Advertisement

Methods for Optimal Resource Allocation on Cooperative Task Scheduling in Cross-Organizational Business Process

  • Wenan TanEmail author
  • Lu Zhao
  • Na Xie
  • Anqiong Tang
  • Xiaoming Hu
  • Shan Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11280)

Abstract

The optimal resource allocation (ORA) strategy for cooperative task scheduling is very important to form an efficient execution team to complete an instance in cross-organizational business processes (COBPs). In team formation, members of a team refer to the performers with specific skills and knowledge, and accomplish various tasks by cooperation and collaboration of corresponding resource roles. The team as a whole should focus on the overall comprehensive ability, which includes professional ability (PA) of members and cooperative ability (CA) between them, instead of individual combat. To address the resource allocation issue of COBPs for social networking cooperation, this paper proposes an ORA model for cooperative task scheduling based on the PA of performer who is qualified to complete task and the CA between performers whose roles require cooperation. In the proposed model, the tabu search (TS) algorithm is utilized to address the objective function solution, which outputs the optimal solutions mapping on resource allocation strategies. Finally, experiments show that the proposed optimization model for resource allocation supporting cooperative task scheduling is more in line with modern enterprise resource management models and it provides a new way for resource allocation during the cooperative task scheduling in COBPs.

Keywords

Cross-organizational business process Resource allocation Cooperative task scheduling Tabu search algorithm Interaction and cooperation 

References

  1. 1.
    Alotaibi, Y., Liu, F.: Survey of business process management: challenges and solutions. Enterp. Inf. Syst. 11(8), 1119–1153 (2017).  https://doi.org/10.1080/17517575.2016.1161238CrossRefGoogle Scholar
  2. 2.
    Tan, W., Xu, W., Yang, F., et al.: A framework for service enterprise workflow simulation with multi-agents cooperation. Enterp. Inf. Syst. 7(4), 523–542 (2013).  https://doi.org/10.1080/17517575.2012.660503CrossRefGoogle Scholar
  3. 3.
    Gadiraju, U., Demartini, G., Kawase, R., et al.: Human beyond the machine: Challenges and opportunities of microtask crowdsourcing. IEEE Intell. Syst. 30(4), 81–85 (2015). http://doi.ieeecomputersociety.org/10.1109/MIS.2015.66CrossRefGoogle Scholar
  4. 4.
    Schall, D., Satzger, B., Psaier, H.: Crowdsourcing tasks to social networks in BPEL4People. World Wide Web 17(1), 1–32 (2014).  https://doi.org/10.1007/s11280-012-0180-6CrossRefGoogle Scholar
  5. 5.
    Yin, H., Cui, B., Huang, Y.: Finding a wise group of experts in social networks. In: Tang, J., King, I., Chen, L., Wang, J. (eds.) ADMA 2011. LNCS (LNAI), vol. 7120, pp. 381–394. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-25853-4_29CrossRefGoogle Scholar
  6. 6.
    Juang, M.C., Huang, C.C., Huang, J.L.: Efficient algorithms for team formation with a leader in social networks. J. Supercomput. 66(2), 721–737 (2013).  https://doi.org/10.1007/s11227-013-0907-xCrossRefGoogle Scholar
  7. 7.
    Reijers, H.A., Jansen-Vullers, M.H., zur Muehlen, M., Appl, W.: Workflow management systems + swarm intelligence = dynamic task assignment for emergency management applications. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 125–140. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-75183-0_10CrossRefGoogle Scholar
  8. 8.
    Kumar, A., Dijkman, R., Song, M.: Optimal resource assignment in workflows for maximizing cooperation. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 235–250. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40176-3_20CrossRefGoogle Scholar
  9. 9.
    Kittur, A., Nickerson, J.V., Bernstein, M., et al.: The future of crowd work. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work, pp. 1301–1318. ACM (2013).  https://doi.org/10.1145/2441776.2441923
  10. 10.
    Anagnostopoulos, A., Becchetti, L., Castillo, C., et al.: Online team formation in social networks. In: Proceedings of the 21st International Conference on World Wide Web, pp. 839-848. ACM (2012).  https://doi.org/10.1145/2187836.2187950
  11. 11.
    Lappas, T., Liu, K., Terzi, E.: Finding a team of experts in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 467–476. ACM (2009).  https://doi.org/10.1145/1557019.1557074
  12. 12.
    Xu, J., Huang, Z., Yu, Y., et al.: A performance analysis on task allocation using social context. In: Proceedings of the 2012 Second International Conference on Cloud and Green Computing, pp. 637–644. IEEE (2012).  https://doi.org/10.1109/CGC.2012.88
  13. 13.
    Bajaj, A., Russell, R.: AWSM: allocation of workflows utilizing social network metrics. Decis. Support Syst. 50(1), 191–202 (2010).  https://doi.org/10.1016/j.dss.2010.07.014CrossRefGoogle Scholar
  14. 14.
    Wang, X., Zhao, Z., Ng, W.: A comparative study of team formation in social networks. In: Renz, M., Shahabi, C., Zhou, X., Cheema, M.A. (eds.) DASFAA 2015. LNCS, vol. 9049, pp. 389–404. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-18120-2_23CrossRefGoogle Scholar
  15. 15.
    Basiri, J., Taghiyareh, F., Ghorbani, A.: Collaborative team formation using brain drain optimization: a practical and effective solution. World Wide Web 20(6), 1385–1407 (2017).  https://doi.org/10.1007/s11280-017-0440-6CrossRefGoogle Scholar
  16. 16.
    Cross, R., Parker, A., Prusak, L., et al.: Knowing what we know: supporting knowledge creation and sharing in social networks. Organ. Dyn. 30(2), 100–120 (2001).  https://doi.org/10.1016/S0090-2616(01)00046-8CrossRefGoogle Scholar
  17. 17.
    Wi, H., Oh, S., Mun, J., et al.: A team formation model based on knowledge and collaboration. Expert Syst. Appl. 36(5), 9121–9134 (2009).  https://doi.org/10.1016/j.eswa.2008.12.031CrossRefGoogle Scholar
  18. 18.
    Zhao, L., Tan, W., Fang, X.: Role identification to discover potential opportunity information in business process. In: Proceedings of the 14th International Conference on e-Business Engineering (ICEBE), pp. 70–75. IEEE (2017). http://doi.ieeecomputersociety.org/10.1109/ICEBE.2017.20
  19. 19.
    Tan, W., Zhang, Q., Sun, Y.: Proactive scheduling optimization of emergency rescue based on hybrid genetic-tabu optimization algorithm. In: Zu, Q., Hu, B. (eds.) HCC 2016. LNCS, vol. 9567, pp. 400–408. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-31854-7_36CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Wenan Tan
    • 1
    • 2
    Email author
  • Lu Zhao
    • 2
  • Na Xie
    • 2
  • Anqiong Tang
    • 1
  • Xiaoming Hu
    • 1
  • Shan Tang
    • 1
  1. 1.School of Computer and Information EngineeringShanghai Polytechnic UniversityShanghaiChina
  2. 2.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina

Personalised recommendations