Concurrency optimization of business processes to balance workload

Document Type : Computer Article

Authors

uni

Abstract

Today, business process management systems (BPMS) are rapidly expanding. Organizations and corporations need to leverage BPMS to manage their business processes and to increase economic productivity for competing in the international market. Therefore, researchers are to increase the features of BPMS. Workload balancing in BPMS is one of the challenges which have been studied by researchers. The purposes of workload balancing include increasing system stability, improving the resource efficiency and enhancing the quality of its products. In this paper, undesirable effects of the time delay of arrival rate in the tasks are presented. To overcome the undesirable effects, we define the workload pattern of resources and we propose a heuristic method to manage and regulate the processes in the BPMS as Process Concurrency. we leverage PSO algorithm to optimize process concurrency. Therefore, in addition to balancing the workload of resources, workload for each resource is maintained uniform through time. Creating uniformity in workload, as a result, increase the resource efficiency and improve the quality of products and services. To evaluate the proposed method, experimental results showed the optimal process Concurrency 29.8% increase balance and uniformity in the resource workload.

Keywords

Main Subjects


[1]    M. Hammer. (2003), The agenda: What every business must do to dominate the decade, Crown Publisher
[2]    M. Hammer and J. Champy. (2009), Reengineering the Corporation: Manifesto for Business Revolution, A, Zondervan Publisher
[3]    H. Smith and P. Fingar. (2003), Business process management: the third wave, Meghan-Kiffer Press Tampa. vol. 1.
[4]    W. M. Van Der Aalst, A. H. Ter Hofstede and M. Weske. (2003), "Business process management: A survey" International conference on business process management. (Springer).
[5]    J. Xu, C. Liu and X. Zhao. (2008), Resource allocation vs. business process improvement: How they impact on each other, BPM. (Springer)
[6]    Z. Huang, X. Lu and H. Duan. (2012), A task operation model for resource allocation optimization in business process management, Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, vol. 42, no. 5, pp. 1256-1270.
[7]    Z. Huang, W. M. van der Aalst, X. Lu and H. Duan. (2011), Reinforcement learning based resource allocation in business process management, Data & Knowledge Engineering, vol. 70, no. 1, pp. 127-145.
[8]    J. Wang and A. Kumar. (2005), "A framework for document-driven workflow systems", International Conference on Business Process Management, (Springer), pp. 285-301.
[9]    B.-H. Ha, J. Bae, Y. T. Park and S.-H. Kang. (2006), Development of process execution rules for workload balancing on agents, Data & Knowledge Engineering, vol. 56, no. 1, pp. 64-84.
[10] A. Wibisono, A. S. Nisafani, H. Bae and Y.-J. Park. (2015), On-the-Fly Performance-Aware Human Resource Allocation in the Business Process Management Systems Environment Using Naïve Bayes, Asia Pacific Business Process Management, Springer. pp. 70-80.
[11] Y. Xie, C.-F. Chien and R.-Z. Tang. (2015), 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.
[12] D. E. Culler, J. P. Singh and A. Gupta. (1999), Parallel computer architecture: a hardware/software approach, Gulf Professional Publishing.
[13] D. Grosu and A. T. Chronopoulos. (2004), Algorithmic mechanism design for load balancing in distributed systems, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 34, no. 1, pp. 77-84.
[14] E. Rahm. (1996), "Dynamic load balancing in parallel database systems", European Conference on Parallel Processing. (Springer).
[15] L.-j. Jin, F. Casati, M. Sayal and M.-C. Shan. (2001), Load balancing in distributed workflow management system, Proceedings of the 2001 ACM symposium on Applied computing. (ACM).
[16] W. Zhao, L. Yang, H. Liu and R. Wu. (2015), "The Optimization of Resource Allocation Based on Process Mining",  International Conference on Intelligent Computing, (Springer), pp. 341-353.
[17] M. Zur Muehlen. (2004), Organizational management in workflow applications–issues and perspectives, Information Technology and Management, vol. 5, no. 3-4, pp. 271-291.
[18] B.-H. Ha, J. Bae and S.-H. Kang. (2004), "Workload balancing on agents for business process efficiency based on stochastic model", International Conference on Business Process Management, (Springer). pp. 195-210.
[19] A. S. Nisafani, A. Wibisono, S. Kim and H. Bae. (2012), Bayesian Selection Rule for Human-Resource Selection in Business Process Management Systems, Journal of Society for e-Business Studies, vol. 17, no. 1. pp. 53-74.
[20] E. G. Coffman and J. L. Bruno. (1976), Computer and job-shop scheduling theory, John Wiley & Sons.
[21] K. R. Baker. (1974), Introduction to sequencing and scheduling, John Wiley & Sons.
[22] M. Pinedo. (1995), Scheduling: theory, algorithms and systems, Prentice-Hall, Englewood Cliffs, NJ.
[23] S. Rhee, H. Bae, D. Ahn and Y. Seo. (2003), "Efficient workflow management through the introduction of TOC concepts" Proceedings of the 8th annual international conference on industrial engineering theory, applications and practice (IJIE2003).
[24] M. Shen, G.-H. Tzeng and D.-R. Liu. (2003), "Multi-criteria task assignment in workflow management systems", System Sciences, Proceedings of the 36th Annual Hawaii International Conference (IEEE).
[25] Y. Liu and K. Zhang. (2009), Strategy for Workflow Task Assignment Based on Load Balance and Experiential Value [J], Computer Engineering, vol. 21, no. 1  pp. 1-22.
[26] S. Larbi and S. Mohamed. (2014), Modeling the Scheduling Problem of Identical Parallel Machines with Load Balancing by Time Petri Nets, International Journal of Intelligent Systems and Applications, vol. 7, no. 1, pp. 42.
[27] X. Liu, J. Chen, Y. Ji and Y. Yu. (2015), "Q-learning Algorithm for Task Allocation Based on Social Relation", International Workshop on Process-Aware Systems, pp. 49-58.
[28] A. Kumar, W. M. van der Aalst and E. M. Verbeek. (2002), Dynamic work distribution in workflow management systems: How to balance quality and performance, Journal of Management Information Systems, vol. 18, no. 3, pp. 157-193.
[29] J. Eder, H. Pichler, W. Gruber and M. Ninaus. (2003), "Personal schedules for workflow systems", International Conference on Business Process Management. (Springer).
[30] J. H. Son and M. H. Kim. (2001), Improving the performance of time-constrained workflow processing, Journal of Systems and Software, vol. 58, no. 3, pp. 211-219.
[31] D.-H. Chang, J. H. Son and M. H. Kim. (2002), Critical path identification in the context of a workflow, Information and software Technology, vol. 44, no. 7, pp. 405-417.
[32] W. M. Van Der Aalst, K. M. Van Hee and H. A. Reijers. (2000), Analysis of discrete‐time stochastic petri nets, Statistica Neerlandica, vol. 54, no. 2, pp. 237-255.
[33] K. van Hee and H. Reijers. (1999), An analytical method for computing throughput times in stochastic workflow nets, Simulation in industry, vol. 10, no. 3.
[34] L. Zerguini and K. M. van Hee. (2002), A new reduction method for the analysis of large workflow models, Promise, Citeseer, pp. 188-201.
[35] E. A. Alluisi and B. B. Morgan Jr. (1976), Engineering psychology and human performance, Annual review of psychology, vol. 27, no. 1, pp. 305-330.
[36] J. Kennedy. (2011), Particle swarm optimization, Encyclopedia of machine learning, (Springer), pp. 760-766.
[37] S. Intelligence. (2007), Particle swarm optimization, MCCAFFREY, James. [online].[cit. 2014-05-20]. Dostupné z: http://msdn. microsoft. com/en-us/magazine/hh335067.aspx.
[38] Z. Liu, P. Zhu, W. Chen and R.-J. Yang. (2015), Improved particle swarm optimization algorithm using design of experiment and data mining techniques, Structural and Multidisciplinary Optimization, vol. 52, no. 4, pp. 813-826.
[39] C. Ou-Yang, H.-J. Cheng and Y.-C. Juan. (2015), An Integrated mining approach to discover business process models with parallel structures: towards fitness improvement, International Journal of Production Research, vol. 53, no. 13, pp. 3888-3916.
[40] H.-J. Cheng, C. Ou-Yang and Y.-C. Juan. (2015), A hybrid approach to extract business process models with high fitness and precision, Journal of Industrial and Production Engineering, vol. 32, no. 6, pp. 351-359.