Integration of resource allocation and task assignment for optimizing the cost and maximum throughput of business processes
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Abstract
To improve efficiency and keep an edge in today’s increasingly competitive global business environments, this study aims to integrate resource allocation and task assignment for optimizing the cost and maximum throughput of business processes with many-to-many relationships between resources and activities using numerical analysis approaches and improved genetic algorithm. Firstly, a formal business process model for analyzing cost and maximum throughput is presented based on set theory. Secondly, the mathematic models of integrating resources allocation and task assignment for optimizing the cost and maximum throughput of business process are proposed respectively and solved by the improved genetic algorithm. Finally, the effectiveness and viability of the proposed methods are verified in numerical and practical cases respectively.
Keywords
Business process Cost Maximum throughput Resource allocation Task assignmentNotes
Acknowledgements
This work is supported by Ministry of Education of the People’s Republic of China under Grant No. 11YJA630161; and Zhejiang Provincial Natural Science Foundation of China under Grant No. LY17G010001. This work is also sponsored by Contemporary Business and Trade Research Center of Zhejiang Gongshang University, Collaborative Innovation Center for Contemporary Commerce Circulation System Construction, Ministry of Education’s Key Research Institute in University & 2011 Collaborative Innovation Center of Zhejiang Province.
References
- Afzalirad, M., & Shafipour, M. (2015). Design of an efficient genetic algorithm for resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-015-1117-6.
- Alotaibi, Y. (2016). Business process modelling challenges and solutions: A literature review. Journal of Intelligent Manufacturing, 27(4), 701–723.CrossRefGoogle Scholar
- Bae, H., Lee, S., & Moon, I. (2014). Planning of business process execution in business process management environments. Information Sciences, 268, 357–369.CrossRefGoogle Scholar
- Bhoskar, M. T., Kulkarni, M. O. K., Kulkarni, M. N. K., Patekar, M. S. L., Kakandikar, G. M., & Nandedkar, V. M. (2015). Genetic algorithm and its applications to mechanical engineering: A review. Materials Today: Proceedings, 2(4–5), 2624–2630.Google Scholar
- Cámara, J., Canal, C., Cubo, J., & Vallecillo, A. (2006). Formalizing WSBPEL business processes using process algebra. Electronic Notes in Theoretical Computer Science, 154(1), 159–173.CrossRefGoogle Scholar
- Casas, I., Taheri, J., Ranjan, R., Wang, L., & Zomaya, A. Y. (2016). GA-ETI: An enhanced genetic algorithm for the scheduling of scientific workflows in cloud environments. Journal of Computational Science. doi: 10.1016/j.jocs.2016.08.007.
- Chang, D. H., Son, J. H., & Kim, M. H. (2002). Critical path identification in the context of a workflow. Information and Software Technology, 44(7), 405–417.CrossRefGoogle Scholar
- Deng, T. N., Yi, Y., Chang, H. Y., Xiao, Z. J., & Inoue, A. (2006). Model and intelligent algorithm for workflow resource optimization to minimize total flow time. In Proceedings of the fifth international conference on machine learning and cybernetics (pp. 3557–3562). Dalian: IEEE.Google Scholar
- Dong, M., & Chen, F. F. (2005). Petri net-based workflow modelling and analysis of the integrated manufacturing business processes. The International Journal of Advanced Manufacturing Technology, 26(9–10), 1163–1172.CrossRefGoogle Scholar
- Gao, X., & Li, Z. (2006). Business process modelling and analysis using UML and polychromatic sets. Production Planning and Control: The Management of Operations, 17(8), 780–791.CrossRefGoogle Scholar
- Gao, X., Wang, X., Li, Y., Yang, M., Liu, Y., & Guo, W. (2016). Workflow dynamic change and instance migration approach based on polychromatic sets theory. International Journal of Computer Integrated Manufacturing, 29(4), 386–405.CrossRefGoogle Scholar
- Gao, X., Xu, L., Wang, X., Li, Y., Yang, M., & Liu, Y. (2013). Workflow process modelling and resource allocation based on polychromatic sets theory. Enterprise Information Systems, 7(2), 198–226.CrossRefGoogle Scholar
- Ha, B. H., Bae, J., Park, Y. T., & Kang, S. H. (2006a). Development of process execution rules for workload balancing on agents. Data & Knowledge Engineering, 56(1), 64–84.CrossRefGoogle Scholar
- Ha, B. H., Reijers, H. A., Bae, J., & Bae, H. (2006b). An approximate analysis of expected cycle time in business process execution. In International conference on business process management, BPM 2006 workshops (pp. 65–74). Berlin: Springer.Google Scholar
- Huang, Z., Lu, X., & Duan, H. (2011a). Mining association rules to support resource allocation in business process management. Expert Systems with Applications, 38(8), 9483–9490.CrossRefGoogle Scholar
- Huang, Z., van der Aalst, W. M. P., Lu, X., & Duan, H. (2011b). Reinforcement learning based resource allocation in business process management. Data & Knowledge Engineering, 70(1), 127–145.CrossRefGoogle Scholar
- Kamrani, F., Ayani, R., & Moradi, F. (2012). A framework for simulation-based optimization of business process models. Simulation: Transactions of the Society for Modeling and Simulation International, 88(7), 852–869.CrossRefGoogle Scholar
- Li, L. J., Gao, J. M., Chen, K., & Jiang, H. Q. (2011). The identification of irrationally allocated resources in business process based on network centrality analysis. International Journal of Computer Integrated Manufacturing, 24(8), 748–755.CrossRefGoogle Scholar
- Liu, T., Cheng, Y., & Ni, Z. (2012). Mining event logs to support workflow resource allocation. Knowledge-Based Systems, 35, 320–331.CrossRefGoogle Scholar
- Liu, S., Fan, Y. S., & Lin, H. P. (2009). Dwelling time probability density distribution of instances in a workflow model. Computer & Industrial Engineering, 57(3), 874–879.CrossRefGoogle Scholar
- Liu, S., Fan, Y. S., & Yin, C. W. (2005). Method of resources configuration optimization based on workflow model. Computer Integrated Manufacturing Systems, 11(9), 1272–1278.Google Scholar
- Montgomery, D. C. (2008). Design and analysis of experiments. London: Wiley.Google Scholar
- Oliveto, P. S., & Witt, C. (2015). Improved time complexity analysis of the simple genetic algorithm. Theoretical Computer Science, 605, 21–41.CrossRefGoogle Scholar
- Ou-Yang, C., & Lin, Y. D. (2008). BPMN-based business process model feasibility analysis: A Petri net approach. International Journal of Production Research, 46(14), 3763–3781.CrossRefGoogle Scholar
- Pla, A., Gay, P., Meléndez, J., & López, B. (2014). Petri net-based process monitoring: A workflow management system for process modelling and monitoring. Journal of Intelligent Manufacturing, 25(3), 539–554.CrossRefGoogle Scholar
- Reijers, H. A. (2003). Design and control of workflow processes: Business process management for the service industry. Berlin: Springer.CrossRefGoogle Scholar
- Ren, G. Q., Han, R., Liu, Y. B., Zhao, J., Jin, T., Zhang, L., et al. (2013). Applying genetic algorithm to optimise personal worklist management in workflow systems. International Journal of Production Research, 51(17), 5158–5179.CrossRefGoogle Scholar
- Ryan, J., & Heavey, C. (2006). Process modeling for simulation. Computers in Industry, 57(5), 437–450.CrossRefGoogle Scholar
- Son, J. H., & Kim, M. H. (2001). Improving the performance of time-constrained workflow processing. Journal of Systems and Software, 58(3), 211–219.CrossRefGoogle Scholar
- Son, J. H., Kim, J. S., & Kim, M. H. (2005). Extracting the workflow critical path form the extended well-formed workflow schema. Journal of Computer and System Sciences, 70(1), 86–106.CrossRefGoogle Scholar
- van der Aalst, W. M. P., & van Hee, K. M. (1996). Business process redesign: A Petri-net-based approach. Computers in Industry, 29(1), 15–26.CrossRefGoogle Scholar
- Wong, P. Y. H., & Gibbons, J. (2011). Formalisations and applications of BPMN. Science of Computer Programming, 76(8), 633–650.CrossRefGoogle Scholar
- Xie, Y., Chien, C. F., & Tang, R. Z. (2013). A method for estimating the cycle time of business processes with many-to-many relationships among the resources and activities based on individual worklists. Computers & Industrial Engineering, 65(2), 194–206.CrossRefGoogle Scholar
- Xie, Y., Chien, C. F., & Tang, R. Z. (2016). A dynamic task assignment approach based on individual worklists for minimizing the cycle time of business processes. Computers & Industrial Engineering, 99, 401–414.CrossRefGoogle Scholar
- Xiong, P. C., & Fan, Y. S. (2007). Optimization method of workflow resources allocation under cost constraints. Computer Integrated Manufacturing Systems, 13(9), 1833–1838.Google Scholar
- Xu, J., Liu, C., Zhao, X., & Ding, Z. (2013). Incorporating structural improvement into resource allocation for business process execution planning. Concurrency and Computation: Practice and Experience, 25(3), 427–442.CrossRefGoogle Scholar
- Yu, Y., Pan, M., Li, X., & Jiang, H. (2011). Tabu search heuristics for workflow resource allocation simulation optimization. Concurrency and Computation: Practice and Experience, 23(16), 2020–2033.CrossRefGoogle Scholar
- Zhao, W., Liu, H., Dai, W., & Ma, J. (2015). An entropy-based clustering ensemble method to support resource allocation in business process management. Knowledge and Information Systems. doi: 10.1007/s10115-015-0879-7.
- Zheng, H. Y., & Wang, L. (2015). Reduction of carbon emissions and project makespan by a Pareto-based estimation of distribution algorithm. International Journal of Production Economics, 164, 421–432.CrossRefGoogle Scholar
- Zomaya, A. Y., Ward, C., & Macey, B. (1999). Genetic scheduling for parallel processor systems: Comparative studies and performance issues. IEEE Transactions on Parallel and Distributed Systems, 10(8), 795–812.CrossRefGoogle Scholar