Andres jimenez c ai-se13 presentation

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Andres jimenez c ai-se13 presentation

  1. 1. 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 barbara.weber@uibk.ac.at
  2. 2. CAiSE 2013 – 17-21 June, Valencia (Spain) 2/33 System Configuration Process Enactment Evaluation Process Design & Analysis BPM lifecycle
  3. 3. CAiSE 2013 – 17-21 June, Valencia (Spain) 3/33 Designing the model Ferreira, H.M. et al. (2006) Karim, A. et al. (2013)
  4. 4. CAiSE 2013 – 17-21 June, Valencia (Spain) 4/33 Flexible design
  5. 5. CAiSE 2013 – 17-21 June, Valencia (Spain) A declarative language for modelling dynamic business processes 1) Tasks (smallest unit of work) 2) Relations (constraints, no order of execution) A B C 0..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 C C is executed once Declare (2006) Declarative languages Pesic, M. and van der Aalst, W.M.P. : (2006) 5/33
  6. 6. CAiSE 2013 – 17-21 June, Valencia (Spain) Just say what, and let the AI tell you how. Our proposal 6/33
  7. 7. CAiSE 2013 – 17-21 June, Valencia (Spain) Just say what, and let the AI tell you how. Our proposal 7/33
  8. 8. CAiSE 2013 – 17-21 June, Valencia (Spain) Just say what, and let the AI tell you how. Our proposal 8/33
  9. 9. CAiSE 2013 – 17-21 June, Valencia (Spain) Recommendations Just say what, and let the AI tell you how. Our proposal 9/33
  10. 10. 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 10/33
  11. 11. CAiSE 2013 – 17-21 June, Valencia (Spain) 2. Declare-R an extension of Declare Estimates + Resources + Multiple Instances + Data + Temporal (0, 10) Client Data (client) {clientName, bookedServic es, appointmentTime} this.startTime ≥ client.appointmentTime 20 Different activity attributes 11/33
  12. 12. CAiSE 2013 – 17-21 June, Valencia (Spain) 12/33 2. Declare-R an extension of Declare Services
  13. 13. CAiSE 2013 – 17-21 June, Valencia (Spain) 13/33 2. Declare-R an extension of Declare
  14. 14. CAiSE 2013 – 17-21 June, Valencia (Spain) 3. Enactment Plans how is it executed R1 A 0..2 4 1 3 2 R1 C 1 1 1 Res. Availability #R1: 1 #R2: 2 profit duration R2 B 14/33
  15. 15. 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 15/33 profit duration R1 A 0..2 4 1 3 2 R1 C 1 1 1 R2 B
  16. 16. 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 16/33 profit duration R1 A 0..2 4 1 3 2 R1 C 1 1 1 R2 B
  17. 17. 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 17/33 profit duration R1 A 0..2 4 1 3 2 R1 C 1 1 1 R2 B
  18. 18. 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: 5 Total profit: 4 Total time: 7 Total profit: 6 Total time: 5 Total profit: 6 Total time: 1 Total profit: 1 Minimize total time Maximize total profit Res. Availability #R1: 1 #R2: 2 18/33 profit duration R1 A 0..2 4 1 3 2 R1 C 1 1 1 R2 B
  19. 19. 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: 5 Total profit: 4 Total time: 7 Total profit: 6 Total time: 5 Total profit: 6 Total time: 1 Total profit: 1 Minimize total time Maximize total profit Res. Availability #R1: 1 #R2: 2 19/33 profit duration R1 A 0..2 4 1 3 2 R1 C 1 1 1 R2 B
  20. 20. 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. 20/33 The solutions of a CSP are all the possible combinations of values of the variables which satisfy the constraints. search algorithm
  21. 21. 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 21/33 R1 A 0..2 1 4 2 3 R1 C 1 1 1 R2 B Start time
  22. 22. CAiSE 2013 – 17-21 June, Valencia (Spain) OF2 OF1 4. Multi-objective approach 22/33
  23. 23. CAiSE 2013 – 17-21 June, Valencia (Spain) OF2 OF1 4. Multi-objective approach 23/33 Ɛ-constraint method
  24. 24. CAiSE 2013 – 17-21 June, Valencia (Spain) OF2 OF1 4. Multi-objective approach 24/33 Ɛ-constraint method
  25. 25. CAiSE 2013 – 17-21 June, Valencia (Spain) OF2 OF1 Pareto Front solutions 4. Multi-objective approach 25/33
  26. 26. CAiSE 2013 – 17-21 June, Valencia (Spain) 26/33 Low work load High work load 4. Multi-objective approach Number of clients Waiting Time or Profit 15 minutes of waiting time!
  27. 27. Future Work - Robustness t = 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 time Same total profit - Stochastic attributes R1 C [1..5] 1 27/33
  28. 28. Thank you Any question? 21st International Conference on Information Systems Development 2012 Andrés Jiménez Ramírez Departamento de Lenguajes y Sistemas Informáticos. University of Seville, Spain ajramirez@us.es
  29. 29. CAiSE 2013 – 17-21 June, Valencia (Spain) Applications 1) Simulation 2) Time prediction 3) Recommendations 4) Generation BP models 29/33
  30. 30. CAiSE 2013 – 17-21 June, Valencia (Spain) 1) Simulation 2) Time prediction 3) Recommendations 4) Generation BP models 30/33 Applications
  31. 31. CAiSE 2013 – 17-21 June, Valencia (Spain) 1) Simulation 2) Time prediction 3) Recommendations 4) Generation BP models What-if scenarios (reduce resources change estimates, etc.) 31/33 Applications
  32. 32. CAiSE 2013 – 17-21 June, Valencia (Spain) 1) Simulation 2) Time prediction 3) Recommendations 4) Generation BP models What-if scenarios (reduce resources change estimates, etc.) 32/33 Applications
  33. 33. CAiSE 2013 – 17-21 June, Valencia (Spain) 1) Simulation 2) Time prediction 3) Recommendations 4) Generation BP models Predicting the completion time of the running instances 33/33 Applications
  34. 34. CAiSE 2013 – 17-21 June, Valencia (Spain) 1) Simulation 2) Time prediction 3) Recommendations 4) Generation BP models Predicting the completion time of the running instances 34/33 Applications
  35. 35. CAiSE 2013 – 17-21 June, Valencia (Spain) 1) Simulation 2) Time prediction 3) Recommendations 4) Generation BP models Partial traces 35/33 Applications
  36. 36. CAiSE 2013 – 17-21 June, Valencia (Spain) 1) Simulation 2) Time prediction 3) Recommendations 4) Generation BP models Partial traces 36/33 Applications
  37. 37. CAiSE 2013 – 17-21 June, Valencia (Spain) 1) Simulation 2) Time prediction 3) Recommendations 4) Generation BP models Convert enactment plans to BP models in standard BPMN A B C 0..2 1 R1 4 R2 3 R1 1 A C + B1 B2 R 1 R 2 Plan 37/33 Applications

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