Volume 15, Issue 2 (9-2018)                   JSDP 2018, 15(2): 103-118 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Yaghoubi M, Zahedi M, Ahmadyfard A. A Dynamic Programing Algorithm for Tuning Concurrency of Business Processes. JSDP. 2018; 15 (2) :103-118
URL: http://jsdp.rcisp.ac.ir/article-1-623-en.html
Shahrood university of technology
Abstract:   (922 Views)

Business process management systems (BPMS) are vital complex information systems to compete in the global market and to increase economic productivity. Workload balancing of resources in BPMS is one of the challenges have been long studied by researchers. Workload balancing of resources increases the system stability, improves the efficiency of the resources and enhances the quality of their products. Workload balancing of resources in BPMS is considered as an important factor of the performance and the stability in systems. Setting the workload of each source at a certain level increases the efficiency of the resources.
The main objectives of this research are the concept of resource workload balance and uniformity of the workload for each source at a specified level. To optimize the balance workload and uniformity of each source, the ​​setting multi-process concurrency was offered and studied. Also, the regulation of multi-process concurrency was mentioned as an optimization problem. In this paper, tuning concurrency of the business process is introduced as a problem in BPMS, which is an application issue to improve at workload balance of resources and uniformity in the workload of each resource.
To solve this problem, a delay vector is defined, each element of delay vector makes the synthetic delay at the first of each business process, then a dynamic optimization algorithm is presented to compute delay vector and the speed of the proposed algorithms is compared with and state-space search algorithm and evolutionary algorithm of PSO. The comparison shows that the speed of the proposed algorithm is 37 hours to 5.8 years compared to the state-space search algorithm, while the POS algorithm solves the same problem in just 3 minutes. The experimental results on a real dataset show 21.64 percent improvement in the performance of the proposed algorithm.

Full-Text [PDF 4669 kb]   (321 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2016/12/19 | Accepted: 2017/08/20 | Published: 2018/09/16 | ePublished: 2018/09/16

1. [1] M. Hammer, The agenda: What every business must do to dominate the decade: Crown Pub, 2003.
2. [2] M. Hammer and J. Champy, Reengineering the Corporation: Manifesto for Business Revolution, A: Zondervan, 2009.
3. [3] H. Smith and P. Fingar, Business process management: the third wave vol. 1: Meghan-Kiffer Press Tampa, 2003.
4. [4] W. M. Van Der Aalst, A. H. Ter Hofstede, and M. Weske, "Business process management: A survey," in International conference on business process management, 2003, pp. 1-12. [DOI:10.1007/3-540-44895-0_1]
5. [5] B.-H. Ha, J. Bae, and S.-H. Kang, "Workload balancing on agents for business process efficiency based on stochastic model," in Business Process Management, ed: Springer, 2004, pp. 195-210. [PMID]
6. [6] B.-H. Ha, J. Bae, Y. T. Park, and S.-H. Kang, "Development of process execution rules for workload balancing on agents," Data & Knowledge Engineering, vol. 56, pp. 64-84, 2006. [DOI:10.1016/j.datak.2005.02.007]
7. [7] Y. Xie, C.-F. Chien, and R.-Z. Tang, "A dynamic task assignment approach based on individual worklists for minimizing the cycle time of business processes," Computers & Industrial Engineering, vol. 99, pp. 401-414, September 2015. [DOI:10.1016/j.cie.2015.11.023]
8. [8] X. Liu, J. Chen, Y. Ji, and Y. Yu, "Q-learning Algorithm for Task Allocation Based on Social Relation," Process-Aware Systems, pp. 49-58, 2015.
9. [9] D. E. Culler, J. P. Singh, and A. Gupta, Parallel computer architecture: a hardware/software approach: Gulf Professional Publishing, 1999.
10. [10] D. Grosu and A. T. Chronopoulos, "Algorithmic mechanism design for load balancing in distributed systems," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 34, pp. 77-84, 2004. [DOI:10.1109/TSMCB.2002.805812] [PMID]
11. [11] E. Rahm, "Dynamic load balancing in parallel database systems," in European Conference on Parallel Processing, 1996, pp. 37-52.
12. [12] L.-j. Jin, F. Casati, M. Sayal, and M.-C. Shan, "Load balancing in distributed workflow management system," in Proceedings of the 2001 ACM symposium on Applied computing, 2001, pp. 522-530. [DOI:10.1145/372202.372452]
13. [13] W. Zhao, L. Yang, H. Liu, and R. Wu, "The Optimization of Resource Allocation Based on Process Mining," in Advanced Intelligent Computing Theories and Applications, ed: Springer, 2015, pp. 341-353.
14. [14] M. Zur Muehlen, "Organizational management in workflow applications–issues and perspectives," Information Technology and Management, vol. 5, pp. 271-291, 2004. [DOI:10.1023/B:ITEM.0000031582.55219.2b]
15. [15] J. Xu, C. Liu, and X. Zhao, "Resource allocation vs. business process improvement: How they impact on each other," in BPM, 2008, pp. 228-243.
16. [16] A. S. Nisafani, A. Wibisono, S. Kim, and H. Bae, "Bayesian Selection Rule for Human-Resource Selection in Business Process Management Systems," Journal of Society for e-Business Studies, vol. 17, 2014.
17. [17] A. Wibisono, A. S. Nisafani, H. Bae, and Y.-J. Park, "On-the-Fly Performance-Aware Human Resource Allocation in the Business Process Management Systems Environment Using Naïve Bayes," in Asia Pacific Business Process Management, ed: Springer, 2015, pp. 70-80.
18. [18] S. Rhee, H. Bae, D. Ahn, and Y. Seo, "Efficient workflow management through the introduction of TOC concepts," in Proceedings of the 8th annual international conference on industrial engineering theory, applications and practice (IJIE2003), 2003.
19. [19] M. Shen, G.-H. Tzeng, and D.-R. Liu, "Multi-criteria task assignment in workflow management systems," in System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference on, 2003.
20. [20] K. Georgoulakos, K. Vergidis, G. Tsakalidis, and N. Samaras, "Evolutionary Multi-Objective Optimization of business process designs with pre-processing," in Evolutionary Computation (CEC), 2017 IEEE Congress on, 2017, pp. 897-904.
21. [21] M. Wibig, "Dynamic Programming and Genetic Algorithm for Business Processes Optimisation," International Journal of Intelligent Systems and Applications, vol. 5, p. 44, 2012. [DOI:10.5815/ijisa.2013.01.04]
22. [22] M. Wibig and C. Polska, "NSGA II algorithm application within the dynamic programming approach to business process optimisation," Journal of Applied Computer Science, vol. 21, pp. 195-207, 2013.
23. [23] E. A. Alluisi and B. B. Morgan Jr, "Engineering psychology and human performance," Annual review of psychology, vol. 27, pp. 305-330, 1976. [DOI:10.1146/annurev.ps.27.020176.001513]
24. [24] S. Dreyfus, "Richard Bellman on the birth of dynamic programming," Operations Research, vol. 50, pp. 48-51, 2002. [DOI:10.1287/opre.]
25. [25] P. Diban, M. K. A. Aziz, D. C. Foo, X. Jia, Z. Li, and R. R. Tan, "Optimal biomass plantation replanting policy using dynamic programming," Journal of Cleaner Production, vol. 126, pp. 409-418, 2016. [DOI:10.1016/j.jclepro.2016.03.097]
26. [26] R. Jia, S. J. Mellon, S. Hansjee, A. Monk, D. Murray, and J. A. Noble, "Automatic bone segmentation in ultrasound images using local phase features and dynamic programming," in Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, 2016, pp. 1005-1008. [PMID] [PMCID]
27. [27] M. Roozegar, M. Mahjoob, and M. Jahromi, "Optimal motion planning and control of a nonholonomic spherical robot using dynamic programming approach: simulation and experimental results," Mechatronics, 2016. [DOI:10.1016/j.mechatronics.2016.05.002]
28. [28] C. Finck and R. Li, "Operational load shaping of office buildings connected to thermal energy storage using dynamic programming," 2016.
29. [29] M.Fatehi Hassan Abaad, H.Ghanee and A.M. Latif, "A novel method for suitable selection of watermark strength in digital image watermarking based on imperialist competitive algorithm" JSDP, vol. 10 no. 1, pp. 56-43, 2013.
30. [30] J. Kennedy, "Particle swarm optimization," in Encyclopedia of machine learning, ed: Springer, 2011, pp. 760-766.
31. [31] Z. Liu, P. Zhu, W. Chen, and R.-J. Yang, "Improved particle swarm optimization algorithm using design of experiment and data mining techniques," Structural and Multidisciplinary Optimization, vol. 52, pp. 813-826, 2015. [DOI:10.1007/s00158-015-1271-7]
32. [32] C. Ou-Yang, H.-J. Cheng, and Y.-C. Juan, "An Integrated mining approach to discover business process models with parallel structures: towards fitness improvement," International Journal of Production Research, vol. 53, pp. 3888-3916, 2015. [DOI:10.1080/00207543.2014.974847]
33. [33] H.-J. Cheng, C. Ou-Yang, and Y.-C. Juan, "A hybrid approach to extract business process models with high fitness and precision," Journal of Industrial and Production Engineering, vol. 32, pp. 351-359, 2015. [DOI:10.1080/21681015.2015.1065519]
34. [34] F. Ahmadizar, Kh.B. Soltanian and F. Akhlaghian, " Construction and Training of Artificial Neural Networks using Evolution Strategy with Parallel Populations", JSDP. vol. 13, no. 1, pp. 101-114, 2016.
35. [35] W. M. van der Aalst and A. H. Ter Hofstede, "YAWL: yet another workflow language," Information systems, vol. 30, pp. 245-275, 2005. [DOI:10.1016/j.is.2004.02.002]

Add your comments about this article : Your username or Email:

Send email to the article author

© 2015 All Rights Reserved | Signal and Data Processing