Abstract
Resource allocation in process management focuses on how to maximize process performance via proper resource allocation since the quality of resource allocation determines process outcome. In order to improve resource allocation, this paper proposes a resource allocation method, which is based on the improved hybrid particle swarm optimization (PSO) in the multi-process instance environment. Meanwhile, a new resource allocation model is put forward, which can optimize the resource allocation problem reasonably. Furthermore, some improvements are made to streamline the effectiveness of the method, so as to enhance resource scheduling results. In the end, experiments are conducted to demonstrate the effectiveness of the proposed method.





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References
Blackwell, T. (2007). Particle Swarm optimization in dynamic environments. Evolutionary computation in dynamic and uncertain environments (pp. 29–49). Berlin: Springer.
Chen C Y, Chuang C H, Wu M C. (2012). Combining concepts of inertia weights and constriction factors in particle swarm optimization. 2012 I.E. International conference on computational intelligence for measurement systems and applications, Tianjin, China, (pp. 73–76).
Cheng, K., Zhang, H., & Zhang, R. (2013). A task-resource allocation method based on effectiveness. Knowledge-Based Systems, 37(1), 196–202.
Delias, P., Doulamis, A., Doulamis, N., et al. (2010). Optimizing resource conflicts in workflow management systems. IEEE Transactions on Knowledge & Data Engineering, 23(3), 417–432.
Eberhart, R., & Shi, Y. (1998). Comparison between genetic algorithms and particle swarm optimization, Evolutionary programming VII (pp. 611–616). Berlin Heidelberg: Springer.
Gao Y, Li S. (2010). Improved particle swarm optimization algorithm. Proceedings of 2010 international conference on computational intelligence and software engineering (pp. 1–4), Springer-Verlag NewYork.
Huang, Z., Lu, X., & Duan, H. (2011). Mining association rules to support resource allocation in business process management. Expert Systems with Applications, 38(8), 9483–9490.
Huang, Z., Lu, X., & Duan, H. (2012). A task operation model for resource allocation optimization in business process management. IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans, 42(5), 1256–1270.
Jin, X. U., Fei, S. M., Zhang, S. Y., et al. (2011). Adaptive particle swarm optimization for the project scheduling problem with dynamic allocation of resource. Computer Integrated Manufacturing Systems, 17(8), 1790–1797.
Liu, Z., Zhu, P., Chen, W., et al. (2015). Improved particle swarm optimization algorithm using design of experiment and data mining techniques. Structural & Multidisciplinary Optimization, 524(4), 1–14.
Ly, L., Rinderle, S., Dadam, P., et al. (2006). Mining staff assignment rules from event-based data. Lecture Notes in Computer Science (pp. 177–190). Berlin: Springer.
MAO, K.-f., Guang-qing, B. A. O., & Chi, X. U. (2010). Particle swarm optimization algorithm based on non-symmetric learning factor adjusting. Computer Engineering, 36(19), 182–184.
Petkov, S., Oren, E., & Haller, A. (2005). Aspects in workflow management (pp. 167–178). Galway: National University of Ireland.
Pluhacek, M., Senkerik, R., Davendra, D., et al. (2013). On the behavior and performance of chaos driven PSO algorithm with inertia weight. Computers & Mathematics with Applications, 66(2), 122–134.
Salman, A., & Ahmad, I. (2002). Particle swarm optimization for task assignment problem. Microprocessor and Microsystems, 26(8), 363–371.
Senkul, P., & Toroslu, I. H. (2005). An architecture for workflow scheduling under resource allocation constraints. Information Systems, 30(5), 399–422.
Smanchat, S., Indrawan, M., Ling, S., et al. (2011). A scheduler based on resource competition for parameter sweep workflow. Procedia Computer Science, 4(4), 176–185.
Trelea, I. (2003). The particle swarm optimization algorithm. Information Processing Letters, 85(6), 317–325.
Van Der Aalst, W. M. P., Barthelmess, P., Ellis, C. A., et al. (2001). Proclets: A framework for lightweight interacting workflow processes. International Journal of Cooperative Information Systems, 10(4), 443–481.
Wang, W.-B., Feng, Q., & Liu, D. (2011). Application of chaotic particle swarm optimization algorithm to pattern synthesis of antenna arrays. Pier, 115(1), 173–189.
Wang, X. Y., Zhang, G. X., Zhao, J. B., et al. (2015). A modified membrane-inspired algorithm based on particle swarm optimization for mobile robot path planning. International Journal of Computers, Communications & Control, 10(5), 732–745.
Xu, J., Liu, C., & Zhao, X. (2008). Resource allocation vs. In Business process improvement: How they impact on each other. Procedings of Business Process Management, International Conference, Milan, Italy (pp. 228–243).
Yang H, Wang C, Liu Y, et al. (2008). An optimal approach for workflow staff assignment based on hidden Markov models: on the move to meaningful Internet systems. Proceedings of 2008 Workshops on OTM. Springer Berlin Heidelberg, pp 24–26
Zhang, Y., & Gong, D. W. (2013). Robot path planning in uncertain environment using multi-objective particle swarm optimization. Neurocomputing, 103, 172–185.
Zhao W. (2014). Smart Business Process Management. Shanghai: Fudan University Press. (pp. 10–15)
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Zhao, W., Zeng, Q., Zheng, G. et al. The resource allocation model for multi-process instances based on particle swarm optimization. Inf Syst Front 19, 1057–1066 (2017). https://doi.org/10.1007/s10796-017-9743-5
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DOI: https://doi.org/10.1007/s10796-017-9743-5