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


Designing the model

Ferreira, H.M. et al. (2006)

Karim, A. et al. (2013)


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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Just say what, and

let the AI tell you

how.

Our proposal

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Just say what, and

let the AI tell you

how.

Our proposal

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Just say what, and

let the AI tell you

how.

Our proposal

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Recommendations

Just say what, and

let the AI tell you

how.

Our proposal

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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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Services


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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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



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


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profit

durationR1A

0..2

4

1

3

2R1C

1

1

1

R2B



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


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profit

durationR1A

0..2

4

1

3

2R1C

1

1

1

R2B



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




Minimize total timeMaximize total profit


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profit

durationR1A

0..2

4

1

3

2R1C

1

1

1

R2B



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





Minimize total timeMaximize total profit


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profit

durationR1A

0..2

4

1

3

2R1C

1

1

1

R2B


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


4. Constraint Satisfaction Problem

Solve a Constraint Satisfaction / (CSP/COP)

Generate an Enactment Plan Optimization Problem


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


OF2

OF1

4. Multi-objective approach

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OF2

OF1


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Ɛ-constraint method


OF2

OF1


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Ɛ-constraint method


OF2

OF1

Pareto Front solutions


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Low work load

High work load


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

[email protected]


Applications

1) Simulation

2) Time prediction

3) Recommendations

4) Generation BP models

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1) Simulation

2) Time prediction

3) Recommendations


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Applications


1) Simulation

2) Time prediction3) Recommendations4) Generation BP models

What-if scenarios (reduce resources change estimates, etc.)

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Applications


1) Simulation

2) Time prediction3) Recommendations4) Generation BP models

What-if scenarios (reduce resources change estimates, etc.)

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Applications


1) Simulation2) Time prediction

3) Recommendations4) Generation BP models

Predicting the completion time of the running instances

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Applications


1) Simulation2) Time prediction

3) Recommendations4) Generation BP models

Predicting the completion time of the running instances

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Applications


1) Simulation2) Time prediction3) Recommendations


Partial traces

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Applications


1) Simulation2) Time prediction3) Recommendations


Partial traces

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Applications


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

andres jimenez c ai-se13 presentation

Business

c b b b b b b plan

c r2 b b b b b b plan

c r2 b b b t

profit duration r2 b

r1 r2 plan

r1 c total time

profit duration r1

total profit