved, and each team can also flexibly adjust the work arrangement according to actual needs, which is suitable for different team requirements in different regions. During the scheduling process, it can meet the individual needs of employees and ensure the scheduling Fairness improves employees' work efficiency, and the front-end interaction of the scheduling process can give employees a simple and generous sense of use.

The above-mentioned serial numbers of the embodiments of the present application are merely for description, and do not represent the superiority or inferiority of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the methods in the above embodiments can be implemented by means of software plus a necessary universal hardware platform, and of course, also by hardware, but in many cases the former is better. Implementation. Based on such an understanding, the technical solution of this application that is essentially or contributes to the existing technology can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, The optical disc) includes several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in the embodiments of the present application.

The above are only preferred embodiments of the present application, and thus do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the description and drawings of the application, or directly or indirectly used in other related technical fields Are included in the scope of patent protection of this application.

Claims

A scheduling method applied to a server is characterized in that the scheduling method includes:

Set rest days and working days in a time interval according to a preset date policy;

Acquiring historical workload data and predicting the workload in the time interval according to the historical workload data;

Calculate the required manpower based on the predicted workload and a man-day baseline workload; and

Perform scheduling within the set working day according to the required manpower and preset constraints, and generate a scheduling table;

The preset constraint condition includes one of the following conditions or a combination of multiple conditions: the length of the shift, the total number of rest days, the number of consecutive working days, the balance of the total working hours, the time interval between shifts, and the same shift of the same group of personnel.
The scheduling method according to claim 1, wherein the steps of obtaining historical workload data and predicting the workload in the time interval according to the historical workload data comprise:

Acquiring the historical workload data and analyzing the historical workload data to eliminate abnormal data;

Calculate a monthly regular line with monthly dimensions based on historical workload data after excluding abnormal data; and

Predicting the workload of the time interval according to the monthly regular line and a preset monthly workload increase ratio;

Wherein, the workload in the time interval = (Σ monthly regular line) / number of days in a month * month workload increase ratio.
The scheduling method according to claim 1, wherein the step of calculating the required manpower based on the predicted workload and the man-day benchmark workload comprises:

Calculating the required manpower according to the predicted workload, the manpower day benchmark workload, and a manpower day work efficiency;

Wherein, the required manpower = predicted workload / (manual daily reference workload * manual daily work efficiency).
The scheduling method according to claim 1, wherein the scheduling method further comprises:

Obtaining the predicted workload and the actual workload in multiple historical time intervals to obtain the actual workload matrix and the predicted workload matrix;

Subtracting the predicted workload matrix from the actual workload matrix in the same time interval, and averaging multiple matrices obtained after the subtraction operation to obtain a coefficient adjustment matrix; and

The workload obtained by the prediction is modified according to the coefficient adjustment matrix, so that the required manpower is calculated according to the workload obtained by the corrected prediction.
The scheduling method according to claim 1, wherein the scheduling method further comprises:

Obtain actual employee busy-to-free ratios and employee predicted busy-to-free ratios in multiple historical time intervals to obtain multiple actual busy-to-free ratio matrices and multiple predicted busy-to-free ratio matrices;

Subtracting the actual busy-to-empty ratio matrix and the predicted busy-to-free ratio matrix in the same time interval, and averaging a plurality of matrices obtained after the subtraction operation to obtain a coefficient adjustment matrix; and

The workload obtained by the prediction is modified according to the coefficient adjustment matrix, so that the required manpower is calculated according to the workload obtained by the corrected prediction.
The scheduling method according to claim 1, wherein the scheduling method further comprises:

Record the attendance and work information of each scheduled employee according to the schedule to output a schedule compliance report.
The scheduling method according to any one of claims 2-5, wherein the scheduling method further comprises:

Record the attendance and work information of each scheduled employee according to the schedule to output a schedule compliance report.
A server is characterized in that the server includes a memory and a processor, and the memory stores a scheduling program that can be run on the processor, and the scheduling program is implemented as follows when executed by the processor: step:

Set rest days and working days in a time interval according to a preset date policy;

Acquiring historical workload data and predicting the workload in the time interval according to the historical workload data;

Calculate the required manpower based on the predicted workload and a man-day baseline workload; and

Perform scheduling within the set working day according to the required manpower and preset constraints, and generate a scheduling table;

The preset constraint condition includes one of the following conditions or a combination of multiple conditions: the length of the shift, the total number of rest days, the number of consecutive working days, the balance of the total working hours, the time interval between shifts, and the same shift of the same group of personnel.
The server according to claim 8, wherein the steps of obtaining historical workload data and predicting the workload in the time interval according to the historical workload data comprise:

Acquiring the historical workload data and analyzing the historical workload data to eliminate abnormal data;

Calculate a monthly regular line with monthly dimensions based on historical workload data after excluding abnormal data; and

Predicting the workload of the time interval according to the monthly regular line and a preset monthly workload increase ratio;

Wherein, the workload in the time interval = (Σ monthly regular line) / number of days in a month * month workload increase ratio.
The server according to claim 8, characterized in that the step of calculating the required manpower according to the predicted workload and the manpower daily reference workload comprises:

Calculating the required manpower according to the predicted workload, the manpower day benchmark workload, and a manpower day work efficiency;

Wherein, the required manpower = predicted workload / (manual daily reference workload * manual daily work efficiency).
The server according to claim 8, characterized in that, when the scheduling program is executed by the processor, the following steps are further implemented:

Obtaining the predicted workload and the actual workload in multiple historical time intervals to obtain the actual workload matrix and the predicted workload matrix;

Subtracting the predicted workload matrix from the actual workload matrix in the same time interval, and averaging multiple matrices obtained after the subtraction operation to obtain a coefficient adjustment matrix; and

The workload obtained by the prediction is modified according to the coefficient adjustment matrix, so that the required manpower is calculated according to the workload obtained by the corrected prediction.
The server according to claim 8, characterized in that, when the scheduling program is executed by the processor, the following steps are further implemented:

Obtain actual employee busy-to-free ratios and employee predicted busy-to-free ratios in multiple historical time intervals to obtain multiple actual busy-to-free ratio matrices and multiple predicted busy-to-free ratio matrices;

Subtracting the actual busy-to-empty ratio matrix and the predicted busy-to-free ratio matrix in the same time interval, and averaging a plurality of matrices obtained after the subtraction operation to obtain a coefficient adjustment matrix; and

The workload obtained by the prediction is modified according to the coefficient adjustment matrix, so that the required manpower is calculated according to the workload obtained by the corrected prediction.
The server according to claim 8, wherein when the scheduler is executed by the processor, the following steps are further implemented:

Record the attendance and work information of each scheduled employee according to the schedule to output a schedule compliance report.
The server according to any one of claims 9-12, wherein the scheduler program further implements the following steps when executed by the processor:

Record the attendance and work information of each scheduled employee according to the schedule to output a schedule compliance report.
A computer-readable storage medium stores a scheduling program, and the scheduling program can be executed by at least one processor, so that the at least one processor executes the following steps:

Set rest days and working days in a time interval according to a preset date policy;

Acquiring historical workload data and predicting the workload in the time interval according to the historical workload data;

Calculate the required manpower based on the predicted workload and a man-day baseline workload; and

Perform scheduling within the set working day according to the required manpower and preset constraints, and generate a scheduling table;

The preset constraint condition includes one of the following conditions or a combination of multiple conditions: the length of the shift, the total number of rest days, the number of consecutive working days, the balance of the total working hours, the time interval between shifts, and the same shift of the same group of personnel.
The computer-readable storage medium of claim 15, wherein the steps of obtaining historical workload data and predicting the workload in the time interval based on the historical workload data comprise:

Acquiring the historical workload data and analyzing the historical workload data to eliminate abnormal data;

Calculate a monthly regular line with monthly dimensions based on historical workload data after excluding abnormal data; and

Predicting the workload of the time interval according to the monthly regular line and a preset monthly workload increase ratio;

Wherein, the workload in the time interval = (Σ monthly regular line) / number of days in a month * month workload increase ratio.
The computer-readable storage medium of claim 15, wherein the step of calculating the required manpower based on the predicted workload and the man-hour benchmark workload comprises:

Calculating the required manpower according to the predicted workload, the manpower day benchmark workload, and a manpower day work efficiency;

Wherein, the required manpower = predicted workload / (manual daily reference workload * manual daily work efficiency).
The computer-readable storage medium according to claim 15, wherein when the schedule program is executed by the processor, the following steps are further implemented:

Obtaining the predicted workload and the actual workload in multiple historical time intervals to obtain the actual workload matrix and the predicted workload matrix;

Subtracting the predicted workload matrix from the actual workload matrix in the same time interval, and averaging multiple matrices obtained after the subtraction operation to obtain a coefficient adjustment matrix; and

The workload obtained by the prediction is modified according to the coefficient adjustment matrix, so that the required manpower is calculated according to the workload obtained by the corrected prediction.
The computer-readable storage medium according to claim 15, wherein when the schedule program is executed by the processor, the following steps are further implemented:

Obtain actual employee busy-to-free ratios and employee predicted busy-to-free ratios in multiple historical time intervals to obtain multiple actual busy-to-free ratio matrices and multiple predicted busy-to-free ratio matrices;

Subtracting the actual busy-to-empty ratio matrix and the predicted busy-to-free ratio matrix in the same time interval, and averaging a plurality of matrices obtained after the subtraction operation to obtain a coefficient adjustment matrix; and

The workload obtained by the prediction is modified according to the coefficient adjustment matrix, so that the required manpower is calculated according to the workload obtained by the corrected prediction.
The computer-readable storage medium according to any one of claims 15 to 19, wherein when the scheduling program is executed by the processor, the following steps are further implemented:

Record the attendance and work information of each scheduled employee according to the schedule to output a schedule compliance report.