Employee scheduling method and device, electronic equipment and storage medium

Abstract

The invention provides an employee scheduling method and device, electronic equipment, a storage medium and a computer program product, and the method comprises the steps: determining the performance parameters of employees of a target entity based on the historical performance data, historical scheduling data and historical load data of the target entity, the target entity has a plurality of workstations for executing production tasks of the target entity, the historical scheduling data indicates the workstations served by the employees in history, the historical load data indicates the historical workloads of the plurality of workstations of the target entity, and the performance parameters indicate the work efficiency of the employees at the plurality of workstations of the target entity; obtaining workload data and scheduling constraint conditions of the target entity; and on the basis of the performance parameters and the workload data of the employees of the target entity and the scheduling constraint condition, determining a work station which needs to be served by each employee of the target entity.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technologies, and more particularly to the field of data processing, and specifically to a method, apparatus, electronic device, computer-readable storage medium, and computer program product for employee scheduling. Background Art

[0002] Currently, employees are mainly scheduled in advance by managers. However, this scheduling method often produces unreasonable scheduling plans, resulting in low production efficiency, inability to fully utilize production resources, and causing resource waste.

[0003] The methods described in this section are not necessarily methods that have been previously conceived or adopted. Unless otherwise specified, any method described in this section should not be considered prior art merely because it is included in this section. Similarly, unless otherwise specified, the problems mentioned in this section should not be considered to have been recognized in any prior art. Summary of the Invention

[0004] The present disclosure provides a method, apparatus, electronic device, computer-readable storage medium, and computer program product for employee scheduling.

[0005] According to one aspect of the present disclosure, there is provided a method for employee scheduling, including: determining a performance parameter of an employee of a target entity based on historical performance data, historical scheduling data, and historical load data of the target entity, where the target entity has a plurality of workstations for performing production tasks of the target entity, the historical scheduling data indicates the workstations that the employee has served historically, the historical load data indicates the historical workloads of the plurality of workstations of the target entity, and the performance parameter indicates the work efficiency of the employee at the plurality of workstations of the target entity; obtaining workload data and scheduling constraint conditions of the target entity, where the workload data includes the workloads at the plurality of workstations in the target entity, and the scheduling constraint conditions include the constraint conditions for scheduling the employees of the target entity; and determining the workstations that each employee of the target entity needs to serve based on the performance parameter and workload data of the employees of the target entity, and the scheduling constraint conditions.

[0006] According to another aspect of the present disclosure, there is provided an apparatus for employee scheduling, including: a performance determination module configured to determine performance parameters of employees of a target entity based on historical performance data, historical scheduling data, and historical load data of the target entity, where the target entity includes a plurality of workstations for performing production tasks of the target entity, the historical scheduling data indicates the workstations served by the employees historically, the historical load data indicates the historical workloads of the plurality of workstations of the target entity, and the performance parameters indicate the work efficiency of the employees at the plurality of workstations of the target entity; a condition acquisition module configured to acquire the workload data and scheduling constraint conditions of the target entity, where the workload data includes the workloads at the plurality of workstations in the target entity, and the scheduling constraint conditions include the constraint conditions for scheduling the employees of the target entity; and a scheduling determination module configured to determine the workstations that each employee of the target entity needs to serve based on the performance parameters and workload data of the employees of the target entity, and the scheduling constraint conditions..

[0007] According to another aspect of the present disclosure, there is provided an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above method for employee scheduling.

[0008] According to another aspect of the present disclosure, there is provided a computer-readable storage medium storing computer instructions, where the computer instructions are used to cause the computer to execute the above method for employee scheduling.

[0009] According to another aspect of the present disclosure, there is provided a computer program product including a computer program, where the computer program implements the above method for employee scheduling when executed by a processor.

[0010] According to the embodiments as described in the present disclosure, the performance parameters of the employees of the target entity are determined through the historical data of the target entity, so as to efficiently and accurately evaluate the work efficiency of the employees at the plurality of workstations of the target entity; through the determined performance parameters of the employees, and the acquired current workload data and scheduling constraint conditions of the target entity, the time required for employee scheduling can be shortened, the accuracy of employee scheduling can be improved, the efficiency and flexibility of employee scheduling can be improved, thereby improving production efficiency, making full use of production resources, and reducing resource waste.

[0011] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. Description of the Drawings

[0012] The accompanying drawings exemplarily illustrate embodiments and form a part of the specification, and are used together with the written description of the specification to explain the exemplary implementation manners of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. In all the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0013] Figure 1 A flowchart of a method for employee scheduling according to an embodiment of the present disclosure is shown;

[0014] Figure 2 A flowchart of a process for obtaining performance parameters and workload data of employees of a target entity according to an embodiment of the present disclosure is shown;

[0015] Figure 3 A flowchart of a process for determining workstations that each employee of a target entity needs to serve based on the performance parameters and workload data of the employees of the target entity and scheduling constraint conditions is shown;

[0016] Figure 4 A block diagram of a device for employee scheduling according to an exemplary embodiment of the present disclosure is shown;

[0017] Figure 5 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Embodiments

[0018] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings. Various details of the embodiments of the present disclosure are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0019] In the present disclosure, unless otherwise specified, the terms "first", "second", etc. are used to describe various elements and are not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, and in certain cases, based on the context description, they may also refer to different instances.

[0020] In the description of the various examples in this disclosure, the terms used are for the purpose of describing specific examples only and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or more. In addition, the term "and / or" used in this disclosure covers any and all possible combinations of the listed items.

[0021] As described above, currently, the management staff makes prior scheduling arrangements for employees. However, this scheduling method often produces unreasonable scheduling plans, resulting in low production efficiency and waste of production resources.

[0022] For example, in restaurant management, the restaurant management team needs to determine the employee scheduling arrangement one week in advance based on the estimated workload of the restaurant for the next week (e.g., the number of orders). However, this manual scheduling of employees is based on the estimated restaurant workload, and since the time interval between scheduling the employees and the actual scheduling time is often long, the error between the estimated restaurant workload and the actual restaurant workload is large, making it difficult to ensure the reasonableness of the scheduling plan (e.g., receiving a large number of hamburger orders from customers at a certain time period, resulting in a large workload at the hamburger assembly workstation, while the expected situation is that the workload is evenly distributed among all workstations, thus resulting in low production efficiency at the hamburger assembly workstation and waste of production resources at other workstations); moreover, since employees may take temporary leave, the available employees for scheduling are different from those scheduled, making it difficult to apply the pre-determined scheduling arrangement.

[0023] To at least solve the above problems, the present disclosure provides a method for employee scheduling, including: determining performance parameters of employees of a target entity based on historical performance data, historical scheduling data, and historical workload data of the target entity, where the target entity has multiple workstations for performing the production tasks of the target entity, the historical scheduling data indicates the workstations where the employees have served historically, the historical workload data indicates the historical workloads of the multiple workstations of the target entity, and the performance parameters indicate the work effectiveness of the employees at the multiple workstations of the target entity; obtaining the workload data and scheduling constraint conditions of the target entity, where the workload data includes the workloads at the multiple workstations in the target entity, and the scheduling constraint conditions include the constraint conditions for scheduling the employees of the target entity; and determining the workstations that each employee of the target entity needs to serve based on the performance parameters, workload data, and scheduling constraint conditions of the employees of the target entity.

[0024] According to an embodiment as described in the present disclosure, by using the historical data of a target entity, performance parameters of the employees of the target entity are determined, so as to efficiently and accurately evaluate the work efficiency of the employees at multiple workstations of the target entity; by using the determined performance parameters of the employees, as well as the obtained workload data and scheduling constraint conditions of the current target entity, the time required for employee scheduling is shortened, the accuracy of employee scheduling is improved, the efficiency and flexibility of employee scheduling are increased, thereby improving the production efficiency, making full use of production resources, and reducing resource waste.

[0025] Figure 1 FIG. 4 shows a flowchart of a method 100 for employee scheduling according to an exemplary embodiment of the present disclosure. As Figure 1 shown, the method 100 for employee scheduling may include the following steps:

[0026] Step S110: Based on the historical performance data, historical scheduling data, and historical workload data of the target entity, determine the performance parameters of the employees of the target entity, where the target entity has multiple workstations for performing the production tasks of the target entity, the historical scheduling data indicates the workstations served by the employees historically, the historical workload data indicates the historical workloads of the multiple workstations of the target entity, and the performance parameters indicate the work efficiency of the employees at the multiple workstations of the target entity;

[0027] Step S120: Obtain the workload data and scheduling constraint conditions of the target entity, where the workload data includes the workloads at multiple workstations in the target entity, and the scheduling constraint conditions include the constraint conditions for scheduling the employees of the target entity;

[0028] Step S130: Based on the performance parameters and workload data of the employees of the target entity, as well as the scheduling constraint conditions, determine the workstations that each employee of the target entity needs to serve.

[0029] In the embodiment as described in the present disclosure, the "target entity" may be an organization that can schedule employees to complete its work tasks, which may be an organization with a physically operating location geographically (for example, a restaurant, a factory, etc.), or an organization that does not have a physically operating location geographically but has an electronic operation relying on computer technology (for example, an online store).

[0030] In an embodiment as described in the present disclosure, a "workstation" may be a part of a target entity that completes a specific production task. For example, for the organization of a physically operating location in terms of geography, a workstation may be the geographical scope that completes a specific production link (for example, for the back kitchen area of a restaurant, it may include a baking workstation, a frying workstation, a thawing workstation, etc.). Or, for an organization that does not have a physically operating location but has an electronic operation relying on computer technology, a workstation may be a virtual scope that completes a specific production link (for example, for an online store, the pre-sales area may include graphical user interfaces such as a product management workstation and a pre-sales consultation workstation).

[0031] In an embodiment as described in the present disclosure, a "work area" may be a part of a target entity that completes a specific production link. For example, for the organization of a physically operating location in terms of geography, the work area may be the geographical scope that completes a specific production link (for example, for a restaurant, the work area may include a cashier area, a back kitchen area, and a dining area). Or, for an organization that does not have a physically operating location but has an electronic operation relying on computer technology, it may be a virtual scope that completes a specific production link (for example, for an online store, the work area may include the pre-sales area and the after-sales area in the graphical user interface).

[0032] According to some embodiments, a target entity has a plurality of work areas corresponding to the production links of the target entity, wherein each work area includes one or more workstations (for example, for the back kitchen area of a restaurant, it may include a baking workstation, a frying workstation, a thawing workstation, etc.).

[0033] In an embodiment as described in the present disclosure, "workload data" is data indicating the workload to be completed by a target entity. According to some embodiments, the workload data of a target entity includes the workload data corresponding to each workstation therein.

[0034] According to some embodiments, the load of each workstation can be determined based on a single target entity-related factor. For example, based on the order volume, the load of each workstation is determined. For example, through historical data, the proportion of the order volume related to each workstation in the total order volume is calculated, and then the current order volume is multiplied by this proportion to obtain the load of each workstation. According to other embodiments, the load of each workstation can be determined based on multiple target entity-related factors (such as order volume, order amount, and the task amount of each workstation).

[0035] According to some embodiments, an employee's performance parameter indicates the contribution of the employee to the performance indicators of a target entity, and the employee's performance parameter includes sub-performance parameters of the employee at each workstation of the target entity, wherein the sub-performance parameter of the employee at each workstation of the target entity can indicate the contribution of the employee to the performance indicators of the target entity at that workstation.

[0036] According to some embodiments, at step S110, linear fitting is performed on historical data to determine the performance parameters of the employees of the target entity.

[0037] According to some embodiments, linear fitting is performed based on one or more of the following assumptions:

[0038] (1) The performance parameters of employees do not change over time;

[0039] (2) Employees do not affect each other's performance parameters;

[0040] (3) The values of the performance indicators of the target entity are related to the production capacity (e.g., order volume) and / or task volume (e.g., order load per work area) of the target entity;

[0041] (4) A linear relationship is satisfied between the values of the performance indicators of the target entity and the performance parameters of employees (e.g., sub-performance parameters of employees corresponding to each performance indicator); and

[0042] (5) The error between the fitted performance value obtained by linear fitting and the historical performance value follows a normal distribution.

[0043] According to some embodiments, the historical performance data includes historical performance values corresponding to one or more performance indicators of the target entity, and determining the performance parameters of the employees of the target entity based on the historical performance data, historical scheduling data, and historical load data of the target entity includes: obtaining the historical performance data, historical scheduling data, and historical load data of the target entity; constructing a linear regression model of the target entity based on the historical scheduling data and historical load data, where the linear regression model includes a linear regression function and a linear objective function, the linear regression function includes a linear function of the fitted performance value of one or more performance indicators with respect to the performance parameters of employees, and the linear objective function is to minimize the error between the historical performance values of one or more performance indicators and the fitted performance values; and solving the linear regression model of the target entity to determine the performance parameters of employees.

[0044] The following refers to Figure 2 for further description Figure 1 of the process of obtaining the performance parameters and workload data (e.g., Figure 1 step S110 in Figure 2 ). As shown in

[0045] Step S110 includes:

[0046] Step S210, obtaining the historical performance data, historical scheduling data, and historical load data of the target entity;

[0046] Step S220: Based on historical scheduling data and historical load data, construct a linear regression model for the target entity. The linear regression model includes a linear regression function and a linear objective function. The linear regression function includes a linear function of the fitted performance values of one or more performance indicators with respect to the performance parameters of the employees. The linear objective function is to minimize the error between the historical performance values and the fitted performance values of one or more performance indicators; and Step S230: Solve the linear regression model of the target entity to determine the performance parameters of the employees.

[0047] In the embodiments described in the present disclosure, by obtaining historical data, performing linear fitting, and determining the performance parameters of employees, the contribution of employees to the production capacity of the target entity can be accurately evaluated, and the optimal utilization of human resources can be achieved for employee scheduling based on these performance parameters.

[0048] According to some embodiments, obtaining the historical performance data, historical scheduling data, and historical load data of the target entity includes: obtaining the original performance data of the target entity; obtaining the historical scheduling data and the original load data corresponding to the original performance data; and performing normalization processing on the original performance data to obtain the historical performance data, and performing normalization processing on the original load data to obtain the historical load data.

[0049] According to some embodiments, the original performance data is the values of different performance indicators of the target entity collected historically, and the historical performance data is the normalized values of different performance indicators of the target entity obtained after performing normalization processing on the original performance data.

[0050] According to some embodiments, the original load data is the values of different load indicators of the target entity collected historically, and the historical load data is the normalized values of different load indicators of the target entity obtained after performing normalization processing on the original load data. In the embodiments described in the present disclosure, through normalization processing, the dimensional differences between the collected original data of different dimensions (for example, the original performance values corresponding to different performance indicators, the original load values corresponding to different load indicators) are eliminated.

[0051] According to some embodiments, in Step S210, the historical performance data, historical scheduling data, and historical load data of the target entity obtained are data that correspond to each other in time. For example, the historical failure data, historical scheduling data, and historical load data are data corresponding to the same time period.

[0052] According to some embodiments, historical performance data for a certain period can be obtained, and historical scheduling data and historical load data for this period are obtained. Among them, the historical performance data has multiple sub-historical performance data corresponding to multiple sub-periods within this period, and for each sub-historical performance data, there is sub-historical scheduling data corresponding to this sub-historical performance data in the historical scheduling data, and there is sub-historical load data corresponding to this sub-historical performance data in the historical load data.

[0053] According to some embodiments, the historical performance data of the target entity is matrix R, where matrix R can be shown as the following formula (1):

[0054]

[0055] Among them, the historical performance data includes N sub-historical performance data corresponding to M performance indicators. Among them, the i-th row of matrix R is the i-th data (for example, the data of the i-th day), the j-th column of matrix R is the data corresponding to the j-th indicator, and the element in the i-th row and j-th column of matrix R is the data corresponding to the j-th indicator in the i-th data of the target entity.

[0056] According to some embodiments, the elements in the above matrix R are all greater than or equal to 0. According to some other embodiments, the above matrix R also includes elements less than 0 (for example, the profit value of the target entity).

[0057] According to some embodiments, the historical scheduling data of the target entity includes one or more workstation scheduling matrices X s , where each matrix X s corresponds to the scheduling situation of a workstation. Among them, matrix X s can be shown as the following formula (2):

[0058]

[0059] Among them, each workstation scheduling matrix X s includes N sub-historical scheduling data corresponding to all schedulable employees of the target entity (for example, employees worker1 to worker W , W is the number of employees). Among them, the i-th row of matrix X s is the i-th data (for example, the data of the i-th day), the j-th column of matrix X s is the data corresponding to employee worker j , and the element in the i-th row and j-th column of matrix X s is the data corresponding to employee worker j in the i-th data of the target entity.

[0060] According to some embodiments, matrix R and matrix Xs The row data with the same row numbers in [[]] correspond to the data for the same time period. For example, the i-th row data in matrix R and matrix X s The i-th row data in [[]] correspond to the data for the same time (e.g., the data for the i-th day).

[0061] According to some embodiments, matrix X s The elements of [[]] are 0-1 variables. For example, for an element When the element is 1, it indicates that the employee worker j serves at this workstation. And when the element is 0, it indicates that the employee worker j does not serve at this workstation.

[0062] According to some embodiments, the historical load data of the target entity is matrix L, where matrix L can be shown as the following formula (3):

[0063]

[0064] Among them, the historical load data includes N sub-historical load data corresponding to T load indicators. Among them, the i-th row of matrix L is the i-th data (e.g., the data for the i-th day), the j-th column of matrix T is the data corresponding to the j-th indicator, and the element in the i-th row and j-th column of matrix T is the data corresponding to the j-th indicator in the i-th data of the target entity.

[0065] In an actual scenario, due to the different assessment dimensions of different performance indicators of the target entity, it is necessary to normalize the obtained original performance data to eliminate this difference and obtain the historical performance data for further processing.

[0066] According to some embodiments, the original performance data includes one or more sub-original performance data, each sub-original performance data includes the original performance values of one or more performance indicators, the historical performance data includes one or more sub-historical performance data, and each historical performance data includes the historical performance values of one or more performance indicators. And normalizing the original performance data includes: for any target performance indicator in the performance indicators, determining the performance boundary value of the target performance indicator, where the performance boundary value is the maximum or minimum value of the target performance indicator in the original performance data; and based on the performance boundary value of the target performance indicator and the original performance value of the target performance indicator in the sub-original performance data in the original performance data, determining the historical performance value of the target performance indicator in the sub-historical performance data in the historical performance data.

[0067] In the embodiments as described in the present disclosure, by normalizing the original performance data, the differences caused by different assessment dimensions of different performance indicators of the target entity are eliminated.

[0068] According to some embodiments, the original performance data of the target entity is a matrix K, where the matrix K can be as shown in the following formula (4):

[0069]

[0070] Wherein, the original performance data includes N sub-original performance data corresponding to M performance indicators. Among them, the i-th row of the matrix M is the i-th data (for example, the data of the i-th day), the j-th column of the matrix M is the data corresponding to the j-th indicator, and the element in the i-th row and j-th column of the matrix M is the data corresponding to the j-th indicator in the i-th data of the target entity.

[0071] According to some embodiments, for the case where the performance boundary value is the maximum value of the original performance value corresponding to the sub-original performance data in the original performance data for this performance indicator, the historical performance value corresponding to the sub-historical performance data in the historical performance data is: the value obtained by dividing the corresponding original performance value by the performance boundary value.

[0072] For example, the historical performance value in the matrix R corresponding to the historical performance data Wherein, k ij is the corresponding element in the matrix K corresponding to the original performance data, and max j is the performance boundary value of the j-th performance indicator, that is, the maximum value of the elements in the j-th column of the matrix K.

[0073] According to some embodiments, for the performance indicators with expected maximization, the performance boundary value is set to the maximum value of the original performance value corresponding to the sub-original performance data in the original performance data for this performance indicator. According to some embodiments, the performance indicators include one or more first performance indicators, and the one or more first performance indicators include at least one of customer good review data, order quantity, and meal delivery rate within a predetermined time. And, corresponding to the first performance indicator, the performance boundary value is the maximum value of the first performance indicator of the sub-original performance data in the original performance data.

[0074] According to some embodiments, for the case where the performance boundary value is the minimum value of the original performance value corresponding to the sub-original performance data in the original performance data for this performance indicator, the historical performance value corresponding to the sub-historical performance data in the historical performance data is: the value obtained by dividing the performance boundary value by the corresponding original performance value.

[0075] For example, the historical performance value in the matrix R corresponding to the historical performance data Wherein, k ijis the corresponding element in matrix K corresponding to the original performance data, min j is the performance boundary value of the j-th performance indicator, that is, the minimum value of the elements in the j-th column of matrix K.

[0076] According to some embodiments, for the performance indicator to be minimized, the performance boundary value is set to the minimum value of the original performance value corresponding to the performance indicator of the sub-original performance data in the original performance data. According to some embodiments, the performance indicators include one or more second performance indicators, and the one or more second performance indicators include at least one of customer complaint data and abnormal warehousing data. And, corresponding to the second performance indicator, the performance boundary value is the minimum value of the second performance indicator of the sub-original performance data in the original performance data.

[0077] According to some embodiments, similar to the original performance data, due to the different evaluation dimensions of the different load indicators of the target entity, it is necessary to normalize the obtained original load data to eliminate this difference and obtain historical load data for further processing.

[0078] According to some embodiments, the original load data includes one or more sub-original load data, and each sub-original load data includes the original load value corresponding to one or more load indicators. The historical load data includes one or more sub-historical load data, and each historical load data includes the historical load value corresponding to one or more load indicators. And normalizing the original load data includes: for any target load indicator in the load indicators, determining the load boundary value of the target load indicator, where the load boundary value is the maximum or minimum value of the target load indicator in the original load data; and based on the load boundary value of the target load indicator and the original load value of the target load indicator of the sub-original load data in the original load data, determining the historical load value of the target load indicator of the sub-historical load data in the historical load data.

[0079] In the embodiments described in the present disclosure, by normalizing the original load data, the differences caused by the different assessment dimensions of the different load indicators of the target entity are eliminated.

[0080] According to some embodiments, one or more load indicators include at least one of the following: order volume, order amount, and order volume load.

[0081] According to some embodiments, as described above, the linear regression function includes a linear function of the fitted performance value of one or more performance indicators with respect to the performance parameters of the employee. For example, the linear regression function may include a linear function corresponding to each performance indicator of the target entity, where each linear function of the performance indicator is a linear function of the fitted performance value of the performance indicator with respect to the performance parameter of the employee corresponding to the performance indicator.

[0082] According to some embodiments, the performance parameter of each employee includes sub-performance parameters of the employee at multiple workstations of the target entity. Among them, the sub-performance parameter of the employee at each workstation includes the performance contribution value of the employee at the workstation relative to one or more performance indicators. The performance contribution value can reflect the competence of the employee in the production tasks of the workstation. For example, if employee Zhang San is very familiar with meal preparation, then his performance contribution value relative to one or more performance indicators at the workstation corresponding to meal preparation will be higher than that of other employees at the same workstation relative to these indicators.

[0083] According to some embodiments, for the j-th performance indicator of the target entity, its corresponding linear function can be constructed as the following formula (5):

[0084]

[0085] where score j is the fitted performance value corresponding to the j-th performance indicator, x iw is the historical scheduling volume of the w-th employee at the i-th workstation, I is the total number of workstations of the target entity, p iwj is the performance parameter of the w-th employee at the i-th workstation corresponding to the j-th performance indicator, worker1 is the number of the first schedulable employee, worker W is the number of the last schedulable employee, l t is the historical load value corresponding to the t-th load indicator, T is the total number of load indicators, and b is the linear fitting coefficient.

[0086] According to some embodiments, the linear objective function can be constructed to minimize the error between the historical performance value and the fitted performance value of one or more performance indicators. According to some embodiments, the linear objective function includes an objective function corresponding to each performance indicator. Among them, each objective function is to minimize the sum of the squares of the errors between the historical performance value and the fitted performance value of each indicator.

[0087] According to some embodiments, for each historical performance value, substitute the sub-historical scheduling data and sub-historical load data corresponding to the historical performance value (for example, the sub-historical scheduling data and sub-historical load data belonging to the same period as the sub-historical performance data to which the historical performance value belongs) into the objective function to obtain the fitted performance value, and calculate the difference between the fitted performance value and the historical performance value as the error corresponding to the historical performance value; and for each performance indicator, its corresponding objective function is: add up the sum of the squares of the corresponding errors of all historical performance values corresponding to the performance indicator in the historical performance data.

[0088] For example, for the j-th performance indicator, its corresponding objective function can be constructed as shown in Equation (6) below:

[0089]

[0090] where score ij is the fitting score of the j-th indicator obtained by linearly fitting the i-th sub-data based on historical scheduling data and historical load data, and r ij is the j-th historical performance value in the i-th sub-data of historical performance data, and N is the number of sub-data in historical scheduling data or historical load data.

[0091] According to some embodiments, for the linear regression model of the target entity, constraint conditions for solution are set. For example, the value of the performance parameter of an employee (e.g., parameter p iwj ) in Equation (5) is greater than or equal to 0.

[0092] According to some embodiments, the solver Gurobi can be called to obtain the performance parameters of an employee (e.g., parameter p iwj ) in Equation (5).

[0093] According to some embodiments, at step S120, based on the operation requirements of the target entity, one or more scheduling constraint conditions of the target entity are obtained (e.g., each employee can only go to the workstations where the employee has the corresponding skills). According to some embodiments, at step S120, based on the goal of optimizing employee scheduling, one or more scheduling constraint conditions of the target entity are obtained (e.g., the number of employees assigned to each work area is greater than or equal to the predetermined number threshold of that work area).

[0094] According to some embodiments, the target entity has multiple work areas corresponding to the production links of the target entity, where each work area includes one or more workstations.

[0095] According to some embodiments, the scheduling constraint conditions include at least one of the following: each employee can only go to the workstations where the employee has the corresponding skills; each employee is assigned to the workstations in the work area served by the employee; each employee cannot be assigned to the workstations in the work area not served by the employee; each employee can only serve one work area; the number of workstations assigned to each employee should be less than or equal to the predetermined station threshold; the number of employees assigned to each work area is greater than or equal to the predetermined number threshold of that work area; and the number of regional change times of the current employee scheduling relative to the previous employee scheduling should be less than or equal to the predetermined change threshold.

[0096] In the embodiments described in the present disclosure, by obtaining the scheduling constraint conditions, the obtained employee scheduling plan can be made to meet the actual scheduling requirements as much as possible.

[0097] Figure 3 illustrates a process of determining workstations required for each employee of a target entity based on performance parameters and workload data of the employees of the target entity and scheduling constraints according to an embodiment of the present disclosure (e.g., Figure 1 step S130 in Figure 3 ). As

[0098] shown, step S130 includes:

[0099] Step S310, determining a performance function of the entity performance of the target entity with respect to the scheduling variables of the employees, where the scheduling variables include a first scheduling component indicating the workstation required by the employee; and

[0100] Step S320, based on the performance function, determining a planning objective function of the integer programming model of the target entity, where the planning objective function is to maximize the entity performance of the target entity;

[0101] Step S330, based on the scheduling constraints of the target entity, determining the model constraints of the integer programming model of the target entity, where the model constraints are associated with the scheduling variables of the employees; and

[0102] In the embodiment as described in the present disclosure, by converting the requirements of maximizing the production capacity of the target entity and various scheduling constraints considered in the actual scheduling into an overall planning model, automated calculation of employee scheduling can be achieved.

[0103] According to some embodiments, the entity performance of the target entity may be a value used to measure the overall operating state of the target entity. For example, it is the weighted sum of various performance indicators of the target entity. According to some embodiments, the performance value of the target entity corresponding to each performance indicator may be calculated based on the sub-performance parameters of each employee, and the performance values of the target entity corresponding to each performance indicator are weighted and summed to obtain the entity performance of the target entity. For example, the performance function may be as shown in the following formula (7):

[0104]

[0105] The definitions of the variables in formula (7) are as follows:

[0106] 1) Set K is the set of performance indicators of the target entity, where k ∈ K;

[0107] 2) Set N is the set of employees of the target entity that can be scheduled currently, where i ∈ N;

[0108] 3) The set M is the set of workstations of the target entity, where j ∈ M;

[0109] 4) ω k is the weight coefficient of the performance indicator k;

[0110] 5) x ij is a 0-1 variable indicating whether employee i serves at workstation j. Among them, x ij = 1 means that employee i is assigned to workstation j, otherwise x ij = 0

[0111] 6) p ijk is the sub-performance parameter of person i at workstation j with respect to the performance indicator k; and

[0112] 7) wordload j is the workload at workstation j, where wordkload j can be the workload at workstation j calculated based on one or more of the above-mentioned load indicators.

[0113] According to some embodiments, at step S320, the planning objective function of the integer programming model of the target entity can be constructed based on the performance function. For example, based on the performance function shown in Equation (7), the planning objective function can be constructed as Equation (8):

[0114]

[0115] The definitions of the variables in Equation (8) are the same as those in Equation (7), so they are omitted here.

[0116] According to some embodiments, at step S330, the scheduling constraint conditions of the target entity are converted into the model constraint conditions of the target entity.

[0117] According to some embodiments, the constraint conditions can also include constraints on the values of the scheduling variables of employees. For example, the scheduling variables of employees are constrained to be 0-1 variables.

[0118] According to some embodiments, the first scheduling component includes the workstation scheduling quantity corresponding to each workstation, and the component of each workstation scheduling quantity includes the employee scheduling quantity corresponding to each employee. Among them, corresponding to each workstation, for the employees serving at this workstation, the employee scheduling quantity corresponding to this employee in the workstation scheduling quantity corresponding to this workstation is 1, and for the employees not serving at this workstation, the employee scheduling quantity corresponding to this employee in the workstation scheduling quantity corresponding to this workstation is 0.

[0119] According to some embodiments, the target entity has a plurality of work areas for the production process corresponding to the target entity, wherein each work area includes one or more workstations, and the scheduling variable further includes a second scheduling component indicating the work area served by the employee, wherein the second scheduling component includes a regional scheduling amount corresponding to each work area, and each regional scheduling component includes an employee scheduling amount corresponding to each employee, and wherein, corresponding to each work area, for the employees serving the work area, the employee scheduling amount corresponding to the employee in the regional scheduling amount corresponding to the work area is 1, and for the employees not serving the work area, the employee scheduling amount corresponding to the employee in the regional scheduling amount corresponding to the work area is 0.

[0120] According to some embodiments, the constraint conditions of the integer programming model can be constructed as shown in the following formula (9):

[0121]

[0122] Wherein, the definitions of the common variables in formula (9) and formulas (7) and (8) are the same as those described above with reference to formula (7), so they are omitted here, and the definitions of other variables in formula (9) are as follows:

[0123] 1) Set M′ i Is the set of workstations that person i can serve. For example, employee Zhang San can only work at the meal preparation workstation, baking workstation, and thawing workstation;

[0124] 2) y is Is a 0-1 variable indicating whether employee i is currently serving in the work area. Among them, y is = 1 indicates that employee i is assigned to workstation s, otherwise y is = 0;

[0125] 3) Is a 0-1 variable indicating whether employee i served in the work area in the previous scheduling. Among them, Indicates that employee i is assigned to workstation s, otherwise

[0126] 4) Set S is the set of work areas of the target entity, where s ∈ M;

[0127] 5) Set M s Is the set of workstations corresponding to work area s;

[0128] 6) Is the maximum value of the workstations served by each person;

[0129] 7) b s Is the minimum value of the people served in work area s;

[0130] 8) amount max Is the maximum threshold for the number of person-times for adjustment.

[0131] Moreover, constraint (a) in formula (9) indicates that each employee can only go to the workstations where the employee has the corresponding skills; constraint (b) indicates that if the workstation served by the employee belongs to a certain work area, then the employee serves in that work area, that is, each employee is assigned to the workstations in the work area served by the employee; constraint (c) indicates that if the workstation served by the employee is not assigned to any workstation in the work area, then the employee does not serve in that work area, that is, each employee cannot be assigned to the workstations in the work area that the employee does not serve; constraint (d) indicates that the number of workstations assigned to each employee should be less than or equal to the predetermined site threshold; constraint (e) indicates that each employee can only serve in one work area; constraint (f) indicates that the number of employees assigned to each workstation is greater than or equal to the predetermined number threshold of that work area; constraint (g) indicates that the number of area change person-times of the current employee scheduling relative to the previous employee scheduling should be less than or equal to the predetermined change threshold; constraint (h) indicates the value range of each variable of the integer programming model.

[0132] According to some embodiments, for the work area of the target entity, the predetermined number threshold of the work area is determined based on the number of employees assigned to the work area last time (for example, subtracting one from the number of employees assigned to the workstation last time to obtain the predetermined number threshold of the work area). According to other embodiments, for the work area of the target entity, the predetermined number threshold of the work area is determined based on the number of employees pre-scheduled in the work area.

[0133] In the embodiments as described in the present disclosure, determining the predetermined number threshold of the work area based on the number of employees assigned to the work area last time or the number of employees pre-assigned to the work area can reasonably set the predetermined number threshold of the work area to avoid the situation of no solution or too few employees assigned to a certain work area as much as possible.

[0134] According to some embodiments, based on the performance parameters of the employees of the target entity, the workload data of the target entity, and the scheduling constraint conditions, determining the workstations served by each employee of the target entity further includes: in response to the current overall planning model having no solution, performing at least one of the following to change the constraint conditions of the overall planning model: reducing the predetermined number threshold; increasing the predetermined change threshold; and increasing the predetermined site threshold; and re-solving the changed overall planning model. In the embodiments as described in the present disclosure, by relaxing the soft constraint conditions with adjustable thresholds, the situation of no solution of the model can be avoided, and the applicability of the personnel scheduling method can be improved.

[0135] Figure 4 FIG. 2 shows a structural block diagram of an apparatus 400 for employee scheduling according to an exemplary embodiment of the present disclosure.

[0136] As Figure 4 shown, the apparatus 400 includes:

[0137] A data acquisition module 410, configured to: acquire performance parameters and workload data of employees of a target entity, where the target entity includes a plurality of workstations for performing production tasks of the target entity, and the performance parameters include sub-performance parameters of the employees at the plurality of workstations of the target entity;

[0138] A condition acquisition module 420, configured to: acquire scheduling constraint conditions of the target entity, where the scheduling constraint conditions include constraint conditions for scheduling employees of the target entity; and

[0139] A scheduling determination module 430, configured to: determine workstations that each employee of the target entity needs to serve based on the performance parameters and workload data of the employees of the target entity, and the scheduling constraint conditions.

[0140] According to some embodiments, the data acquisition module 410 includes:

[0141] A historical data acquisition sub-module, configured to: acquire historical performance data, historical scheduling data, and historical workload data of the target entity, where the historical performance data includes historical performance values corresponding to one or more performance indicators of the target entity, and the historical scheduling data indicates workstations that employees have served historically;

[0142] A regression model construction sub-module, configured to: construct a linear regression model of the target entity based on the historical scheduling data and the historical workload data, where the linear regression model includes a linear regression function and a linear objective function, the linear regression function includes a linear function of the fitted performance values of one or more performance indicators with respect to the performance parameters of the employees, and the linear objective function is to minimize the error between the historical performance values and the fitted performance values of one or more performance indicators; and

[0143] A regression model solving sub-module, configured to: solve the linear regression model of the target entity to determine the performance parameters of the employees.

[0144] According to some embodiments, the historical data acquisition sub-module includes:

[0145] A raw data acquisition sub-module, configured to: acquire raw performance data of the target entity; and acquire historical scheduling data and raw workload data corresponding to the raw performance data; and

[0146] The normalization sub-module is configured to: normalize the original performance data to obtain historical performance data, and normalize the original load data to obtain historical load data.

[0147] According to some embodiments, the original performance data includes one or more sub-original performance data, each sub-original performance data includes an original performance value corresponding to one or more performance indicators, the historical performance data includes one or more sub-historical performance data, and each historical performance data includes a historical performance value corresponding to one or more performance indicators, and normalizing the original performance data includes: for each performance indicator, determining a performance boundary value of the original performance value corresponding to the performance indicator in the original performance data, where the performance boundary value is the maximum or minimum value of the original performance value of the sub-original performance data corresponding to the performance indicator in the original performance data; and based on the performance boundary value and the original performance value of the sub-original performance data corresponding to the performance indicator in the original performance data, determining the historical performance value of the sub-historical performance data corresponding to the performance indicator in the historical performance data.

[0148] According to some embodiments, the performance indicators include one or more first performance indicators, the one or more first performance indicators include at least one of customer positive review data, order quantity, and meal delivery rate within a predetermined time, and corresponding to the first performance indicator, the performance boundary value is the maximum value of the original performance value of the sub-original performance data corresponding to the performance indicator in the original performance data.

[0149] According to some embodiments, the performance indicators include one or more second performance indicators, the one or more second performance indicators include at least one of customer complaint data and abnormal warehousing data, and corresponding to the second performance indicator, the performance boundary value is the minimum value of the original performance value of the sub-original performance data corresponding to the performance indicator in the original performance data.

[0150] According to some embodiments, the original load data includes one or more sub-original load data, each sub-original load data includes an original load value corresponding to one or more load indicators, the historical load data includes one or more sub-historical load data, and each historical load data includes a historical load value corresponding to one or more load indicators, and normalizing the original load data includes: for each load indicator, determining a load boundary value of the original load value corresponding to the load indicator in the original load data, where the load boundary value is the maximum or minimum value of the original load value of the sub-original load data corresponding to the load indicator in the original load data; and based on the load boundary value and the original load value of the sub-original load data corresponding to the load indicator in the original load data, determining the historical load value of the sub-historical load data corresponding to the load indicator in the historical load data.

[0151] According to some embodiments, one or more load indicators include at least one of the following: order volume, order amount, and order volume load.

[0152] According to some embodiments, the scheduling determination module 430 includes:

[0153] A performance function determination sub-module, configured to determine a performance function of the entity performance of the target entity relative to the scheduling variables of the employee, where the scheduling variables include a first scheduling component indicating the workstations required to be served by the employee; and

[0154] A planning objective function determination sub-module, configured to determine a planning objective function of the integer programming model of the target entity based on the performance function, where the planning objective function is to maximize the entity performance of the target entity;

[0155] A model constraint condition determination sub-module, configured to determine the model constraint conditions of the integer programming model of the target entity based on the scheduling constraint conditions of the target entity, where the model constraint conditions are associated with the scheduling variables of the employee; and

[0156] A scheduling variable value determination sub-module, configured to solve the overall planning model of the target entity to determine the values of the scheduling variables of the employee.

[0157] According to some embodiments, the first scheduling component includes a workstation scheduling volume corresponding to each workstation, and each component of the workstation scheduling volume includes an employee scheduling volume corresponding to each employee. Wherein, corresponding to each workstation, for the employees serving the workstation, the employee scheduling volume corresponding to the employee in the workstation scheduling volume corresponding to the workstation is 1, and for the employees not serving the workstation, the employee scheduling volume corresponding to the employee in the workstation scheduling volume corresponding to the workstation is 0.

[0158] According to some embodiments, the target entity has a plurality of work areas for the production links corresponding to the target entity, where each work area includes one or more workstations, and the scheduling variables further include a second scheduling component indicating the work area served by the employee. Wherein, the second scheduling component includes a regional scheduling volume corresponding to each work area, and each regional scheduling component includes an employee scheduling volume corresponding to each employee, and wherein, corresponding to each work area, for the employees serving the work area, the employee scheduling volume corresponding to the employee in the regional scheduling volume corresponding to the work area is 1, and for the employees not serving the work area, the employee scheduling volume corresponding to the employee in the regional scheduling volume corresponding to the work area is 0.

[0159] According to some embodiments, the target entity has multiple working areas for the production process of the corresponding target entity. Each working area includes one or more workstations, and the scheduling constraints include at least one of the following: each employee can only go to the workstations where the employee has the corresponding skills; each employee is assigned to the workstations in the working area served by the employee; each employee cannot be assigned to the workstations in the working area not served by the employee; each employee can only serve one working area; the number of workstations assigned to each employee should be less than or equal to a predetermined station threshold; the number of employees assigned to each working area is greater than or equal to the predetermined number threshold of that working area; and the number of area change person-times of the current employee scheduling relative to the previous employee scheduling should be less than or equal to a predetermined change threshold.

[0160] According to some embodiments, for the working area of the target entity, the predetermined number threshold of the working area is determined based on the number of employees previously assigned to the working area.

[0161] According to some embodiments, based on the performance parameters of the employees of the target entity, the workload data of the target entity, and the scheduling constraints, determining the workstations served by each employee of the target entity further includes: in response to the current overall planning model having no solution, performing at least one of the following to change the constraints of the overall planning model: reducing the predetermined number threshold; increasing the predetermined change threshold; and increasing the predetermined station threshold; and solving the changed overall planning model again.

[0162] It should be understood that Figure 4 each of the units 410 to 430 of the device 400 shown in Figure 1 can correspond to each of the steps S110 to S130 in the method 100 described with reference to

[0163] According to another aspect of the present disclosure, there is also provided an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above-mentioned method for employee scheduling.

[0164] According to another aspect of the present disclosure, there is also provided a computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the above-mentioned method for employee scheduling.

[0165] According to another aspect of the present disclosure, there is also provided a computer program product including a computer program, wherein the computer program, when executed by a processor, implements the above-described method for employee scheduling.

[0166] Referring to Figure 5 , a block diagram of an electronic device 500 according to an embodiment of the present disclosure will now be described, which is an example of a hardware device applicable to various aspects of the present disclosure. The electronic device may be different types of computer devices, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0167] Figure 5 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. As Figure 5 shown, the electronic device 500 may include at least one processor 501, a working memory 502, an I / O device 504, a display device 505, a storage device 506, and a communication interface 507 that can communicate with each other via a system bus 503.

[0168] The processor 501 may be a single processing unit or multiple processing units, and all processing units may include a single or multiple computing units or multiple cores. The processor 501 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and / or any device that manipulates signals based on operation instructions. The processor 501 may be configured to obtain and execute computer-readable instructions stored in the working memory 502, the storage device 506, or other computer-readable media, such as program codes of an operating system 502a, program codes of an application 502b, etc.

[0169] The working memory 502 and the storage device 506 are examples of computer-readable storage media for storing instructions that are executed by the processor 501 to implement the various functions described above. The working memory 502 may include both volatile and non-volatile memories (such as RAM, ROM, etc.). In addition, the storage device 506 may include a hard disk drive, a solid-state drive, removable media, including external and removable drives, memory cards, flash memory, floppy disks, optical disks (such as CDs, DVDs), storage arrays, network-attached storage, storage area networks, and so on. The working memory 502 and the storage device 506 may both be collectively referred to as memory or computer-readable storage media herein, and may be non-transitory media capable of storing computer-readable, processor-executable program instructions as computer program code that can be executed by the processor 501 as a specific machine configured to implement the operations and functions described in the examples herein.

[0170] The I / O device 504 may include input devices and / or output devices. The input devices may be any type of device capable of inputting information into the electronic device 500, and may include, but are not limited to, a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone, and / or a remote control. The output devices may be any type of device capable of presenting information, and may include, but are not limited to, a video / audio output terminal, a vibrator, and / or a printer.

[0171] The communication interface 507 allows the electronic device 500 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks, and may include, but are not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a BluetoothTM device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0172] The application 502b in the working register 502 may be loaded and executed to perform the various methods and processes described above, such as Figure 1 steps S110 - S140. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 500 via the storage device 506 and / or the communication interface 507. When the computer program is loaded and executed by the processor 501, one or more steps of the method for employee scheduling described above may be performed.

[0173] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0174] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on the remote machine or server.

[0175] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0176] To provide for interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0177] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), and the Internet.

[0178] A computing system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client - server relationship is created by computer programs that run on the respective computers and have a client - server relationship with each other.

[0179] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0180] Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above methods, systems, and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is only defined by the authorized claims and their equivalent scope. Various elements in the embodiments or examples may be omitted or replaced by their equivalent elements. In addition, the steps may be executed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples may be combined in various ways. Importantly, with the evolution of technology, many of the elements described herein may be replaced by equivalent elements that emerge after the present disclosure.

Claims

1. A method for employee scheduling, comprising: Based on the historical performance data, historical scheduling data, and historical load data of the target entity, determine the performance parameters of the employees of the target entity, where the target entity has multiple workstations for performing the production tasks of the target entity, the historical scheduling data indicates the workstations served by the employees historically, the historical load data indicates the historical workloads of the multiple workstations of the target entity, and the performance parameters indicate the work efficiency of the employees at the multiple workstations of the target entity; Obtain the workload data and scheduling constraints of the target entity, where the workload data includes the workloads at multiple workstations in the target entity, and the scheduling constraints include the constraints for scheduling the employees of the target entity; and Based on the performance parameters of the employees of the target entity, the workload data, and the scheduling constraints, determine the workstations that each employee of the target entity needs to serve.

2. The method according to claim 1, wherein The historical performance data includes historical performance values corresponding to one or more performance indicators of the target entity, and the determining the performance parameters of the employees of the target entity based on the historical performance data, historical scheduling data, and historical load data of the target entity includes: Obtain the historical performance data, historical scheduling data, and historical load data of the target entity; Based on the historical scheduling data and the historical load data, construct a linear regression model of the target entity, where the linear regression model includes a linear regression function and a linear objective function, the linear regression function includes a linear function of the fitted performance values of the one or more performance indicators with respect to the performance parameters of the employees, and the linear objective function is to minimize the error between the historical performance values and the fitted performance values of the one or more performance indicators; and Solve the linear regression model of the target entity to determine the performance parameters of the employees.

3. The method according to claim 2, wherein The obtaining the historical performance data, historical scheduling data, and historical load data of the target entity includes: Obtain the original performance data of the target entity; Obtain the historical scheduling data and original load data corresponding to the original performance data; and Normalize the original performance data to obtain the historical performance data, and normalize the original load data to obtain the historical load data.

4. The method according to any one of claims 1-3, wherein The original performance data includes one or more sub-original performance data, each sub-original performance data includes the original performance values of the one or more performance indicators, the historical performance data includes one or more sub-historical performance data, and each historical performance data includes the historical performance values of the one or more performance indicators, and The normalizing the original performance data includes: For any target performance indicator among the performance indicators, determine the performance boundary value of the target performance indicator, where the performance boundary value is the maximum or minimum value of the target performance indicator in the original performance data; and Determine the historical performance value of the target performance indicator for the sub-historical performance data in the historical performance data based on the performance boundary value of the target performance indicator and the original performance value of the target performance indicator for the sub-original performance data in the original performance data.

5. The method according to claim 4, wherein The performance indicators include one or more first performance indicators and / or one or more second performance indicators. Among them, the one or more first performance indicators include at least one of customer praise data, order quantity, and meal delivery rate within a predetermined time. And, corresponding to the first performance indicator, the performance boundary value is the maximum value of the first performance indicator for the sub-original performance data in the original performance data. And Among them, the one or more second performance indicators include at least one of customer complaint data and inbound exception data. And, corresponding to the second performance indicator, the performance boundary value is the minimum value of the second performance indicator for the sub-original performance data in the original performance data.

6. The method according to any one of claims 1-3, wherein The original load data includes one or more sub-original load data, and each sub-original load data includes the original load value corresponding to one or more load indicators. The historical load data includes one or more sub-historical load data, and each historical load data includes the historical load value corresponding to the one or more load indicators. And The normalization process for the original load data includes: For any target load indicator among the load indicators, determine the load boundary value of the target load indicator, where the load boundary value is the maximum or minimum value of the target load indicator in the original load data; and Based on the load boundary value of the target load indicator and the original load value of the target load indicator for the sub-original load data in the original load data, determine the historical load value of the target load indicator for the sub-historical load data in the historical load data.

7. The method according to claim 6, wherein The one or more load indicators include at least one of the following: order volume, order amount, and order volume load.

8. The method according to any one of claims 1-7, wherein The determination of the workstations that each employee of the target entity needs to serve based on the performance parameters of the employees of the target entity, the workload data of the target entity, and the scheduling constraint conditions includes: Determine the performance function of the entity performance of the target entity with respect to the scheduling variables of the employees. The scheduling variables include the first scheduling component indicating the workstations that the employees need to serve. And Based on the performance function, determine the planning objective function of the integer programming model of the target entity, where the planning objective function is to maximize the entity performance of the target entity. Based on the scheduling constraint conditions of the target entity, determine the model constraint conditions of the integer programming model of the target entity, where the model constraint conditions are associated with the scheduling variables of the employees. And Solve the overall planning model of the target entity to determine the values of the scheduling variables of the employees.

9. The method according to claim 8, wherein The first scheduling component includes the workstation scheduling volume corresponding to each workstation, and each component of the workstation scheduling volume includes the employee scheduling volume corresponding to each employee. Among them, corresponding to each workstation, for an employee serving this workstation, the employee scheduling amount corresponding to this employee in the workstation scheduling amount corresponding to this workstation is 1, and for an employee not serving this workstation, the employee scheduling amount corresponding to this employee in the workstation scheduling amount corresponding to this workstation is 0.

10. An apparatus for employee scheduling, comprising: A performance determination module, configured to: determine performance parameters of employees of the target entity based on historical performance data, historical scheduling data, and historical load data of the target entity, where the target entity includes a plurality of workstations for executing production tasks of the target entity, the historical scheduling data indicates the workstations served by the employees historically, the historical load data indicates the historical workloads of the plurality of workstations of the target entity, and the performance parameters indicate the work efficiency of the employees at the plurality of workstations of the target entity; A condition acquisition module, configured to: acquire workload data and scheduling constraint conditions of the target entity, where the workload data includes workloads at a plurality of workstations in the target entity, and the scheduling constraint conditions include constraint conditions for scheduling employees of the target entity; and A scheduling determination module, configured to: determine workstations to be served by each employee of the target entity based on the performance parameters of the employees of the target entity, the workload data, and the scheduling constraint conditions.

11. An electronic device, comprising: At least one processor; And A memory communicatively connected to the at least one processor; Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1-9.

12. A computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to execute the method according to any one of claims 1-9.

13. A computer program product, comprising a computer program, wherein, The computer program implements the method according to any one of claims 1-9 when executed by a processor.

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