Skip to main content
SpringerLink
Log in

Designing a Decision-Making Process for Partially Observable Environments Using Markov Theory

  • Conference paper
  • First Online:
Enterprise, Business-Process and Information Systems Modeling (BPMDS 2017, EMMSAD 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 287))

Abstract

This paper is motivated by the problem of deciding how to proceed in a business process when a workaround occurs. In addition to this problem, most of the times, the exact state of the business processes is not fully available to the industrial organization. Therefore, it means that something wrong happens during automatic (or manual) operation; however, the managers do not know exactly the state of the operating systems. Usually, the combination of these problems drives to management decision without enough information and thus error prone. This paper integrates Markov theory with business processes design to predict the impact of each decision in the operational environment. The solution is tested in an agro-food industrial company that transforms fresh fruit to preparations that are sold to others companies. The paper shows that anticipating the production processes changes has the benefit of minimizing lot infections or stock disrupts. The main identified limitations are the compute intensive process involved and the effort required to estimate the business processes details. In future research, this work might be (i) extended to friendly software interfaces in order to facilitate the interaction with end-users, (ii) optimized to the algorithm of informed decision-making computation, and (iii) automatic estimation of business processes details using machine learning techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Referred in the literature as the opposite of “a system is completely observable if every state variable of the system affects some of the outputs” [13], i.e., “a system is partially observable if some state variable of the system affects some of the outputs”.

  2. 2.

    Intensional is usually defined as: giving the essence of a term.

References

  1. van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Database Management & Information Retrieval. Springer, Heidelberg (2011)

    Book  Google Scholar 

  2. von Alan, R.H., March, S.T., Park, J., Ram, S.: Design science in information systems research. MIS Q. 28(1), 75–105 (2004)

    Google Scholar 

  3. Bertalanffy, L.V.: General Systems Theory. George Braziller, New York (1969)

    Google Scholar 

  4. Boehm, B.W., Madachy, R., Steece, B., et al.: Software Cost Estimation with Cocomo II with Cdrom. Prentice Hall PTR, Upper Saddle River (2000)

    Google Scholar 

  5. Bonhomme, P.: Marking estimation of p-time petri nets with unobservable transitions. IEEE Trans. Syst. Man Cybern. Syst. 45(3), 508–518 (2015)

    Article  Google Scholar 

  6. Cai, P.: Approximate POMDP planning (appl) toolkit (2010). https://github.com/petercaiyoyo/appl

  7. Cassandra, A.R.: POMDP solver (v53 2005). http://www.pomdp.org/code/index.shtml

  8. De Bruyn, P., Huysmans, P., Oorts, G., Mannaert, H., Verelst, J.: Using entropy’s justificatory knowledge for a business process design theory. In: 2014 47th Hawaii International Conference on System Sciences (HICSS), pp. 3717–3726. IEEE (2014)

    Google Scholar 

  9. Dietz, J.L.G.: Enterprise Ontology: Theory and Methodology. Springer, Heidelberg (2006)

    Book  Google Scholar 

  10. Dietz, J.L., Hoogervorst, J.A., Albani, A., Aveiro, D., Babkin, E., Barjis, J., Caetano, A., Huysmans, P., Iijima, J., van Kervel, S., et al.: The discipline of enterprise engineering. Int. J. Organ. Des. Eng. 3(1), 86–114 (2013)

    Google Scholar 

  11. Elovici, Y., Braha, D.: A decision-theoretic approach to data mining. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 33(1), 42–51 (2003)

    Article  Google Scholar 

  12. Fleischmann, A., Stary, C.: Whom to talk to? A stakeholder perspective on business process development. Univ. Access Inf. Soc. 11(2), 125–150 (2012). http://dx.doi.org/10.1007/s10209-011-0236-x

    Article  Google Scholar 

  13. Franklin, G., Powell, J., Emami-Naeini, A.: Feedback Control of Dynamic Systems, 2nd edn. Addison-Wesley Publishing Company, Reading (1991)

    Google Scholar 

  14. Group, O.M: BPMN specification 1.2. pdf available in internet, January 2009

    Google Scholar 

  15. Guerreiro, S.: Decision-making in partially observable environments. In: 2014 IEEE 16th Conference on Business Informatics (CBI), vol. 1, pp. 159–166, July 2014

    Google Scholar 

  16. Guerreiro, S., Tribolet, J.: Conceptualizing enterprise dynamic systems control for run-time business transactions. In: European Conference on Information Systems (ECIS) 2013, paper 5 (2013)

    Google Scholar 

  17. Guerreiro, S.: Business rules elicitation combining Markov decision process with demo business transaction space. In: 2013 IEEE 15th Conference on Business Informatics (CBI), pp. 13–20 (2013)

    Google Scholar 

  18. Guizzardi, G.: On ontology, ontologies, conceptualizations, modeling languages, and (meta) models. Front. Artif. Intell. Appl. 155, 18 (2007)

    Google Scholar 

  19. Hoogervorst, J.A.: Enterprise Governance and Enterprise Engineering. The Enterprise Engineering Series. Springer, Heidelberg (2009)

    Book  Google Scholar 

  20. Johnson, P., Ullberg, J., Buschle, M., Franke, U., Shahzad, K.: An architecture modeling framework for probabilistic prediction. Inf. Syst. e-Bus. Manag. 12(4), 595–622 (2014)

    Article  Google Scholar 

  21. Laudon, K.C., Laudon, J.P., et al.: Essentials of Management Information Systems. Pearson, Upper Saddle River (2011)

    Google Scholar 

  22. Metzger, A., Leitner, P., Ivanovi, D., Schmieders, E., Franklin, R., Carro, M., Dustdar, S., Pohl, K.: Comparing and combining predictive business process monitoring techniques. IEEE Trans. Syst. Man Cybern. Syst. 45(2), 276–290 (2015)

    Article  Google Scholar 

  23. Mikaelian, T., Nightingale, D.J., Rhodes, D.H., Hastings, D.E.: Real options in enterprise architecture: a holistic mapping of mechanisms and types for uncertainty management. IEEE Trans. Eng. Manag. 58(3), 457–470 (2011)

    Article  Google Scholar 

  24. Montiel, L., Bickel, J.: A simulation-based approach to decision making with partial information. Decis. Anal. 9(4), 329–347 (2012)

    Article  Google Scholar 

  25. OMG: Business process model and notation (2011). http://www.omg.org/spec/BPMN/2.0/

  26. Österle, H., Becker, J., Frank, U., Hess, T., Karagiannis, D., Krcmar, H., Loos, P., Mertens, P., Oberweis, A., Sinz, E.J.: Memorandum on design-oriented information systems research. Eur. J. Inf. Syst. 20(1), 7–10 (2011)

    Article  Google Scholar 

  27. Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York (1994)

    Book  Google Scholar 

  28. Reijers, H.A., Limam, S., van der Aalst, W.M.P.: Product-based workflow design. J. Manag. Inf. Syst. 20(1), 229–262 (2003)

    Google Scholar 

  29. Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008). doi:10.1016/j.is.2007.07.001

    Article  Google Scholar 

  30. Russell, S., Norvig, P.: Artificial Intelligence: A Modern approach. Artificial Intelligence, 3rd edn. Prentice Hall, Englewood Cliffs (2010)

    Google Scholar 

  31. Smith, T.: ZMDP software for POMDP and MDP planning (2007). https://github.com/trey0/zmdp

  32. Sommestad, T., Ekstedt, M., Johnson, P.: A probabilistic relational model for security risk analysis. Comput. Secur. 29(6), 659–679 (2010)

    Article  Google Scholar 

  33. Spaan, M.: Perseus randomized point-based approximate value iteration algorithm (2004). http://users.isr.ist.utl.pt/mtjspaan/software/index_en.html

  34. van der Aalst, W.M.P., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012). doi:10.1007/978-3-642-28108-2_19

    Chapter  Google Scholar 

  35. Vanderfeesten, I., Reijers, H.A., Van der Aalst, W.M.P.: Product-based workflow support. Inf. Syst. 36(2), 517–535 (2011)

    Article  Google Scholar 

  36. Weber, M.: Decision making with incomplete information. Eur. J. Oper. Res. 28(1), 44–57 (1987)

    Article  Google Scholar 

  37. Wieringa, R.J.: Design Science Methodology for Information Systems and Software Engineering. Springer, London (2014)

    Book  Google Scholar 

  38. Wilf, J., Port, D.: Decisions and disasters: modeling decisions that contribute to mishaps. In: 2016 49th Hawaii International Conference on System Sciences (HICSS), pp. 5635–5641. IEEE (2016)

    Google Scholar 

  39. Winograd, T.: A language/action perspective on the design of cooperative work. In: Proceedings of the 1986 ACM Conference on Computer-Supported Cooperative Work, pp. 203–220. ACM (1986)

    Google Scholar 

  40. Winter, R.: Design science research in Europe. Eur. J. Inf. Syst. 17(5), 470–475 (2008)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with reference UID/CEC/50021/2013.

Author information

Authors and Affiliations

  1. Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal

    Sérgio Guerreiro

  2. INESC-ID, Rua Alves Redol 9, 1000-029, Lisbon, Portugal

    Sérgio Guerreiro

Authors
  1. Sérgio Guerreiro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sérgio Guerreiro .

Editor information

Editors and Affiliations

  1. University of Haifa , Haifa, Israel

    Iris Reinhartz-Berger

  2. University of Duisburg-Essen , Duisburg, Germany

    Jens Gulden

  3. Université de Paris 1 Panthéon - Sorbonne, Paris, France

    Selmin Nurcan

  4. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Wided Guédria

  5. Saint Louis University , St. Louis, Missouri, USA

    Palash Bera

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Guerreiro, S. (2017). Designing a Decision-Making Process for Partially Observable Environments Using Markov Theory. In: Reinhartz-Berger, I., Gulden, J., Nurcan, S., Guédria, W., Bera, P. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2017 2017. Lecture Notes in Business Information Processing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-59466-8_16

Download citation

Publish with us

Policies and ethics

Search

Navigation