Advertisement

Project Scheduling with Fuzzy Cost and Schedule Buffers

  • Pawel Blaszczyk
  • Tomasz Blaszczyk
  • Maria B. Kania
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 170)

Abstract

The aim of this research was the trial of modelling and optimizing the time-cost trade-offs in project planning problem with taking into account the behavioral impact of performers’ (or subcontractors’) estimations of basic activity parameters. However, such a model must include quantitative measurements of budget and duration, so we proposed to quantify and minimize the apprehension of their underestimations. The base of the problem description contains both safe and reasonable amounts of work estimations and the influence factors matrix. We assumed also the pricing opportunity of performance improving. Finally we introduce fuzzy measurements for work amount. This paper is a revised, extended version of Blaszczyk et al. 2011, presented on the World Congress on Engineering and Computer Science 2011.

Keywords

Buffer management Project planning Time-cost trade-off Fuzzy numbers Scheduling Fuzzy linear programming 

References

  1. 1.
    Blaszczyk T, Nowak B (2008) Project costs estimation on the basis of critical chain approach (in Polish), Trzaskalik T (ed.) Modelowanie preferencji a Ryzyko’08, Akademia Ekonomiczna w Katowicach, KatowiceGoogle Scholar
  2. 2.
    Blaszczyk P, Blaszczyk T, Kania MB (2009) Task duration buffers or work amount buffers? In: Proceedings of the first earned value analysis conference for the continental Europe, Geneva, 2009. vol 1, pp 345–375Google Scholar
  3. 3.
    Blaszczyk P, Blaszczyk T, Kania MB (2011) The bi-criterial approach to project cost and schedule buffers sizing. Lecture notes in mathematics and economy, Springer, Berlin 2011. pp 105–114Google Scholar
  4. 4.
    Blaszczyk P, Blaszczyk T, Kania MB (2011) Theoretical foundations of fuzzy bi-criterial approach to project cost and schedule buffers sizing. In: Proceedings of the world congress on engineering and computer science WCECS 2011, San Francisco, 19–21 October 2011. Lecture notes in engineering and computer science, pp 1121–1125Google Scholar
  5. 5.
    Brucker P, Drexl A, Mohring R, Neumann K, Pesch E (1999) Resource-constrained project scheduling: notation, classification, models and methods. Eur J Oper Res 112:3–41MATHCrossRefGoogle Scholar
  6. 6.
    Buckey JJ, Eslami E, Feuring E (2002) Fuzzy mathematics in economy and engineering. Springer, HeidelbergGoogle Scholar
  7. 7.
    Chen L, Liang F, Xiaoran S, Deng Y, Wang H (2010) Fuzzy-safety-buffer approach for project buffer sizing considering the requirements from project managers and customers. The 2nd IEEE international conference on information management and engineering (ICIME), 2010. pp 482–486Google Scholar
  8. 8.
    Fulkerson DR (1961) A network flow computation for project cost curves. Manag Sci 7:167–178MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Goldratt E (1997) Critical chain. North River Press, Great BarringtonGoogle Scholar
  10. 10.
    Gonzalez V, Alarcon LF, Molenaar K (2009) Multiobjective design of Work-In-Process buffer for scheduling repetitive projects. Autom Constr 18:95–108CrossRefGoogle Scholar
  11. 11.
    Herroelen W, Leus R (2009) On the merits and pitfalls of critical chain scheduling. J Oper Manag 19:559–577CrossRefGoogle Scholar
  12. 12.
    Jamison KD, Lodwick WA (2001) Fuzzy linear programming using a penalty method. Fuzzy Sets Syst 119:97–110MathSciNetMATHCrossRefGoogle Scholar
  13. 13.
    Kelley JE (1961) Critical-path planning and scheduling: mathematical basis. Oper Res 9:296–320MathSciNetMATHCrossRefGoogle Scholar
  14. 14.
    Leach L (2003) Schedule and cost buffer sizing: how account for the bias between project performance and your model. Proj Manag J 34:34–47Google Scholar
  15. 15.
    Long LD, Ohsato A (2008) Fuzzy critical method for project schedulling under resource constraints and uncertainty. Int J Proj Manag 26:688–698CrossRefGoogle Scholar
  16. 16.
    Ramik J (2006) Duality in fuzzy linear programming with possibility and necessity relations. Fuzzy Sets Syst 157:1283–1302MathSciNetMATHCrossRefGoogle Scholar
  17. 17.
    Rogalska M, Bozejko W, Hejducki Z (2008) Time/cost optimization using hybrid evolutionary algorithm in construction project scheduling. Autom Constr 18:24–31CrossRefGoogle Scholar
  18. 18.
    Shi Q, Gong T (2010) An improved project buffer sizing approach to critical chain management under resources constraints and fuzzy uncertainty. International conference on artificial intelligence and computational intelligence AICI ’09, November 2010. pp. 486–490Google Scholar
  19. 19.
    Tukel OI, Rom WO, Eksioglu SD (2006) An investigation of buffer sizing techniques in critical chain scheduling. Eur J Opera Res 172:401–416MATHCrossRefGoogle Scholar
  20. 20.
    Van de Vonder S, Demeulemeester E, Herroelen W, Leus R (2005) The use of buffers in project management: The trade-off between stability and makespan. Int J Prod Econ 97:227–240CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Pawel Blaszczyk
    • 1
  • Tomasz Blaszczyk
    • 2
  • Maria B. Kania
    • 1
  1. 1.Institute of MathematicsUniversity of SilesiaKatowicePoland
  2. 2.Department of Operations ResearchUniversity of Economics in KatowiceKatowicePoland

Personalised recommendations