Andres jimenez c ai-se13 presentation
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Transcript of Andres jimenez c ai-se13 presentation
Generating Multi-objective Optimized Business Process Enactment Plans
25th International Conference on
Advanced Information Systems Engineering
2013
Andrés Jiménez, Irene Barba, Carmelo del Valle and Barbara Weber Departamento de Lenguajes y Sistemas Informáticos. University of Seville, Spain
{ajramirez, irenebr, carmelo}@us.es
Department of Computer Science, University of Innsbruck, Austria [email protected]
CAiSE 2013 – 17-21 June, Valencia (Spain) 2/33
System Configuration
Process EnactmentEvaluation
Process Design & Analysis
BPM lifecycle
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Designing the model
Ferreira, H.M. et al. (2006)
Karim, A. et al. (2013)
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Flexible design
CAiSE 2013 – 17-21 June, Valencia (Spain)
A declarative language for modelling dynamic business processes
1) Tasks (smallestunit of work)
2) Relations (constraints, no order of execution)
A B C0..2 1
if A is executed, B is executed and
vice versa
B can be executed at most twice
every B is eventually
followed by CC is executed
once
Declare (2006)
Declarative languages
Pesic, M. and van der Aalst, W.M.P. : (2006)
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CAiSE 2013 – 17-21 June, Valencia (Spain)
Just say what, and
let the AI tell you
how.
Our proposal
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CAiSE 2013 – 17-21 June, Valencia (Spain)
Just say what, and
let the AI tell you
how.
Our proposal
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CAiSE 2013 – 17-21 June, Valencia (Spain)
Just say what, and
let the AI tell you
how.
Our proposal
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CAiSE 2013 – 17-21 June, Valencia (Spain)
Recommendations
Just say what, and
let the AI tell you
how.
Our proposal
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CAiSE 2013 – 17-21 June, Valencia (Spain)
Outline
1. Background & Introduction
2. The What. Extension of Declare
3. The How. BP Enactment Plans
4. Constraint Satisfaction Problems and Optimization
5. Future work
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2. Declare-R an extension of Declare
Estimates + Resources + Multiple Instances + Data + Temporal
(0, 10)
Client Data (client) {clientName,
bookedServices, appointmentTime}
this.startTime ≥ client.appointmentTime
20Different activity
attributes
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CAiSE 2013 – 17-21 June, Valencia (Spain) 12/33
2. Declare-R an extension of Declare
Services
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2. Declare-R an extension of Declare
CAiSE 2013 – 17-21 June, Valencia (Spain)
3. Enactment Plans how is it executed
R1A
0..2
4
1
3
2R1C
1
1
1 Res. Availability#R1: 1#R2: 2
profit
durationR2B
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CAiSE 2013 – 17-21 June, Valencia (Spain)
3. Enactment Plans how is it executed
Plan 1
t = 0 1 2 3 4
R1
R2
A A A A C
B B B
Res. Availability#R1: 1#R2: 2
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profit
durationR1A
0..2
4
1
3
2R1C
1
1
1
R2B
CAiSE 2013 – 17-21 June, Valencia (Spain)
3. Enactment Plans how is it executed
Plan 1
t = 0 1 2 3 4
R1 A A A A C
R2 B B B
t = 0 1 2 3 4 5 6
R1
R2
Plan 2
A A A A C
B B B B B B
Res. Availability#R1: 1#R2: 2
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profit
durationR1A
0..2
4
1
3
2R1C
1
1
1
R2B
CAiSE 2013 – 17-21 June, Valencia (Spain)
3. Enactment Plans how is it executed
Plan 1
t = 0 1 2 3 4
R1 A A A A C
R2 B B B
t = 0 1 2 3 4 5 6
R1 A A A A C
R2 B B B B B B
Plan 2 Plan 3
t = 0 1 2 3 4
R1
R21
R22
A A A A C
B B B
B B B
Res. Availability#R1: 1#R2: 2
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profit
durationR1A
0..2
4
1
3
2R1C
1
1
1
R2B
CAiSE 2013 – 17-21 June, Valencia (Spain)
3. Enactment Plans how is it executed
Plan 1
t = 0 1 2 3 4
R1 A A A A C
R2 B B B
t = 0 1 2 3 4 5 6
R1 A A A A C
R2 B B B B B B
Plan 2 Plan 3
t = 0 1 2 3 4
R1
R21
R22
A A A A C
B B B
B B B
Plan 4
t = 0
R1 C
Total time: 5Total profit: 4
Total time: 7Total profit: 6
Total time: 5Total profit: 6
Total time: 1Total profit: 1
Minimize total timeMaximize total profit
Res. Availability#R1: 1#R2: 2
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profit
durationR1A
0..2
4
1
3
2R1C
1
1
1
R2B
CAiSE 2013 – 17-21 June, Valencia (Spain)
3. Enactment Plans how is it executed
Plan 1
t = 0 1 2 3 4
R1 A A A A C
R2 B B B
t = 0 1 2 3 4 5 6
R1 A A A A C
R2 B B B B B B
Plan 2 Plan 3
t = 0 1 2 3 4
R1
R21
R22
A A A A C
B B B
B B B
Plan 4
t = 0
R1 C
Total time: 5Total profit: 4
Total time: 7Total profit: 6
Total time: 5Total profit: 6
Total time: 1Total profit: 1
Minimize total timeMaximize total profit
Res. Availability#R1: 1#R2: 2
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profit
durationR1A
0..2
4
1
3
2R1C
1
1
1
R2B
CAiSE 2013 – 17-21 June, Valencia (Spain)
4. Constraint Satisfaction Problem
A CSP is composed by - a set of variables, - a domain of values for each variable,- and a set of constraints between variables.
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The solutions of a CSP are all the possible combinations of values of the variables which satisfy the constraints.
search algorithm
CAiSE 2013 – 17-21 June, Valencia (Spain)
4. Constraint Satisfaction Problem
Solve a Constraint Satisfaction / (CSP/COP)
Generate an Enactment Plan Optimization Problem
Res. Availability#R1: 1#R2: 2
Number of times the activity is executed
resource selection
High level constraints
Optimization
Minimize(OCT)
Overall completion
time
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R1A
0..2
1
4
2
3R1C
1
1
1
R2B
Start time
CAiSE 2013 – 17-21 June, Valencia (Spain)
OF2
OF1
4. Multi-objective approach
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OF2
OF1
4. Multi-objective approach
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Ɛ-constraint method
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OF2
OF1
4. Multi-objective approach
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Ɛ-constraint method
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OF2
OF1
Pareto Front solutions
4. Multi-objective approach
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Low work load
High work load
4. Multi-objective approach
Number of clients
Waiting Timeor Profit
15 minutes of waiting time!
Future Work
- Robustnesst = 0 1 2 3 4 5 6 7
R1 A1 A2 A2 A2 A2 A2 C2
R21 B2 B2 B2
R22 B2 B2 B2
t = 0 1 2 3 4 5 6 7
R1 A1 A2 A2 A2 A2 A2 C2
R21 B2 B2 B2 B2 B2 B2
Same completion timeSame total profit
- Stochastic attributes
R1C
[1..5]
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Thank youAny question?
21st International Conference on Information Systems Development
2012
Andrés Jiménez Ramírez Departamento de Lenguajes y Sistemas Informáticos.
University of Seville, Spain
CAiSE 2013 – 17-21 June, Valencia (Spain)
Applications
1) Simulation
2) Time prediction
3) Recommendations
4) Generation BP models
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CAiSE 2013 – 17-21 June, Valencia (Spain)
1) Simulation
2) Time prediction
3) Recommendations
4) Generation BP models
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Applications
CAiSE 2013 – 17-21 June, Valencia (Spain)
1) Simulation
2) Time prediction3) Recommendations4) Generation BP models
What-if scenarios (reduce resources change estimates, etc.)
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Applications
CAiSE 2013 – 17-21 June, Valencia (Spain)
1) Simulation
2) Time prediction3) Recommendations4) Generation BP models
What-if scenarios (reduce resources change estimates, etc.)
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Applications
CAiSE 2013 – 17-21 June, Valencia (Spain)
1) Simulation2) Time prediction
3) Recommendations4) Generation BP models
Predicting the completion time of the running instances
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Applications
CAiSE 2013 – 17-21 June, Valencia (Spain)
1) Simulation2) Time prediction
3) Recommendations4) Generation BP models
Predicting the completion time of the running instances
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Applications
CAiSE 2013 – 17-21 June, Valencia (Spain)
1) Simulation2) Time prediction3) Recommendations
4) Generation BP models
Partial traces
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Applications
CAiSE 2013 – 17-21 June, Valencia (Spain)
1) Simulation2) Time prediction3) Recommendations
4) Generation BP models
Partial traces
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Applications
CAiSE 2013 – 17-21 June, Valencia (Spain)
1) Simulation2) Time prediction3) Recommendations4) Generation BP models
Convert enactment plans to BP models in standard BPMN
A B C0..2 1
R14
R23
R11
A C
+
B1
B2
R1
R2
Plan
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Applications