Method and device for entity scheduling, electronic equipment and storage medium

Abstract

The invention provides an entity scheduling method and device, electronic equipment, a storage medium and a computer program product, and the method comprises the steps: determining a plurality of performance weights of a target entity based on the historical performance data of the target entity, the target entity having a plurality of performance indicators, and determining the performance weights of the target entity; the plurality of performance weights of the target entity comprise the performance weight corresponding to each performance indicator; acquiring attributes of a plurality of persons of the target entity, wherein the attributes of the persons comprise production tasks which can be undertaken by the persons; obtaining workload data and scheduling constraint conditions of the target entity; and on the basis of the attributes of the plurality of personnel, the performance indicator weight of the target entity, the workload data and the scheduling constraint condition, determining a work station which needs to be served by each personnel of the target entity.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technologies, and in particular, to the field of data processing. Specifically, it relates to a method, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product for entity scheduling. Background Art

[0002] Currently, personnel are mainly scheduled in advance by managers. However, this scheduling method is inefficient, inaccurate, difficult to guarantee the scheduling quality, and lacks flexibility, resulting in low production efficiency, inability to fully utilize production resources, and waste of resources.

[0003] The methods described in this section are not necessarily methods that have been previously conceived or adopted. Unless otherwise specified, no method described in this section should be considered prior art solely 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, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product for entity scheduling.

[0005] According to one aspect of the present disclosure, there is provided a method for entity scheduling, including: determining multiple performance weights of a target entity based on historical performance data of the target entity, where the target entity has multiple performance indicators, and the multiple performance weights of the target entity include performance weights corresponding to each performance indicator; obtaining attributes of multiple personnel of the target entity, where the attributes of the personnel include the production tasks that the personnel can undertake; obtaining workload data and scheduling constraint conditions of the target entity, where the target entity has multiple workstations for executing production tasks of the target entity, the workload data includes task information executed at multiple workstations in the target entity, and the scheduling constraint conditions include constraint conditions for scheduling personnel of the target entity; and determining, based on the attributes of the multiple personnel, the performance indicator weights of the target entity, the workload data, and the scheduling constraint conditions, the workstations that each personnel of the target entity needs to serve.

[0006] According to another aspect of the present disclosure, there is provided an apparatus for entity scheduling, including: a weight determination module configured to determine a plurality of performance weights of a target entity based on historical performance data of the target entity, wherein the target entity has a plurality of performance indicators, and the plurality of performance weights of the target entity include performance weights corresponding to each performance indicator; an attribute acquisition module configured to acquire attributes of a plurality of personnel of the target entity, wherein the attributes of the personnel include production tasks that the personnel can undertake; a condition acquisition module configured to acquire workload data and scheduling constraint conditions of the target entity, wherein the target entity has a plurality of workstations for executing production tasks of the target entity, the workload data includes task information executed at the plurality of workstations in the target entity, and the scheduling constraint conditions include constraint conditions for scheduling personnel of the target entity; and a scheduling determination module configured to determine workstations that each personnel of the target entity needs to serve based on the attributes of the plurality of personnel, the performance indicator weights of the target entity, the workload data, 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 when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the above-mentioned method for entity scheduling.

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

[0009] According to another aspect of the present disclosure, there is provided a computer program product, including a computer program, wherein when the computer program is executed by a processor, the above-mentioned method for entity scheduling is implemented.

[0010] According to the embodiments as described in the present disclosure, by using historical data of the target entity to determine performance weights of a plurality of performance indicators of the target entity, the influence of each performance indicator on the overall operation state of the target entity is accurately and efficiently determined, so that the multi-dimensional performance indicators of the target entity can be dimensionally reduced, and the process of determining a scheduling plan for the target entity can be simplified; and by using the determined performance weights, the acquired current workload data and scheduling constraint conditions of the target entity, the time required for scheduling the target entity can be shortened, the accuracy of scheduling the target entity can be improved, the efficiency and flexibility of scheduling the target entity 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 section 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 readily understood from the following description. Description of the Drawings

[0012] The 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 embodiments 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 entity 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 personnel 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 required to be served by each person of a target entity based on the performance parameters and workload data of the personnel of the target entity and scheduling constraint conditions according to an embodiment of the present disclosure is shown;

[0016] Figure 4 A block diagram of a device for entity 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, timing 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 defined, the element can be one or more. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations.

[0021] As described above, currently, the manager makes prior scheduling arrangements for personnel. However, this way of manually scheduling personnel highly relies on the manager's experience, consumes a large amount of time and manpower, it is difficult to guarantee the scheduling quality, and the flexibility is poor.

[0022] For example, in restaurant management, the restaurant management team needs to determine the personnel scheduling arrangement of the target entity one week in advance based on the estimated restaurant workload (e.g., the number of orders) for the next week. However, this manual personnel scheduling is based on the estimated restaurant workload. Since the time interval between the time of estimating the restaurant workload and the actual time of scheduling personnel is often long, the error between the estimated restaurant workload and the actual restaurant workload is large, resulting in difficulty in guaranteeing the scheduling quality; and, since personnel may take temporary leave, the actual personnel available for scheduling is different from the scheduling personnel considered when determining the pre-determined scheduling plan, 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 entity scheduling, including: determining multiple performance weights of a target entity based on the historical performance data of the target entity, where the target entity has multiple performance indicators, and the multiple performance weights of the target entity include the performance weights corresponding to each performance indicator; obtaining the attributes of multiple personnel of the target entity, where the attributes of the personnel include the production tasks that the personnel can undertake; obtaining the workload data and scheduling constraint conditions of the target entity, where the target entity has multiple workstations for executing the production tasks of the target entity, the workload data includes the task information executed at multiple workstations in the target entity, and the scheduling constraint conditions include the constraint conditions for scheduling the personnel of the target entity; and determining the workstations that each personnel of the target entity needs to serve based on the attributes of the multiple personnel, the performance indicator weights of the target entity, the workload data, and the scheduling constraint conditions.

[0024] According to the embodiments as described in the present disclosure, by using the historical data of the target entity to determine the performance weights of multiple performance indicators of the target entity, it accurately and efficiently determines the impact of each performance indicator on the overall operation status of the target entity, thereby enabling the dimensionality reduction of the multi-dimensional performance indicators of the target entity and simplifying the process of determining the personnel scheduling plan for the target entity; and by using the determined performance weights, the obtained workload data of the current target entity, and the scheduling constraints, it can shorten the time required for personnel scheduling of the target entity, improve the accuracy of personnel scheduling of the target entity, and enhance the efficiency and flexibility of personnel scheduling of the target entity.

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

[0026] Step S110: Based on the historical performance data of the target entity, determine multiple performance weights of the target entity, where the target entity has multiple performance indicators, and the multiple performance weights of the target entity include the performance weights corresponding to each performance indicator;

[0027] Step S120: Obtain the attributes of multiple personnel of the target entity, where the attributes of the personnel include the production tasks that the personnel can undertake;

[0028] Step S130: Obtain the workload data and scheduling constraints of the target entity, where the target entity has multiple workstations for executing the production tasks of the target entity, the workload data includes the task information executed at multiple workstations in the target entity, and the scheduling constraints include the constraints for scheduling the personnel of the target entity; and

[0029] Step S140: Based on the attributes of the multiple personnel, the performance indicator weights of the target entity, the workload data, and the scheduling constraints, determine the workstations that each personnel of the target entity needs to serve.

[0030] In the embodiments as described in the present disclosure, the "target entity" may be an organization that can schedule personnel to complete its work tasks, which may be an organization with a physically operating location geographically (e.g., 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 (e.g., an online store).

[0031] 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 physical operation site in terms of geography, the 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 physical operation site in terms of geography but has an electronic operation relying on computer technology, the 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).

[0032] According to some embodiments, the 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, a "work area" may be a part of a target entity that completes a specific production link. For example, for the organization of a physical operation site 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 physical operation site in terms of geography 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 a pre-sales area and a post-sales area in the graphical user interface).

[0034] In an embodiment as described in the present disclosure, "workload data" is data indicating the workload to be completed by the target entity. According to some embodiments, the workload data of the target entity includes the workload data corresponding to each workstation therein.

[0035] 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 volume of each workstation).

[0036] According to some embodiments, at step S110, the performance weight of the performance indicator of the target entity is determined according to historical data.

[0037] According to some embodiments, determining multiple performance weights of a target entity based on historical performance data of the target entity includes: obtaining the historical performance data of the target entity, where the historical performance data includes historical performance values corresponding to multiple performance indicators of the target entity; for any performance indicator of the target entity, based on the historical performance data, determining a removal weight value and / or a correlation weight value of the performance indicator, where the removal weight value indicates the impact of removing the performance indicator on the data performance value of the historical performance data, and the correlation weight value indicates the correlation between the performance indicator and other performance indicators of the target entity; and determining the performance weights of the multiple performance indicators based on the removal weight value and / or the correlation weight value of each of the multiple performance indicators.

[0038] The following refers to Figure 2 for further description Figure 1 the process of obtaining the performance parameters and workload data of the personnel of the target entity (e.g., Figure 1 step S110 in Figure 2 As shown, step S110 includes:

[0039] Step S210, obtaining the historical performance data of the target entity, where the historical performance data includes historical performance values corresponding to multiple performance indicators of the target entity;

[0040] Step S220, for any target performance indicator of the target entity, based on the historical performance data, determining a removal weight value and / or a correlation weight value of the target performance indicator, where the removal weight value indicates the impact of removing the target performance indicator on the data performance value of the historical performance data, and the correlation weight value indicates the correlation between the target performance indicator and other performance indicators of the target entity; and

[0041] Step S230, determining the performance weights of the multiple performance indicators based on the removal weight value and / or the correlation weight value of each of the multiple performance indicators.

[0042] In the embodiments described in the present disclosure, by obtaining historical data, determining the removal weight value and / or the correlation weight value of each performance indicator for further determining the performance weight of each performance indicator, the impact of each performance indicator on the overall operation status of the target entity can be accurately evaluated, and / or the over-inclination and waste of resources caused by strongly correlated performance indicators can be avoided.

[0043] According to some embodiments, obtaining the historical performance data of the target entity includes: obtaining the original performance data of the target entity; and performing normalization processing on the original performance data to obtain the historical performance data.

[0044] In the embodiments as described in the present disclosure, through normalization processing, the dimensional differences between the original data of different dimensions collected (for example, the original performance values corresponding to different performance indicators, the original load values corresponding to different load indicators) are eliminated.

[0045] 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 normalizing the original performance data.

[0046] According to some embodiments, in step S210, the obtained historical performance data of the target entity includes multiple sub-historical performance data, where each sub-historical performance data is the performance data of the target entity in the corresponding time period (for example, one day).

[0047] According to some embodiments, the historical performance data of the target entity is a matrix R, where the matrix R can be shown as the following formula (1):

[0048]

[0049] Among them, the historical performance data includes N sub-historical performance data corresponding to M performance indicators. Among them, the i-th row of the matrix R is the i-th data (for example, the data of the i-th day), the j-th column of the matrix R is the data corresponding to the j-th indicator, and the element in the i-th row and j-th column of the matrix R is the data corresponding to the j-th indicator in the i-th data of the target entity.

[0050] According to some embodiments, all elements in the above matrix R are greater than or equal to 0. According to other embodiments, the above matrix R also includes elements less than 0 (for example, the profit value of the target entity).

[0051] 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 historical performance data for further processing.

[0052] 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 multiple performance indicators, the historical performance data includes one or more sub-historical performance data, and each sub-historical performance data includes the historical performance values of multiple performance indicators. And normalizing the original performance data to obtain the historical performance data includes: for any one of the performance indicators, determining the performance boundary value of the performance indicator, where the performance boundary value is the maximum or minimum value of the performance indicator in the original performance data; and for each performance indicator, based on the performance boundary value of the performance indicator and the original performance values of the performance indicator in one or more sub-original performance data, determining the historical performance values of the performance indicator in one or more sub-historical performance data.

[0053] In the embodiments 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.

[0054] According to some embodiments, the original performance data of the target entity is a matrix K, where the matrix K can be shown as the following formula (2):

[0055]

[0056] Among them, 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.

[0057] 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 a performance indicator, the historical performance value of the sub-historical performance data corresponding to the performance indicator in the historical performance data is: the value obtained by dividing the corresponding original performance value by the performance boundary value.

[0058] For example, the historical performance value in the matrix R corresponding to the historical performance data where, k ij is the corresponding element in the matrix K corresponding to the original performance data, 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.

[0059] According to some embodiments, for a performance indicator for which maximization is desired, the performance boundary value is set to the maximum value of the original performance values corresponding to the performance indicator in the sub-original performance data within the original performance data. According to some embodiments, the performance indicator includes one or more first performance indicators, and 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 first performance indicator in the sub-original performance data within the original performance data.

[0060] According to some embodiments, for the case where the performance boundary value is the minimum value of the original performance values corresponding to the performance indicator in the sub-original performance data within the original performance data, the historical performance value of the sub-historical performance data in the historical performance data corresponding to the performance indicator is: the value obtained by dividing the performance boundary value by the corresponding original performance value.

[0061] For example, the historical performance value in the matrix R corresponding to the historical performance data where k ij is the corresponding element in the matrix K corresponding to the original performance data, and min j is the performance boundary value of the jth performance indicator, that is, the minimum value of the elements in the jth column of the matrix K.

[0062] According to some embodiments, for a performance indicator for which minimization is desired, the performance boundary value is set to the minimum value of the original performance values corresponding to the performance indicator in the sub-original performance data within the original performance data. According to some embodiments, the performance indicator includes one or more second performance indicators, and 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 in the sub-original performance data within the original performance data.

[0063] According to some embodiments, step S220 includes: determining the total data performance value of the historical performance data based on the historical performance values of the historical performance data (for example, all historical performance values of the historical performance data); and for the historical performance data, determining the removed data performance value of the target performance indicator based on the historical performance values of other performance indicators in the historical performance data, where the other performance indicators are performance indicators of the target entity other than the target performance indicator; and determining the removed weight value of the target performance indicator based on the total data performance value of the historical performance data and the removed data performance value of the target performance indicator.

[0064] In the embodiments described in the present disclosure, by determining the total data performance value of the removed historical performance data and the removed data performance value of the target performance indicator, the impact of removing the target performance indicator on the data performance of the historical performance data can be accurately evaluated.

[0065] According to some embodiments, as described above, the historical performance data includes one or more sub-historical performance data (e.g., sub-historical performance data corresponding to one or more time periods).

[0066] According to some embodiments, based on the historical performance value of the historical performance data, determining the total data performance value of the historical performance data includes: for each sub-historical performance data in the historical performance data, determining the total data performance value of this sub-historical performance data. For example, the total data performance value Si of the i-th sub-historical performance data i may be as shown in the following formula (3):

[0067]

[0068] where ri ij is the index value corresponding to the j-th performance index in the i-th sub-historical performance data, and M is the number of performance indices of the target entity.

[0069] According to some embodiments, for the historical performance data, based on the historical performance values of other performance indices in the historical performance data, determining the removed data performance value corresponding to the target performance index of the historical performance data includes: for each sub-historical performance data in the historical performance data, determining the data performance value of the indices other than the target performance index in this sub-historical performance data. According to some embodiments, a data performance calculation formula similar to the total data performance value is used to determine the removed data performance value. For example, the removed data performance value S′j of the j-th index of the i-th sub-historical performance data ij may be as shown in the following formula (4):

[0070]

[0071] where rik ik is the index value corresponding to the k-th performance index in the i-th sub-historical performance data, and M is the number of performance indices of the target entity.

[0072] According to some embodiments, based on the total data performance value and the removed data performance value of the historical performance data, determining the removed weight value of the target performance index includes: for each sub-historical performance data, based on the total data performance value and the removed data performance value of this sub-historical performance data, determining the removed influence value of this sub-historical performance data corresponding to the target performance index; based on the removed influence values of the sub-historical performance data in the historical performance data, determining the removed influence value of the target performance index; and based on the removed influence value of the target performance index, determining the removed weight value of the target performance index.

[0073] According to some embodiments, for each piece of sub-historical performance data, determining the removal impact value of this piece of sub-historical performance data based on the total data performance value and the removal data performance value of the sub-historical performance data includes: calculating the absolute value of the difference between the total data performance value and the removal data performance value of this piece of sub-historical performance data as the removal impact value of this piece of sub-historical performance data corresponding to the target performance indicator; and calculating the sum of the removal impact values of the sub-historical performance data in the historical performance data as the removal impact value of the target performance indicator. For example, as shown in the following formula (5):

[0074]

[0075] where E j is the removal impact value of the j-th indicator, S′ ij is the removal data performance value of the i-th piece of sub-historical performance data for the j-th indicator, S i is the total data performance value of the i-th piece of sub-historical performance data, and M is the number of performance indicators of the target entity.

[0076] According to some embodiments, determining the removal weight value of the target performance indicator based on the removal impact value of the target performance indicator includes: determining the sum of the removal impact values of all the performance indicators of the target entity based on the removal impact value of the performance indicators of the target entity; and dividing the removal impact value of the target performance indicator by the sum of the removal impact values of all the performance indicators of the target entity to obtain the removal weight value of the target performance indicator. For example, as shown in the following formula (6):

[0077]

[0078] where is the removal weight value of the j-th indicator, E j is the removal impact value of the j-th indicator, and E k is the removal impact value of the k-th indicator.

[0079] According to some embodiments, step S220 further includes: determining the correlation between the target performance indicator and other performance indicators of the target entity based on the historical performance data, where the other performance indicators are the performance indicators of the target entity except the target performance indicator; and determining the correlation weight value of the target performance indicator based on the correlation between the target performance indicator and other performance indicators of the target entity.

[0080] In the embodiments as described in the present disclosure, by determining the correlation between the target performance indicator and other performance indicators of the target entity, and determining the relevant weight value of the target performance indicator based on the correlation between the target performance indicator and other performance indicators of the target entity, it is possible to avoid the error in determining the performance weight caused by the strong correlation between different performance indicators (for example, the weights of two strongly correlated performance indicators are both large), thereby resulting in a low accuracy of the personnel scheduling plan for the target entity in the subsequent steps (for example, being biased towards the strongly correlated performance indicators).

[0081] According to some embodiments, based on historical performance data, determining the correlation between the target performance indicator and other performance indicators of the target entity includes: calculating the average value of the target performance indicator and other performance indicators of the target entity; for each piece of sub-historical performance data, calculating the difference between the indicator value of each performance indicator and the average value as the deviation value of the performance indicator; and determining the correlation between the target performance indicator and other performance indicators of the target entity based on the deviation values of the target performance indicator and other performance indicators.

[0082] According to some embodiments, determining the correlation R jk between the target performance indicator (for example, the j-th indicator) and other performance indicators (for example, the k-th indicator) of the target entity can be shown as the following formula (7):

[0083]

[0084] where N is the number of sub-historical performance data in the historical performance data, r ij , r ik are the indicator values corresponding to the j-th and k-th performance indicators in the i-th piece of sub-historical performance data, respectively represent the average values of the j-th and k-th indicators.

[0085] According to some embodiments, based on the correlation between the target performance indicator and other performance indicators of the target entity, determining the relevant weight value of the target performance indicator includes calculating according to the following formula (8):

[0086]

[0087] where is the relevant weight value of the j-th indicator, M is the number of performance indicators of the target entity, and R jk is the correlation between the j-th indicator and the k-th indicator.

[0088] According to some embodiments, sorting data of multiple performance indicators of a target entity is obtained, where the sorting data indicates the sorting of the importance of each performance indicator from high to low; and based on the sorting data of the multiple performance indicators, sorting weight values of the multiple performance indicators are determined, where the sorting weight values are negatively correlated with the sorting values of the performance indicators. For example, the sorting weight value of a performance indicator is calculated as shown in the following formula (9):

[0089]

[0090] where is the sorting weight value of the j-th indicator, M is the number of performance indicators of the target entity, q i and q j are the sorting values of the i-th indicator and the j-th indicator respectively (i.e., the positions where the indicators are ranked).

[0091] According to some embodiments, determining the performance weight of a target performance indicator based on the removal weight value and / or the correlation weight value of the target performance indicator includes: determining the performance weight of the target performance indicator based on at least one of the removal weight value, the correlation weight value, and the sorting weight value of the target performance indicator.

[0092] In the embodiments described in the present disclosure, by converting the sorting data of the performance indicators of the target entity into sorting weight values, the accuracy of the performance weight of the target performance indicator can be further improved.

[0093] According to some embodiments, based on the removal weight value, the correlation weight value, and the sorting weight value of the target performance indicator, the performance weight of the target performance indicator is determined. For example, as shown in formula (10):

[0094]

[0095] where ω j is the target weight of the j-th indicator, is the removal weight value of the j-th indicator, is the correlation weight value of the j-th indicator, is the sorting weight value of the j-th indicator.

[0096] According to some embodiments, a preset weight value can be used instead of the sorting weight value (for example, a preset weight value is set for each performance indicator of the target entity) to be used for determining the performance weight of the target performance indicator.

[0097] According to some embodiments, at step S130, based on the operation requirements of the target entity, one or more scheduling constraint conditions of the target entity are obtained (for example, each person can only go to the workstations where the person has the corresponding skills). According to some embodiments, at step S130, based on the goal of optimizing entity scheduling, one or more scheduling constraint conditions of the target entity are obtained (for example, the number of people assigned to each work area is greater than or equal to the predetermined number threshold of that work area).

[0098] 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.

[0099] According to some embodiments, the scheduling constraint conditions include at least one of the following: each person can only go to the workstations where the person has the corresponding skills; each person is assigned to the workstations in the work area that the person needs to serve; each person cannot be assigned to the workstations in the work area that the person does not serve; each person can only serve one work area; the number of workstations assigned to each person should be less than or equal to the predetermined station threshold; the number of people assigned to each work area is greater than or equal to the predetermined number threshold of that work area; and the number of area change person-times of the current entity scheduling relative to the previous entity scheduling should be less than or equal to the predetermined change threshold.

[0100] In the embodiments described in the present disclosure, by obtaining the scheduling constraint conditions, the obtained entity scheduling plan can be made to meet the actual scheduling requirements as much as possible.

[0101] According to some embodiments, the attributes of the attribute personnel also include the performance parameters of the personnel of the target entity, and the performance parameters indicate the work efficiency of the personnel at multiple workstations of the target entity.

[0102] Figure 3 It shows the process of determining the workstations that each person of the target entity needs to serve based on the attributes of multiple personnel, the performance index weights of the target entity, the workload data, and the scheduling constraint conditions (for example, Figure 1 step S140 in Figure 3 ). As

[0103] shown, step S140 includes:

[0104] Step S310, based on the performance parameters of the personnel of the target entity, and the performance index weights and workload data of the target entity, determine the performance function of the entity performance of the target entity relative to the scheduling variables of the personnel; and

[0105] Step S330: Determine the model constraint conditions of the target entity based on the production tasks that the personnel can undertake and the scheduling constraint conditions of the target entity, where the model constraint conditions are associated with the scheduling variables of the personnel; and

[0106] Step S340: Determine the scheduling variables of the personnel based on the planning objective function and the scheduling constraint conditions.

[0107] In the embodiments described in the present disclosure, by converting the requirements for maximizing the production capacity of the target entity and various scheduling constraints considered in the actual scheduling into the planning objective function and the scheduling constraint conditions, the automated calculation of the entity scheduling can be realized, thereby shortening the time of the entity scheduling, improving the efficiency of the entity scheduling, and moreover, improving the flexibility and real-time performance of the entity scheduling.

[0108] According to some embodiments, the entity performance of the target entity may be a value used to measure the overall operation status of the target entity. For example, it may be the weighted sum of the respective performance indicators of the target entity. According to some embodiments, the performance values of the target entity corresponding to the respective performance indicators may be calculated based on the sub-performance parameters of each personnel. According to some embodiments, based on the performance indicator weights of the target entity, the performance values of the target entity corresponding to the respective performance indicators may be weighted and summed to obtain the entity performance of the target entity.

[0109] For example, the performance function may be as shown in the following formula (11):

[0110]

[0111] The definitions of the variables in formula (11) are as follows:

[0112] 1) The set K is the set of performance indicators of the target entity, where k ∈ K;

[0113] 2) The set N is the set of personnel of the target entity that can be scheduled currently, where i ∈ N;

[0114] 3) The set M is the set of workstations of the target entity, where j ∈ M;

[0115] 4) ω k is the weight coefficient of the performance indicator k;

[0116] 5) x ij is a 0-1 variable indicating whether the personnel i serves at the workstation j. Among them, x ij = 1 means that the personnel i is assigned to the workstation j, otherwise x ij = 0

[0117] 6) p ijk is the sub-performance parameter of the personnel i at the workstation j with respect to the performance indicator k; and

[0118] 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 metrics.

[0119] According to some embodiments, at step S320, the 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 (11), the objective function can be constructed as Equation (12):

[0120]

[0121] The definitions of the variables in Equation (12) are the same as those in Equation (11), so they are omitted here.

[0122] According to some embodiments, at step S330, the scheduling constraint conditions of the target entity are converted into model constraint conditions of the target entity.

[0123] According to some embodiments, the constraint conditions can also include constraints on the values of the scheduling variables of personnel. For example, the scheduling variables of personnel are constrained to be 0-1 variables.

[0124] According to some embodiments, the scheduling component includes the workstation scheduling amount corresponding to each workstation, and each workstation scheduling component includes the personnel scheduling amount corresponding to each person. Wherein, corresponding to each workstation, for the personnel serving this workstation, the personnel scheduling amount corresponding to this person in the workstation scheduling amount corresponding to this workstation is 1, and for the personnel not serving this workstation, the personnel scheduling amount corresponding to this person in the workstation scheduling amount corresponding to this workstation is 0.

[0125] According to some embodiments, the target entity has multiple work areas for the production links corresponding to the target entity. Wherein, each work area includes one or more workstations, and the scheduling variables further include a second scheduling component indicating the work area that the personnel need to serve. Wherein, the second scheduling component includes the area scheduling amount corresponding to each work area, and each area scheduling component includes the personnel scheduling amount corresponding to each person. And wherein, corresponding to each work area, for the personnel serving this work area, the personnel scheduling amount corresponding to this person in the area scheduling amount corresponding to this work area is 1, and for the personnel not serving this work area, the personnel scheduling amount corresponding to this person in the area scheduling amount corresponding to this work area is 0.

[0126] According to some embodiments, the constraint conditions of the integer programming model can be constructed as shown in Equation (13) below:

[0127]

[0128] Among them, the definitions of the common variables in Formula (13) and Formulas (11) and (12) are the same as those described above with reference to Formula (11), so they are omitted here, and the definitions of other variables in Formula (13) are as follows:

[0129] 1) Set M′ i is the set of workstations that person i can serve. For example, person Zhang San can only work at the meal preparation workstation, the baking workstation, and the thawing workstation;

[0130] 2) y is is a 0-1 variable indicating whether person i is currently serving in the work area. Among them, y is = 1 means that person i is assigned to workstation s, otherwise y is = 0;

[0131] 3) is a 0-1 variable indicating whether person i served in the work area during the previous scheduling. Among them, means that person i is assigned to workstation s, otherwise

[0132] 4) Set S is the set of work areas of the target entity, where s ∈ M;

[0133] 5) Set M s is the set of workstations corresponding to work area s;

[0134] 6) is the maximum value of the workstations served by each person;

[0135] 7) b s is the minimum value of the people served in work area s;

[0136] 8) amount max is the maximum threshold for adjusting the number of people.

[0137] Moreover, constraint (a) in formula (13) indicates that each person can only go to the workstations where the person has the corresponding skills; constraint (b) indicates that if the workstation required by a person belongs to a certain work area, then the person serves in that work area, that is, each person is assigned to the workstation in the work area required by the person; constraint (c) indicates that if the workstation required by a person is not assigned to any workstation in the work area, then the person does not serve in that work area, that is, each person cannot be assigned to the workstation in the work area that the person does not serve; constraint (d) indicates that the number of workstations assigned to a person should be less than or equal to a predetermined site threshold; constraint (e) indicates that each person can only serve in one work area; constraint (f) indicates that the number of people 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 regional change person-times of the current entity scheduling relative to the previous entity scheduling should be less than or equal to a predetermined change threshold; constraint (h) indicates the value range of each variable of the integer programming model.

[0138] 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 people assigned to the work area last time (for example, subtracting one from the number of people 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 people in the pre-scheduled work area.

[0139] In the embodiments as described in the present disclosure, determining the predetermined number threshold of the work area based on the number of people assigned to the work area last time or the number of people 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 people assigned to a certain work area as much as possible.

[0140] According to some embodiments, based on the performance parameters of the personnel of the target entity, the workload data of the target entity, and the scheduling constraint conditions, determining the workstations served by each person 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.

[0141] Figure 4 FIG. shows a structural block diagram of an apparatus 400 for entity scheduling according to an exemplary embodiment of the present disclosure.

[0142] As shown Figure 4 in FIG. 400, the apparatus 400 includes:

[0143] A weight determination module 410, configured to: determine multiple performance weights of a target entity based on historical performance data of the target entity, where the target entity has multiple performance indicators, and the multiple performance weights of the target entity include performance weights corresponding to each performance indicator;

[0144] An attribute acquisition module 420, configured to: acquire attributes of multiple employees of the target entity, where the attributes of the personnel include the production tasks that the personnel can undertake;

[0145] A condition acquisition module 430, configured to: acquire workload data and scheduling constraint conditions of the target entity, where the target entity has multiple workstations for executing production tasks of the target entity, the workload data includes task information executed at multiple workstations in the target entity, and the scheduling constraint conditions include constraint conditions for scheduling personnel of the target entity; and

[0146] A scheduling determination module 440, configured to: determine workstations that each personnel of the target entity needs to serve based on attributes of multiple personnel, performance indicator weights of the target entity, workload data, and scheduling constraint conditions.

[0147] According to some embodiments, the weight determination module 410 includes:

[0148] A historical data acquisition sub-module, configured to: acquire historical performance data of the target entity, where the historical performance data includes historical performance values corresponding to multiple performance indicators of the target entity; a weight value determination sub-module, configured to: for any target performance indicator of the target entity, determine a removal weight value and / or a correlation weight value of the target performance indicator based on the historical performance data, where the removal weight value indicates the influence of removing the target performance indicator on the data performance value of the historical performance data, and the correlation weight value indicates the correlation between the target performance indicator and other performance indicators of the target entity; and a performance weight determination sub-module, configured to: determine performance weights of multiple performance indicators based on the removal weight value and / or the correlation weight value of each of the multiple performance indicators. According to some embodiments, the historical data acquisition sub-module includes:

[0149] A raw data acquisition sub-module, configured to: acquire raw performance data of the target entity; and

[0150] A normalization sub-module, configured to: perform normalization processing on the raw performance data to obtain historical performance data.

[0151] 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 multiple performance indicators, the historical performance data includes one or more sub-historical performance data, and each sub-historical performance data includes the historical performance values of multiple performance indicators. And the normalization process of the original performance data to obtain the historical performance data includes: for any performance indicator among the performance indicators, determining the performance boundary value of the performance indicator, where the performance boundary value is the maximum or minimum value of the performance indicator in the original performance data; and for each performance indicator, based on the performance boundary value of the performance indicator and the original performance values of the performance indicator in one or more sub-original performance data, determining the historical performance values of the performance indicator in one or more sub-historical performance data. According to some embodiments, the performance indicators include one or more first performance indicators, and 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 original performance value corresponding to the performance indicator in the sub-original performance data in the original performance data.

[0152] According to some embodiments, the performance indicators include one or more second performance indicators, and one or more second performance indicators include at least one of customer complaint data and inbound abnormal data. And corresponding to the second performance indicator, the performance boundary value is the minimum value of the original performance value corresponding to the performance indicator in the sub-original performance data in the original performance data.

[0153] According to some embodiments, for any target performance indicator in the target entity, determining the removal weight value and / or the correlation weight value of the target performance indicator based on the historical performance data includes: determining the total data performance value of the historical performance data based on the historical performance values of the historical performance data; and for the historical performance data, determining the removal data performance value of the target performance indicator based on the historical performance values of other performance indicators in the historical performance data, where other performance indicators are the performance indicators of the target entity other than the target performance indicator; and determining the removal weight value of the target performance indicator based on the total data performance value of the historical performance data and the removal data performance value of the target performance indicator.

[0154] According to some embodiments, where for any target performance indicator in the target entity, determining at least one of the removal weight value and the correlation weight value of the target performance indicator based on the historical performance data further includes: determining the correlation between the target performance indicator and other performance indicators of the target entity based on the historical performance data, where other performance indicators are the performance indicators of the target entity other than the target performance indicator; and determining the correlation weight value of the target performance indicator based on the correlation between the target performance indicator and other performance indicators of the target entity.

[0155] According to some embodiments, determining multiple performance weights of a target entity based on historical performance data of the target entity further includes: obtaining sorting data of multiple performance indicators of the target entity, where the sorting data indicates the sorting of the importance of each performance indicator from high to low; determining sorting weight values of the multiple performance indicators based on the sorting data of the multiple performance indicators, and determining the performance weight of a target performance indicator based on the removal weight value and / or the correlation weight value of the target performance indicator includes: determining the performance weight of the target performance indicator based on at least one of the removal weight value, the correlation weight value, and the sorting weight value of the target performance indicator. According to some embodiments, the attributes of a person further include the performance parameters of the person of the target entity, and the performance parameters indicate the work efficiency of the person at multiple workstations of the target entity. The scheduling determination module 430 includes:

[0156] A performance function determination sub-module, configured to: determine a performance function of the entity performance of the target entity relative to the scheduling variable of the person based on the performance parameters of the person of the target entity, the performance indicator weights of the target entity, and the workload data, where the scheduling variable is used to indicate whether the person serves the workstation, and the coefficients of the performance function are determined based on the performance parameters of the person of the target entity, the performance indicator weights of the target entity, and the workload data;

[0157] A target function determination sub-module, configured to: determine a planning target function of the target entity based on the performance function, where the planning target function is to maximize the entity performance of the target entity;

[0158] A constraint condition determination sub-module, configured to: determine model constraint conditions of the target entity based on the production tasks that the person can undertake and the scheduling constraint conditions of the target entity, where the model constraint conditions are associated with the scheduling variable of the person; and

[0159] A solving sub-module, configured to: determine the scheduling variable of the person based on the planning target function and the scheduling constraint conditions.

[0160] According to some embodiments, the scheduling variable includes the workstation scheduling amount corresponding to each workstation, and each workstation scheduling component includes the person scheduling amount corresponding to each person. Wherein, corresponding to each workstation, for the person serving the workstation, the person scheduling amount corresponding to the person in the workstation scheduling amount corresponding to the workstation is 1, and for the person not serving the workstation, the person scheduling amount corresponding to the person in the workstation scheduling amount corresponding to the workstation is 0.

[0161] It should be understood that Figure 4 each unit 410 to 430 of the device 400 shown in Figure 1Each step S110 to S130 in the described method 100 corresponds. Thus, the operations, features, and advantages described above for method 100 also apply to apparatus 400 and the units it includes. For the sake of brevity, some operations, features, and advantages are not described herein again.

[0162] 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-described method for entity scheduling.

[0163] 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-described method for entity scheduling.

[0164] 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 entity scheduling.

[0165] See Figure 5 , a block diagram of an electronic device 500 that may be 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.

[0166] 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.

[0167] 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 the program code of the operating system 502a, the program code of the application 502b, etc.

[0168] 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 (e.g., 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 discs (e.g., CD, DVD), 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 in this document, 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.

[0169] The I / O device 504 may include input devices and / or output devices. The input device may be any type of device that can input information into the electronic device 500, and may include, but is 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 device may be any type of device that can present information, and may include, but is not limited to, a video / audio output terminal, a vibrator, and / or a printer.

[0170] The communication interface 507 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and may include, but is 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.

[0171] The application 502b in the working register 502 may be loaded and executed for the various methods and processes described above, for example Figure 1Steps S110 - S140 therein. 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 entity scheduling described above may be performed.

[0172] The various embodiments of the systems and techniques described above in this document may 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 a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may 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 may be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0173] The program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes may 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 a remote machine or server.

[0174] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may 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 machine-readable storage media would include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0175] 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).

[0176] The systems and techniques described herein can be implemented in a computing system that includes back-end 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 front-end 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 back-end components, middleware components, or front-end 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.

[0177] A computing system can include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship to each other.

[0178] It should be understood that various forms of the flows 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 solutions disclosed in this disclosure can be achieved, and no limitation is imposed herein.

[0179] 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 claims after authorization and their equivalent scope. Various elements in the embodiments or examples may be omitted or replaced by their equivalent elements. In addition, the various steps may be performed 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 entity scheduling, comprising: Based on the historical performance data of the target entity, determine multiple performance weights of the target entity, where the target entity has multiple performance indicators, and the multiple performance weights of the target entity include performance weights corresponding to each performance indicator; Obtain the attributes of multiple personnel of the target entity, where the attributes of the personnel include the production tasks that the personnel can undertake; Obtain the workload data and scheduling constraints of the target entity, where the target entity has multiple workstations for executing the production tasks of the target entity, the workload data includes task information executed at multiple workstations in the target entity, and the scheduling constraints include constraints for scheduling the personnel of the target entity; and Based on the attributes of the multiple personnel, the performance indicator weights of the target entity, the workload data, and the scheduling constraints, determine the workstations that each personnel of the target entity needs to serve.

2. The method according to claim 1, wherein The determining multiple performance weights of the target entity based on the historical performance data of the target entity includes: Obtain the historical performance data of the target entity, where the historical performance data includes historical performance values corresponding to multiple performance indicators of the target entity; For any target performance indicator of the target entity, based on the historical performance data, determine the removal weight value and / or the correlation weight value of the target performance indicator, where the removal weight value indicates the impact of removing the target performance indicator on the data performance value of the historical performance data, and the correlation weight value indicates the correlation between the target performance indicator and other performance indicators of the target entity; and Based on the removal weight value and / or the correlation weight value of each of the multiple performance indicators, determine the performance weights of the multiple performance indicators.

3. The method according to claim 2, wherein The obtaining the historical performance data of the target entity includes: Obtain the original performance data of the target entity; and Perform normalization processing on the original performance data to obtain the historical performance data, where the original performance data includes one or more sub-original performance data, each sub-original performance data includes the original performance values of the multiple performance indicators, the historical performance data includes one or more sub-historical performance data, and each sub-historical performance data includes the historical performance values of the multiple performance indicators, and the performing normalization processing on the original performance data to obtain the historical performance data includes: For any one of the performance indicators, determine the performance boundary value of the performance indicator, where the performance boundary value is the maximum or minimum value of the performance indicator in the original performance data; and For each performance indicator, based on the performance boundary value of the performance indicator and the original performance values of the performance indicator in the one or more sub-original performance data, determine the historical performance values of the performance indicator in the one or more sub-historical performance data.

4. The method according to claim 3, 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 favorable comment 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, 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 of the sub-original performance data in the original performance data.

5. The method according to any one of claims 2 - 4, wherein For any target performance indicator in the target entity, determining the removal weight value and / or the correlation weight value of the target performance indicator based on the historical performance data includes: Determining the total data performance value of the historical performance data based on the historical performance value of the historical performance data; and For the historical performance data, determining the removal data performance value of the target performance indicator based on the historical performance values of other performance indicators in the historical performance data, where the other performance indicators are performance indicators of the target entity other than the target performance indicator; and Determining the removal weight value of the target performance indicator based on the total data performance value of the historical performance data and the removal data performance value of the target performance indicator.

6. The method according to any one of claims 2 - 5, wherein For any target performance indicator in the target entity, determining the removal weight value and / or the correlation weight value of the target performance indicator based on the historical performance data further includes: Determining the correlation between the target performance indicator and other performance indicators of the target entity based on the historical performance data, where the other performance indicators are performance indicators of the target entity other than the target performance indicator; and Determining the correlation weight value of the target performance indicator based on the correlation between the target performance indicator and other performance indicators of the target entity.

7. The method according to any one of claims 1 - 6, wherein Determining the multiple performance weights of the target entity based on the historical performance data of the target entity further includes: Obtaining the sorting data of multiple performance indicators of the target entity, where the sorting data indicates the sorting of the importance of each performance indicator from high to low; and Determining the sorting weight values of the multiple performance indicators based on the sorting data of the multiple performance indicators, where the sorting weight values are negatively correlated with the sorting values of the performance indicators, and Determining the performance weight of the target performance indicator based on the removal weight value and / or the correlation weight value of the target performance indicator includes: Determining the performance weight of the target performance indicator based on at least one of the removal weight value, the correlation weight value, and the sorting weight value of the target performance indicator.

8. The method according to any one of claims 1 - 6, wherein The attributes of the personnel further include the performance parameters of the personnel of the target entity, and the performance parameters indicate the work efficiency of the personnel at multiple workstations of the target entity, and Determining the workstations that each person of the target entity needs to serve based on the attributes of the multiple personnel, the performance indicator weights of the target entity, the workload data, and the scheduling constraint conditions includes: Determine a performance function of the entity performance of the target entity with respect to the scheduling variable of the personnel based on the performance parameters of the personnel of the target entity, as well as the performance index weights and workload data of the target entity, wherein the scheduling variable is used to indicate whether the personnel serves the workstation, and the coefficients of the performance function are determined based on the performance parameters of the personnel of the target entity, the performance index weights of the target entity, and the workload data; Determine a planning objective function of the target entity based on the performance function, wherein the planning objective function is to maximize the entity performance of the target entity; Determine the model constraint conditions of the target entity based on the production tasks that the personnel can undertake and the scheduling constraint conditions of the target entity, wherein the model constraint conditions are associated with the scheduling variable of the personnel; and Determine the scheduling variable of the personnel based on the planning objective function and the scheduling constraint conditions.

9. The method according to claim 8, wherein, The scheduling variable includes the workstation scheduling quantity corresponding to each workstation, and each workstation scheduling component includes the personnel scheduling quantity corresponding to each personnel, wherein, corresponding to each workstation, for the personnel serving the workstation, the personnel scheduling quantity corresponding to the personnel in the workstation scheduling quantity corresponding to the workstation is 1, and for the personnel not serving the workstation, the personnel scheduling quantity corresponding to the personnel in the workstation scheduling quantity corresponding to the workstation is 0.

10. An apparatus for entity scheduling, comprising: A weight determination module, configured to: determine multiple performance weights of the target entity based on the historical performance data of the target entity, wherein the target entity has multiple performance indicators, and the multiple performance weights of the target entity include the performance weights corresponding to each performance indicator; An attribute acquisition module, configured to: acquire the attributes of multiple personnel of the target entity, and the attributes of the personnel include the production tasks that the personnel can undertake; A condition acquisition module, configured to: acquire the workload data and scheduling constraint conditions of the target entity, wherein the target entity has multiple workstations for executing the production tasks of the target entity, the workload data includes the task information executed at multiple workstations in the target entity, and the scheduling constraint conditions include the constraint conditions for scheduling the personnel of the target entity; and A scheduling determination module, configured to: determine the workstations that each personnel of the target entity needs to serve based on the attributes of the multiple personnel, the performance index weights 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 so that the at least one processor can 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, when executed by a processor, implements the method according to any one of claims 1-9.

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