Cybernetics and Systems Analysis

, Volume 35, Issue 3, pp 479–490 | Cite as

Method of fuzzy critical path for network planning and control over projects based on soft computations

  • A. I. Sleptsov
  • T. A. Tyshchuk
Systems Analysis

Conclusions

  1. 1.

    As is established from the analysis, the classical method of critical path in network planning of projects is not valid in the case where the evaluations of durations of works are fuzzy quantities.

     
  2. 2.

    The method of fuzzy critical path is based on computation of a fuzzy set of critical works, fuzzy set of critical paths, fuzzy reserve of execution of uncritical works, and analysis of the risk content of a project.

     
  3. 3.

    The realization of soft computations of the proposed method of fuzzy critical path makes it possible to find the characteristics of a project expressed in terms of fuzzy numbers and to compare and improve projects; it promotes control over projects on the basis of fuzzy data as is done in the classical method of network planning and control.

     

Keywords

Fuzzy Number Critical Path Time Schedule Finish Time Network Planning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    H. Taha, An Introduction to Operations Research [Russian translation], Vol. 2, Mir, Moscow (1985).Google Scholar
  2. 2.
    D. Dubois and A. Pradl, Theory of Possibilities: Applications to Knowledge Representation in Informatics [Russian translation], Radio i Svyaz’ (1990).Google Scholar
  3. 3.
    B. E. Teplitskii and A. F. Blishun, “Time scheduling based on fuzzy temporal relations,” in: Fuzzy systems of Decision Making Support, Sb. Nauch. Tr., KGU, Kalinin (1989), pp. 45–58.Google Scholar
  4. 4.
    M. Mares, “Some remarks to fuzzy critical path method,” Ekonomicko-matematicky Obzor (Praha),27, No. 4, 367–370 (1991).MATHGoogle Scholar
  5. 5.
    “Underdetermined technology of time scheduling: New apabilities of TIME-EX/Windows,” in: Artificial Intelligence-96, Sb. Nauch. Tr. V Nats. Konf., KGU, Kazan’ (1996), pp. 239-244.Google Scholar
  6. 6.
    M. Mares, “Critical path method with verbal inputs,” in: Proc. ICOTA-98, Praha (1998), pp. 1–7.Google Scholar
  7. 7.
    L. A. Zadeh, “Fuzzy logic, neural networks and soft computing,” Commun. ACM,37, No. 3, 77–84 (1994).CrossRefMathSciNetGoogle Scholar
  8. 8.
    Z. L. Rabinovich, “Machine intelligence paradigm and its development,” Kibern. Sist. Anal., No. 2, 163-173 (1995).Google Scholar
  9. 9.
    A. I. Sleptsov and A. A. Yurasov, Automation of Designing Systems for controlling Flexible Automated Industrial Processes [in Russian], B. N. Malinovskii (ed.), Tekhnika, Kiev (1986).Google Scholar
  10. 10.
    A. I. Sleptsov and D. A. Zaitsev, “Methodical materials supporting the software of the “Opera” system for real-time network planning and control,” DPI, Donetsk (1991).Google Scholar
  11. 11.
    D. A. Zaitsev and A. I. Sleptsov, “State equations and equivalent transformations for timed Petri nets,” Kibern. Sist. Anal. No. 5, 59–74 (1997).Google Scholar
  12. 12.
    D. A. Pospelov, (ed.), Fuzzy Sets in Models of Control and Artificial Intelligence [in Russian], Nauka, Moscow (1986).Google Scholar
  13. 13.
    A. Koffman, An Introduction to the theory of fuzzy sets [Russian translation], Radio i Svyaz’, Moscow (1982).Google Scholar

Copyright information

© Kluwer Academic/Plenum Publishers 1999

Authors and Affiliations

  • A. I. Sleptsov
  • T. A. Tyshchuk

There are no affiliations available

Personalised recommendations