CN107808258A - The optimal employee's distribution method of workflow based on traffic log and collaboration mode - Google Patents

The optimal employee's distribution method of workflow based on traffic log and collaboration mode Download PDF

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CN107808258A
CN107808258A CN201711166976.6A CN201711166976A CN107808258A CN 107808258 A CN107808258 A CN 107808258A CN 201711166976 A CN201711166976 A CN 201711166976A CN 107808258 A CN107808258 A CN 107808258A
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王娇娇
俞东进
陈耀旺
张蕾
侯文杰
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Hangzhou Taoyi Data Technology Co ltd
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Hangzhou Dianzi University
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G06Q10/063112—Skill-based matching of a person or a group to a task

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Abstract

The invention discloses a kind of optimal employee's distribution method of workflow based on traffic log and collaboration mode.The present invention regard employee's execution activity as an entirety by introducing the concept of entity, proposes the method that movable cooperation degree and entity cooperation degree calculate respectively and is used for measuring activity and the collaboration capabilities to cooperate between horizontal, entity and entity between activity in historical events daily record.It is high collaboration mode to excavate the entity sequence with high collaboration capabilities, different types of coding is carried out respectively to these patterns and activity flow to be allocated, being quickly found out by the matched rule between coding with bit manipulation can be used for treating the high collaboration mode that allocation activities flow carries out employee's distribution, as candidate employee's allocative decision；A kind of allocative decision in the overall cooperation degree maximum that can cause activity flow to be allocated employee allocative decision optimal as cooperation is finally selected from candidate employee's allocative decision.The present invention can fast and effeciently realize the employee's distribution for cooperating optimal in workflow.

Description

The optimal employee's distribution method of workflow based on traffic log and collaboration mode
Technical field
The invention belongs to the performance of MRC process in BPM to optimize field, and in particular to is based on operation flow to one kind The method that daily record and collaboration mode quickly realize optimal employee's distribution in workflow.
Background technology
The method of salary distribution of human resources (employee) directly affects the quality of tasks carrying in operation flow in workflow.Especially Ground, in an operation flow implementation procedure, substantial amounts of interaction is generally required between adjacent two tasks, and perform these The height of collaboration capabilities between the employee (executor) of business typically can largely influence the execution efficiency of whole flow process. Such as in a software development flow, if coding engineer can cooperate very well with Test Engineer, the exploitation of whole software Progress is possible to be accelerated.Therefore, employee is carried out to the activity in business procedure and distributes the collaboration capabilities for causing whole process Maximum can realizes the performance optimization of business procedure, so as to improve the efficiency of business procedure execution.
Current existing employee's distribution method is mostly the distribution method of based role.But this distribution method is often " coarseness ", possibly it can not be applicable in some cases.In addition, also there is some distribution methods concern fitness, urgent Degree, degree of conformity and availability etc., but seldom from employee's distribution in the angle research flow of cooperation.
The research based on cooperation angle existing at present is all that the association between employee is measured according to history flow execution journal Make ability, then integrally degree of cooperation is up to target and carries out flow employee's distribution with flow according to this measurement, but this point It can be influenceed with the collaboration capabilities that have ignored between employee by their currently execution activities.In fact, due between employee Collaboration capabilities can due to they each execution activity difference and difference, therefore can not be according only to employee when employee distributes Between collaboration capabilities be allocated.Moreover, the phenomenon of unreasonable distribution occurs during this method of salary distribution：Member in flow Collaboration capabilities between work and employee are maximum, but the overall collaboration capabilities of flow are not maximum in actually performing.
The content of the invention
In view of the deficiencies of the prior art, the present invention provides a kind of workflow based on traffic log and collaboration mode Optimal employee's distribution method.
The inventive method comprises the concrete steps that：
The optimal employee's distribution method of workflow based on traffic log and collaboration mode, is comprised the concrete steps that：
The event log data collection recorded in step (1) incoming traffic flow system, the event log data are concentrated every An event in the corresponding flow instance of a line, include flow instance ID, event id and the event attribute of the event, wherein Event attribute includes timestamp attribute, activity name attribute and movable executor's attribute, then according to different flow instance ID, Sequencing to all events in each flow instance according to timestamp attribute, with the activity name attribute of event and activity The combination of executor's attribute is the entity on behalf event, obtains an entity sequence i.e. entity track corresponding to the flow instance ETi, have the event log data collection for including n flow instance for one, obtain entity track data collection ET={ ET1,…, ETi,…,ETn}。
Step (2) removes abnormal, incomplete entity track in the entity track data collection ET obtained in step (1), Then alignd these entity tracks according to the activity name attribute of entity using improved Needleman-Wunsch alignment schemes, Obtain an alignment entity track matrix A Mn×m, every a line in matrix represents the entity track after an alignment, each row generation Alignment element of the every entity track of table on the position, obtained further according to activity corresponding to each row in matrix one most long Active sequences refers to active sequences Ras。
Step (3) is according to the alignment entity track matrix A M obtained in step (2)n×mWith movable cooperation degree calculation formula, reality Body cooperation degree calculation formula obtains the entity track matrix A M that alignsn×mThe continuous activity of all different types of two of middle appearance The activity of composition is to cooperation degree table CTapThe entity formed with different types of two continuous entities is to cooperation degree table CTep, so Alignment entity track matrix A M is calculated according to active sequences cooperation degree calculation formula afterwardsn×mMiddle appearance it is all different types of Active sequences and its cooperation degree, active sequences of the wherein cooperation degree more than minimum Collaboration support degree threshold value min_cpt_sup is put In active sequences set, each activity in the active sequences set is calculated respectively further according to entity sequence cooperation degree calculation formula All different types of entity sequences and its cooperation degree corresponding to sequence, wherein it will be more than minimum cooperation confidence threshold value by degree of cooperation Min_cpt_conf entity sequence is that high collaboration mode is placed in high collaboration mode table HP.
Step (4) is according to the reference active sequences R obtained in step (2)as, respectively to this with reference to each in active sequences Activity carries out binary coding according to sequence of positions from left to right, referring next to the coded system in high collaboration mode table HP Each high collaboration mode be entity sequence, according to the activity name attribute of entity carry out binary coding obtain coded sequence Code1, while the activity flow aP of the employee to be allocated of user's input is subjected to binary coding and obtains coded sequence apCode, Then high collaboration mode is subjected to adjoint binary coding again and obtains coded sequence code2.
The binary system of each high collaboration mode of the step (5) in the high collaboration mode table HP obtained in step (4) is compiled Code code1, the binary coding apCode with binary coding code2 and activity flow aP to be allocated, are advised by matching Then find out in high collaboration mode table HP can with the high collaboration mode of activity flow aP successful match to be allocated, by these height cooperation moulds Formula is as candidate employee's allocative decision；
Step (6) is according to the activity obtained in the candidate employee's allocative decision and step (3) obtained in step (5) to cooperation Spend table CTap, entity is to cooperation degree table CTep, the candidate person that is obtained by entity sequence cooperation degree calculation formula from step (5) A kind of allocative decision is selected in work allocative decision and make it that activity flow aP to be allocated overall cooperation degree is maximum, the allocative decision It is exactly the solution obtained by optimal employee's distribution method.
Workflow optimal employee's distribution method provided by the present invention for traffic log and collaboration mode is by one Group functional module composition, they include：The input module of event log data collection, entity Track Pick-up and alignment module, cooperation Spend computing module, high collaboration mode excavates module, coding module, optimal employee's distribute module.
The input module of event log data collection mainly acquires event log data collection from business process system.
Entity Track Pick-up and alignment module are mainly to carry out conversion processing to the log data set obtained in a upper module, I.e. according to the flow instance ID of different event, to all events in flow instance corresponding to each flow instance ID, according to when Between the sequencing that is occurred according to event of stamp attribute, with the activity name attribute of event and the combination of movable executor's attribute (i.e. entity) represents the event, obtains entity sequence corresponding to flow instance, i.e. entity track；Then remove abnormal, endless Whole entity track, alignd using improved Needleman-Wunsch tracks alignment schemes according to the activity name attribute of entity These entity tracks, obtain an alignment entity track matrix.
Cooperation degree computing module be mainly according to the alignment entity track matrix that is obtained in a upper module and movable cooperation degree, The calculation formula of entity cooperation degree calculates all different types of activities occurred in the matrix of alignment entity track to (i.e. two companies Continuous activity) cooperation degree and all different types of entities to the cooperation degree of (i.e. two continuous entities), respectively obtain work Move to cooperation degree table CTap, entity is to cooperation degree table CTep。
High collaboration mode excavation module is first according to the activity obtained in a upper module to cooperation degree table, entity to cooperation degree The alignment entity track matrix obtained in table and active sequences cooperation degree calculation formula computational entity Track Pick-up and alignment module All different types of active sequenceses and its cooperation degree of middle appearance, then wherein it will be more than minimum Collaboration support degree threshold by degree of cooperation The active sequences of value is placed in active sequences set, and the active sequences set is calculated respectively further according to entity cooperation degree calculation formula In each active sequences corresponding to all different types of entity sequence cooperation degree, wherein degree of cooperation will be more than minimum cooperation and put The entity sequence of confidence threshold is that high collaboration mode is placed in high collaboration mode table.
Coding module is first according to each in the alignment entity track matrix obtained in entity Track Pick-up and alignment module Activity corresponding to row obtains an active sequences, i.e., with reference to active sequences, then according to reference to each movable institute in active sequences Position carry out binary coding can obtain unique encodings corresponding to the activity of diverse location, according to this coding to height cooperate Activity in the activity flow of movable and to be allocated employee corresponding to entity in pattern (i.e. entity sequence) is encoded respectively Binary code sequence is obtained, then high collaboration mode obtained with binary coding with binary code sequence.
Optimal employee's distribute module is mainly the binary coding according to the activity flow to be allocated obtained in a upper module Binary coding, adjoint binary coding with high collaboration mode can obtain the time of the activity flow to be allocated by matched rule Select employee's allocative decision, then further according to obtained in cooperation degree computing module activity to cooperation degree table and entity to cooperation degree table A kind of allocative decision for the overall cooperation degree maximum for enabling to activity flow to be allocated is selected from candidate employee's allocative decision, I.e. optimal employee's allocative decision.
Method proposed by the present invention is based on the employee with reference to existing for being implied in historical events daily record with high collaboration capabilities Allocation model is high collaboration mode so as to quickly realize to the thought that optimal employee distributes in workflow, by introducing entity Concept proposes the method that movable cooperation degree and entity cooperation degree calculate and is used for respectively using employee's execution activity as an entirety Activity and the collaboration capabilities to cooperate between horizontal, entity and entity between activity in measurement historical events daily record.This entity It is exactly to distinguish collaboration capabilities different when execution is different movable between employee and employee that cooperation degree between, which calculates,.Base The i.e. high collaboration mode of entity sequence with high collaboration capabilities can be excavated in these calculating and Apriori thoughts, these associations Operation mode represents the higher employee's method of salary distribution of collaboration capabilities, in order to which these high collaboration modes are efficiently used, it is necessary to these Pattern and activity flow to be allocated carry out different types of coding respectively, can by the matched rule between coding with bit manipulation To be quickly found out the high collaboration mode that can be used for treating the progress employee's distribution of allocation activities flow, as candidate employee distribution side Case；A kind of point in the overall cooperation degree maximum that can cause activity flow to be allocated is finally selected from candidate employee's allocative decision With scheme as the optimal employee's allocative decision that cooperates.With it is traditional it is based role, enter according only to the degree of cooperation between employee Office staff's work point method of completing the square is compared, can fast and effeciently be realized using the method stated of the present invention cooperated in workflow it is optimal Employee distributes.Therefore the performance performed for optimization operation flow and the execution efficiency for improving operation flow have critically important meaning Justice.
Brief description of the drawings
Fig. 1 method Organization Charts；
Fig. 2 manufacturing industry procurement process figures；
Fig. 3 coding schematic diagrams；
Fig. 4 matched rule schematic diagrames.
Embodiment
The tool of workflow optimal employee's distribution method provided by the present invention for traffic log and collaboration mode Body embodiment mainly divides 6 steps (as shown in Figure 1)：
(1) event log data collection (such as table recorded is inputted in certain manufacturing industry procurement business flow (as shown in Figure 2) system Shown in 1), the event log data concentrate every a line correspondence one flow instance in an event, including flow instance ID, Event id and event attribute, wherein event attribute include timestamp attribute, activity name attribute and movable executor's attribute；Then According to different flow instance ID, to all events in each flow instance according to timestamp attribute, i.e., the elder generation that event occurs Order afterwards, the flow is obtained with the activity name attribute of event and combination (i.e. entity) event of representative of movable executor's attribute Entity sequence is entity track corresponding to example, merges entity track corresponding to all flow instances and obtains entity track data Collection：
The event log data collection of table 1
The event log data collection L of manufacturing industry procurement process in table 1 is made up of a series of event, wherein E={ e1； e2；…；ei；…；eLRepresent the set of all events that occurs in L, A={ a1；a2；…；ai；…；aLRepresent what is occurred in L All movable set, R={ r1；r2；…；ri；…；rLRepresent the set of all employees that occurs in L.For event sets E In some event e for, generally with event attribute (#startTime(e),#endTime(e),#activity(e),#resource(e)) come The event is described.Wherein, the event, i.e. entity are represented with the activity name attribute of event and the combination of movable executor's attribute ai|ri, it represents executor (employee) riPerform the event and correspond to movable aiProcess.Therefore certain flow instance is represented with entity In all events, according to the timestamp attribute of event according to the sequencing of generation, use relation>LRepresent (such as a1＞La2Represent Movable a1Occur in movable a2Before), the entity track of all events composition in the flow instance can be obtained, i.e.,For event log Data set L can obtain entity track data collection ET={ ET1,…,ETi,…,ETn}。
(2) some pretreatment operations are carried out to the entity track data collection ET obtained in above-mentioned steps (1)：Remove exception , incomplete entity track, the then activity using improved Needleman-Wunsch tracks alignment schemes according to entity Name attribute is alignd these entity tracks, obtains an alignment entity track matrix A Mn×m, every a line in matrix represents one Entity track after alignment, each row represent alignment element of the every entity track on the position, further according to each in matrix Activity obtains a most long active sequences and refers to active sequences R corresponding to rowas：
1. traveling through entity track data collection ET obtained above, movable the executor's attribute or activity name for having missing are rejected Claim the entity track of attribute；
2. to it is above-mentioned 1. in entity track data collection ET after obtained cleaning, use improved Needleman-Wunsch Alignment schemes are alignd these entity tracks according to the activity name attribute of entity, obtain an alignment entity track matrix A Mn×m：
Wherein ai,j|ri,jRepresent alignment after i-th entity track jth row element, the element be entity or "-" is movable identical in all non-"-" entities of same row；
3. by it is above-mentioned 2. in obtained entity track alignment matrix AMn×mBy row piecemeal：
Activity of the same activity as the row according to corresponding to all non-"-" entities in each row, the m finally obtained The active sequences of activity composition, i.e., with reference to active sequences Ras=<a0；a1；…；aj；…；am-1>。
(3) according to the alignment entity track matrix A M obtained in step (2)n×mWith movable cooperation degree calculation formula, entity association Degree of work calculation formula can obtain the entity track matrix A M that alignsn×mAll different types of activities of middle appearance are to (i.e. two companies Continuous activity) form activity to cooperation degree table CTapThe reality that (i.e. two continuous entities) are formed with different types of entity Body is to cooperation degree table CTep, alignment entity track matrix A M is then calculated according to active sequences cooperation degree calculation formulan×mIn go out Existing all different types of active sequenceses and its cooperation degree, wherein it will be more than minimum Collaboration support degree threshold value min_ by degree of cooperation Cpt_sup active sequences is placed in active sequences set, and the work is calculated respectively further according to entity sequence cooperation degree calculation formula All different types of entity sequences and its cooperation degree corresponding to each active sequences in dynamic arrangement set, will wherein degree of cooperation it is big In minimum cooperation confidence threshold value min_cpt_conf entity sequence be that high collaboration mode is placed in high collaboration mode table HP：
1. according to alignment entity track matrix A Mn×mAll different types of have cooperation relation two are found continuously to live It is dynamic that (i.e. activity is to a1,a2)a1＞La2(i.e. entity is to a with two continuous entities having cooperation relation1|r1,a2|r2)a1|r1＞La2|r2；
2. each activity is calculated to a1,a2Between cooperation degree WhereinRepresent two continuous movable a1＞La2The frequency occurred in event log L,Table Show the average frequency that all types of two continuously actives occur in daily record in event log L, k is one and is used for tuning Parameter；
3. each entity is calculated to a1|r1,a2|r2Between cooperation degree (a1≠a2,r1≠r2；a1|r1＞La2|r2),Represent member in event log L Work r1, r2Execution activity a1, a2Average required time,Represent the execution activity a that cooperated in event log L1,a2It is flat The time required to, k is a parameter for being used for tuning；
4. according to step 2. 3. in calculation formula calculation procedure 1. in obtain activity pair and entity pair cooperation degree, can To obtain activity to cooperation degree table CTapWith entity to cooperation degree table CTep；
5. further according to alignment entity track matrix A Mn×mFind all different types of active sequences as=<ak； ak+1；……；al>(0≤k<L≤m) and entity sequence es=<ak|rk；ak+1|rk+1；……；al|rl>(0≤k<l≤m-1)；
6. calculate the cooperation degree of each active sequencesWill wherein degree of cooperation it is big It is placed in minimum Collaboration support degree threshold value min_cpt_sup active sequences in active sequences set；
7. step 6. in the higher active sequences set of the cooperation degree that is filtrated to get, look for respectively To its corresponding entity sequence, the cooperation degree of each entity sequence is calculated Wherein minimum cooperation confidence will be more than by degree of cooperation Degree threshold value min_cpt_conf entity sequence (i.e. high collaboration mode) is placed in a set, i.e., high collaboration mode table HP=< es,cpt>, wherein cpt represents the cooperation degree of the high collaboration mode es.
(4) according to the reference active sequences R obtained in step (2)as=<a0；a1；…；aj；…；am-1>, respectively to the ginseng The each activity examined in active sequences carries out binary coding according to sequence of positions from left to right, with reference to the coded system to step Suddenly each high collaboration mode (i.e. entity sequence) in the high collaboration mode table HP obtained in (3) is according to the activity name category of entity Property carry out binary coding and obtain coded sequence code1, while the activity flow ap of the employee to be allocated of user's input is carried out Binary coding obtains coded sequence apCode, and high collaboration mode then is carried out into adjoint binary coding again obtains coded sequence code2：
1. according to reference to active sequences Ras=<a0；a1；…；aj；…；am-1>Respectively to this with reference to each in active sequences Activity carries out binary coding, i.e. first movable a according to sequence of positions from left to right0It is encoded to 20, second movable a1Compile Code is 21, the like, the activity that can finally obtain each position corresponds to a binary coding, such as the pretreatment in Fig. 3 It is shown；
2. according to step 1. in activity No, to the high collaboration mode table HP=obtained in step (3)<es,cpt>In Each high collaboration mode (i.e. entity sequence) es=<ak|rk；ak+1|rk+1；……；al|rl>(0≤k<L≤m-1), according to reality The activity name attribute of body is ak；ak+1；……；alBinary coding is carried out respectively and obtains coded sequence code1, such as Fig. 3 (a) institutes Show；
3. similar step is 2., to the activity flow aP=to be allocated of user's input<a1；a2；…；ai；…；aL>(ai∈ A) root According to activity name attribute respectively according to step 1. in coded system carry out binary coding obtain coded sequence apCode；
4. 2. volume that each high collaboration mode in the high collaboration mode table HP obtained in step (3) is obtained according to step 0 whole negate in code sequence code1 in adjacent two 1 can obtain coded sequence code2 with binary coding, such as scheme Shown in 3 (b).
(5) binary system volume is carried out according to each high collaboration mode in the high collaboration mode table HP obtained in step (4) respectively Coded sequence code1 that code obtains, the coded sequence code2 obtained with binary coding and activity flow aP to be allocated enter The coded sequence apCode that row binary coding obtains, can be quick by the matched rule (as shown in Figure 4) with bit manipulation To with activity flow aP=to be allocated<a1；a2；…；ai；…；aL>(ai∈ A) carry out the coded sequence that binary coding obtains The high collaboration mode of apCode successful match.The high collaboration mode es=of these successful match<ak|rk；ak+1|rk+1；……；al| rl>(0≤k<L≤m-1) (i.e. entity sequence, containing movable executor's information) can serve as the candidate of activity flow to be allocated Employee's allocative decision.
(6) according to the work obtained in the candidate employee's allocative decision and step (3) obtained in step (5) Move to cooperation degree table CTap, entity is to cooperation degree table CTep, pass through One kind is selected from candidate employee's allocative decision to enable to distribute Whole activity flow aP=to be allocated afterwards<a1；a2；…；ai；…；aL>(ai∈ A) entity cooperation degree it is maximum, the allocative decision It is exactly the solution obtained by optimal employee's distribution method.
The present invention can be used for quick implementation process in cooperate optimal employee distribution, so as to improve the execution performance of flow and Efficiency.

Claims (1)

1. the optimal employee's distribution method of workflow based on traffic log and collaboration mode, it is characterised in that the tool of this method Body step is：
The event log data collection recorded in step (1) incoming traffic flow system, every a line that the event log data is concentrated An event in a corresponding flow instance, include flow instance ID, event id and the event attribute of the event, wherein event Attribute includes timestamp attribute, activity name attribute and movable executor's attribute, then according to different flow instance ID, to every All events in individual flow instance are performed according to the sequencing of timestamp attribute with the activity name attribute and activity of event The combination of person's attribute is the entity on behalf event, obtains an entity sequence i.e. entity track ET corresponding to the flow instancei, There is the event log data collection for including n flow instance for one, obtain entity track data collection ET={ ET1,…, ETi,…,ETn}；
Step (2) removes abnormal, incomplete entity track in the entity track data collection ET obtained in step (1), then Alignd these entity tracks, obtained according to the activity name attribute of entity using improved Needleman-Wunsch alignment schemes One alignment entity track matrix A Mn×m, every a line in matrix represents the entity track after an alignment, and each row represent every Alignment element of the bar entity track on the position, a most long activity is obtained further according to activity corresponding to each row in matrix Sequence refers to active sequences Ras；
Step (3) is according to the alignment entity track matrix A M obtained in step (2)n×mWith movable cooperation degree calculation formula, entity association Degree of work calculation formula obtains the entity track matrix A M that alignsn×mThe continuous activity of all different types of two of middle appearance is formed Activity to cooperation degree table CTapThe entity formed with different types of two continuous entities is to cooperation degree table CTep, Ran Hougen Alignment entity track matrix A M is calculated according to active sequences cooperation degree calculation formulan×mAll different types of activities of middle appearance Sequence and its cooperation degree, active sequences of the wherein cooperation degree more than minimum Collaboration support degree threshold value min_cpt_sup is placed in work In dynamic arrangement set, each active sequences in the active sequences set is calculated respectively further according to entity sequence cooperation degree calculation formula Corresponding all different types of entity sequences and its cooperation degree, wherein it will be more than minimum cooperation confidence threshold value min_ by degree of cooperation Cpt_conf entity sequence is that high collaboration mode is placed in high collaboration mode table HP；
Step (4) is according to the reference active sequences R obtained in step (2)as, each activity in active sequences is referred to this respectively Binary coding is carried out according to sequence of positions from left to right, referring next to the coded system to every in high collaboration mode table HP Individual high collaboration mode is entity sequence, and carrying out binary coding according to the activity name attribute of entity obtains coded sequence code1, The activity flow aP of the employee to be allocated of user's input is subjected to binary coding simultaneously and obtains coded sequence apCode, Ran Houzai High collaboration mode is carried out to obtain coded sequence code2 with binary coding；
The binary coding of each high collaboration mode of the step (5) in the high collaboration mode table HP obtained in step (4) Code1, the binary coding apCode with binary coding code2 and activity flow aP to be allocated, pass through matched rule Find out in high collaboration mode table HP can with the high collaboration mode of activity flow aP successful match to be allocated, by these high collaboration modes As candidate employee's allocative decision；
Step (6) is according to the activity obtained in the candidate employee's allocative decision and step (3) obtained in step (5) to cooperation degree table CTap, entity is to cooperation degree table CTep, the candidate employee point that is obtained from step (5) by entity sequence cooperation degree calculation formula Make it that activity flow aP to be allocated overall cooperation degree is maximum with a kind of allocative decision is selected in scheme, the allocative decision is exactly Solution obtained by optimal employee's distribution method.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108876108A (en) * 2018-05-17 2018-11-23 浙江工业大学 A kind of task schedule optimization method based on Hospital Logistic delivery system
CN109753591A (en) * 2018-12-11 2019-05-14 江阴逐日信息科技有限公司 Operation flow predictability monitoring method
WO2020204811A1 (en) * 2019-04-02 2020-10-08 Hitachi, Ltd. Method and system for workflow assignment

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040078778A1 (en) * 2002-10-21 2004-04-22 International Business Machines Corporation Resource scheduling in workflow management systems
CN106056300A (en) * 2016-06-08 2016-10-26 南京理工大学 Crisscrossed mixed resource allocation rule mining method
CN106095955A (en) * 2016-06-16 2016-11-09 杭州电子科技大学 The behavior patterns mining method matched based on traffic log and entity track

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040078778A1 (en) * 2002-10-21 2004-04-22 International Business Machines Corporation Resource scheduling in workflow management systems
CN106056300A (en) * 2016-06-08 2016-10-26 南京理工大学 Crisscrossed mixed resource allocation rule mining method
CN106095955A (en) * 2016-06-16 2016-11-09 杭州电子科技大学 The behavior patterns mining method matched based on traffic log and entity track

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108876108A (en) * 2018-05-17 2018-11-23 浙江工业大学 A kind of task schedule optimization method based on Hospital Logistic delivery system
CN108876108B (en) * 2018-05-17 2021-10-29 浙江工业大学 Task scheduling optimization method based on hospital logistics transport system
CN109753591A (en) * 2018-12-11 2019-05-14 江阴逐日信息科技有限公司 Operation flow predictability monitoring method
CN109753591B (en) * 2018-12-11 2024-01-09 江阴逐日信息科技有限公司 Business process predictive monitoring method
WO2020204811A1 (en) * 2019-04-02 2020-10-08 Hitachi, Ltd. Method and system for workflow assignment
JP2022527961A (en) * 2019-04-02 2022-06-07 株式会社日立製作所 Workflow allocation method and system
JP7219353B2 (en) 2019-04-02 2023-02-07 株式会社日立製作所 Workflow assignment method and system

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