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Fuzzy project scheduling using constraint programming

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper aims to present an application of constraint programming techniques for project portfolio scheduling taking into account the imprecision in activity duration and cost. Data specification in the form of discrete a-cuts allows combining distinct and imprecise data, and implementing a constraint satisfaction problem with the use of constraint programming. Moreover using a-cuts, optimistic, pessimistic, and several intermediate scenarios concerning the project scheduling and cash flows can be obtained and considered in terms of different risk levels.
Rocznik
Strony
3--16
Opis fizyczny
Bibliogr. 30 poz., fig., tab.
Twórcy
autor
  • University of Zielona Góra, Faculty of Economics and Management, Licealna 9, 65-216 Zielona Góra, Poland
Bibliografia
  • [1] Laslo Z.: Project portfolio management: An integrated method for resource planning and scheduling to minimize planning/scheduling-dependent expenses. International Journal of Project Management, 28, 2010, 609-618.
  • [2] Kormancová G.: Project success and failure. In: Zborník vedeckých prác: Theory of Management 6: The Selected Problems for the Development Support of Management Knowledge Base, Žilinská univerzita v Žiline, EDIS – vydavateľstvo ŽU, Žilina 2012, 117-119.
  • [3] Chelaka M., Abeyasinghe L., Greenwood D.J., Johansen D.E.: An efficient method for scheduling construction projects with resource constraints. International Journal of Project Management, 19, 2001, 29-45.
  • [4] Kastor A., Sirakoulis K.: The effectiveness of resource levelling tools for Resource Constraint Project Scheduling Problem. International Journal of Project Management, 27, 2009, 493-500.
  • [5] Budík J., Doskočil R.: Soft computing as a tool to optimize an investment portfolio. Intellectual Economics, 5(3), 2011, 359-370.
  • [6] Kim J.Y., Kang C.W., Hwang I.K.: A practical approach to project scheduling: considering the potential quality loss cost in the time-cost tradeoff problem. International Journal of Project Management, 30, 2012, 264-272.
  • [7] Yaghootkar K., Gil N.: The effects of schedule-driven project management in multi-project environments. International Journal of Project Management, 30, 2012, 127-140.
  • [8] Zammori F.A., Braglia M., Frosolini M.: A fuzzy multi-criteria approach for critical path definition. International Journal of Project Management 27, 2009, 278-291.
  • [9] Atkinson R., Crawford L., Ward S.: Fundamental uncertainties in projects and the scope of project management. International Journal of Project Management, 24, 2006, 687-698.
  • [10] Bonnal P., Gaurc K., Lacoste G.: Where do we stand with fuzzy project scheduling? Journal of Construction Engineering and Management, 130, 2004, 114-123.
  • [11] Long L.D., Ohsato A.: Fuzzy critical chain method for project scheduling under resource constraints and uncertainty. International Journal of Project Management, 26, 2008, 688-698.
  • [12] Fortin J., Zielinski P., Dubois D., Fargier F.: Criticality analysis of activity networks under interval uncertainty. Journal of Scheduling, 13, 2010, 609-627.
  • [13] Maravas A., Pantouvakis J.P.: A fuzzy repetitive scheduling method for projects with repeating activities. Journal of Construction Engineering and Management, 137, 2011, 561-564.
  • [14] Bocewicz G., Banaszak Z.: Abductive inference based approach to DSS designing for project portfolio planning. Intelligent System for Knowledge Management, Series: Studies in Computational Intelligence, 252, 2009, 107-129.
  • [15] Relich M.: CP-based decision support for scheduling. Applied Computer Science, 7(1), 2011, 7-17.
  • [16] Relich M.: Project prototyping with application of CP-based approach. Management, 15(2), 2011, 364-377.
  • [17] Relich M., Witkowski K., Ważna L.: The application of constraint programming to building the tool of liquidity planning in enterprise. Management, 11(1), 2007, 129-137.
  • [18] Van Roy P., Haridi S.: Concepts, techniques and models of computer programming. Massachusetts Institute of Technology 2004.
  • [19] Rossi F., Van Beek P., Walsh T.: Handbook of Constraint Programming. Elsevier Science 2006.
  • [20] Doskočil R., Doubravský K.: Analysis of empirical characteristics in the PERT method. Business Systems & Economics, 2(1), 2012, 7-19.
  • [21] Mon D.L., Cheng C.H, Lu H.C.: Application of fuzzy distribution on project management. Fuzzy Set and Systems, 73(2), 1995, 227-234.
  • [22] Maravas A., Pantouvakis J.P.: Project cash flow analysis in the presence of uncertainty in activity duration and cost. International Journal of Project Management, 30, 2012, 374-384.
  • [23] Relich M.: An evaluation of project completion with application of fuzzy set theory. Management, 16(1), 2012, 216-229.
  • [24] Cheba K.: The application of the index seasons method in forecasting decade data. Prace Naukowe Akademii Ekonomicznej imienia Oskara Langego we Wrocławiu, 1112, 2006, 49-58.
  • [25] Gondek A.: Solutions for estimating the range of delay in time - area analogy method. Management, 14(2), 2010, 161-170.
  • [26] Relich M.: A decision support system for alternative project choice based on fuzzy neural networks. Management and Production Engineering Review, 1(4) 2010, 46-54.
  • [27] Relich M.: Assessment of task duration in investment projects. Management, 14(2), 2010, 136-147.
  • [28] Sobiechowska-Ziegert A.: Equilibrium price – modeling, forecast and application. Econometrics: Forecasting, 28, 2010, 125-135.
  • [29] Dittmann I.: Forecasting in constructing scenarios for assessing profitability and risk of investing in real property. Econometrics, 32, 2011, 83-91.
  • [30] Kużdowicz P.: Application of the concept of further approximations in production cost accounting. In: Global Crises - Opportunities and Threats, Alumnipress, 2012, 109-116.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4ab64b8a-62dc-4877-9c40-918d8e35a265
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