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A Multi-criteria Approach for Team Recommendation

  • Michael Arias
  • Jorge Munoz-Gama
  • Marcos Sepúlveda
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 281)

Abstract

Team recommendation is a key and little-explored aspect within the area of business process management. The efficiency with which the team is conformed may influence the success of the process execution. The formation of work teams is often done manually, without a comparative analysis based on multiple criteria between the individual performance of the resources and their collective performance in different teams. In this article, we present a multi-criteria framework to allocate work teams dynamically. The framework considers four elements: (i) a resource request characterization, (ii) historical information on the process execution and expertise information, (iii) different metrics which calculate the suitability of the work teams taking into account both individual performance as well as collective performance of the resources, and (iv) a recommender system based on the Best Position Algorithm (BPA2) to obtain a ranking for the recommended work teams. A software development process was used to test the usefulness of our approach.

Keywords

Team recommendation Resource allocation Process mining Business processes Recommender systems Organizational perspective 

Notes

Acknowledgments

This project was partially funded by the Ph.D. Scholarship Program of CONICYT Chile (Doctorado Nacional/2014-63140181), Universidad de Costa Rica and by Fondecyt (Chile) Project No.1150365.

References

  1. 1.
    van der Aalst, W.M.P., Verbeek, H.M.W.: Process discovery and conformance checking using passages. Fundam. Inform. 131(1), 103–138 (2014)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Akbarinia, R., Pacitti, E., Valduriez, P.: Best position algorithms for efficient top-k query processing. Inf. Syst. 36(6), 973–989 (2011)CrossRefGoogle Scholar
  3. 3.
    Arias, M., Rojas, E., Munoz-Gama, J., Sepúlveda, M.: A framework for recommending resource allocation based on process mining. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 458–470. Springer, Cham (2016). doi: 10.1007/978-3-319-42887-1_37 CrossRefGoogle Scholar
  4. 4.
    Ballesteros-Pérez, P., González-Cruz, M.C., Fernández-Diego, M.: Human resource allocation management in multiple projects using sociometric techniques. Intl. J. Project Manage. 30(8), 901–913 (2012)CrossRefGoogle Scholar
  5. 5.
    Barreto, A., de Oliveira Barros, M., Werner, C.M.L.: Staffing a software project a constraint satisfaction and optimization-based approach. Comput. OR 35(10), 3073–3089 (2008)CrossRefzbMATHGoogle Scholar
  6. 6.
    Britto, R., de Alcântara dos Santos Neto, P., Rabelo, R.A.L., Ayala, W., Soares, T.: A hybrid approach to solve the agile team allocation problem. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC, pp. 1–8 (2012)Google Scholar
  7. 7.
    Cabanillas, C., Resinas, M., Ruiz-Cortés, A.: Automated resource assignment in BPMN models using RACI matrices. In: Meersman, R., et al. (eds.) OTM 2012. LNCS, vol. 7565, pp. 56–73. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33606-5_5 CrossRefGoogle Scholar
  8. 8.
    Cabanillas, C., Resinas, M., Mendling, J., Cortés, A.R.: Automated team selection and compliance checking in business processes. In: Proceedings of the 2015 International Conference on Software and System Process, ICSSP, pp. 42–51 (2015)Google Scholar
  9. 9.
    Chaudhuri, S., Dayal, U.: An overview of data warehousing and olap technology. ACM Sigmod Rec. 26(1), 65–74 (1997)CrossRefGoogle Scholar
  10. 10.
    Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Gerogiannis, V.C., Rapti, E., Karageorgos, A., Fitsilis, P.: Human resource assessment in software development projects using fuzzy linguistic 2-tuples. In: Artificial Intelligence, Modelling and Simulation (AIMS), pp. 217–222. IEEE (2014)Google Scholar
  12. 12.
    Huang, Z., van der Aalst, W.M.P., Lu, X., Duan, H.: Reinforcement learning based resource allocation in business process management. DKE 70(1), 127–145 (2011)CrossRefGoogle Scholar
  13. 13.
    Huang, Z., Lu, X., Duan, H.: Mining association rules to support resource allocation in business process management. Expert Syst. Appl. 38(8), 9483–9490 (2011)CrossRefGoogle Scholar
  14. 14.
    Kim, A., Obregon, J., Jung, J.-Y.: Constructing decision trees from process logs for performer recommendation. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 224–236. Springer, Cham (2014). doi: 10.1007/978-3-319-06257-0_18 CrossRefGoogle Scholar
  15. 15.
    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). doi: 10.1007/978-3-642-40176-3_20 CrossRefGoogle Scholar
  16. 16.
    Li, C., Akker, J.M., Brinkkemper, S., Diepen, G.: Integrated requirement selection and scheduling for the release planning of a software product. In: Sawyer, P., Paech, B., Heymans, P. (eds.) REFSQ 2007. LNCS, vol. 4542, pp. 93–108. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-73031-6_7 CrossRefGoogle Scholar
  17. 17.
    Liu, X., Chen, J., Ji, Y., Yu, Y.: Q-learning algorithm for task allocation based on social relation. In: Cao, J., Wen, L., Liu, X. (eds.) PAS 2014. CCIS, vol. 495, pp. 49–58. Springer, Heidelberg (2015). doi: 10.1007/978-3-662-46170-9_5 Google Scholar
  18. 18.
    Munoz-Gama, J., Carmona, J., van der Aalst, W.M.P.: Single-entry single-exit decomposed conformance checking. Inf. Syst. 46, 102–122 (2014)CrossRefGoogle Scholar
  19. 19.
    Narendra, N.C., Ponnalagu, K., Zhou, N., Gifford, W.M.: Towards a formal model for optimal task-site allocation and effort estimation in global software development. In: 2012 Annual SRII Global Conference, pp. 470–477 (2012)Google Scholar
  20. 20.
    Oberweis, A., Schuster, T.: A meta-model based approach to the description of resources and skills. In: AMCIS, p. 383 (2010)Google Scholar
  21. 21.
    Royce, W.W.: Managing the development of large software systems. In: proceedings of IEEE WESCON, vol. 26, pp. 1–9 (1970)Google Scholar
  22. 22.
    Russell, N., Aalst, W.M.P., Hofstede, A.H.M., Edmond, D.: Workflow resource patterns: identification, representation and tool support. In: Pastor, O., Falcão e Cunha, J. (eds.) CAiSE 2005. LNCS, vol. 3520, pp. 216–232. Springer, Heidelberg (2005). doi: 10.1007/11431855_16 CrossRefGoogle Scholar
  23. 23.
    Schönig, S., Cabanillas, C., Jablonski, S., Mendling, J.: A framework for efficiently mining the organisational perspective of business processes. DSSs 89, 87–97 (2016)Google Scholar
  24. 24.
    e Silva, L., Costa, A.P.: Decision model for allocating human resources in information system projects. Intl. J. Proj. Manage. 31(1), 100–108 (2013)CrossRefGoogle Scholar
  25. 25.
    Sommerville, I.: Software Engineering. Pearson, London (2015)zbMATHGoogle Scholar
  26. 26.
    Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Satzilla: Portfolio-based algorithm selection for SAT. CoRR abs/1111.2249 (2011)Google Scholar
  27. 27.
    Zhao, W., Zhao, X.: Process mining from the organizational perspective. In: Wen, Z., Li, T. (eds.) Foundations of Intelligent Systems. AISC, vol. 277, pp. 701–708. Springer, Heidelberg (2014). doi: 10.1007/978-3-642-54924-3_66 Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Michael Arias
    • 1
  • Jorge Munoz-Gama
    • 1
  • Marcos Sepúlveda
    • 1
  1. 1.Department of Computer Science, School of EngineeringPontificia Universidad Católica de ChileSantiagoChile

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