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CP-driven approach to multicriteria decision making based on imprecise data

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Języki publikacji
EN
Abstrakty
EN
Multi-criteria decision-making encompass different aspects of Small and Medium Size Enterprise functioning, e.g. money flow, personnel allocation, task oriented scheduling, etc. In general multi-criteria decision making is aimed at determining an company preferences (e.g. financial benefits, intangible benefits, availability of resources, risk level, etc.), and customers requirements (e.g. specified by the time of implementation and budget of the relevant production orders). Moreover all decisions are usually time and financially constrained. Considering above, decision problems are specified by the diverse character of information (including distinct and imprecise data). The approach considered regards of the Logic-Algebraic Method based and Constraint Programming methodology aimed at interactive and multi-criteria decision making. In case the data introduced are specified by a membership function its representation can be discretized and replaced by an ordered set of discrete values. It means, instead of standard fuzzy-set-like operations, e.g. fuzzy complement, intersection, union, and fuzzy inference rules, a set of constraints is considered. In such approach both: distinct and imprecise data as well as linking them relations are treated in a unified form of discrete Constrained Satisfaction Problem. Moreover, implementation of multi-criteria decision making directly follows from the nature of constraint programming paradigm (constraints propagation and variables distribution). The way of possible approach implementation is illustrated in the example enclosed.
Rocznik
Strony
119--130
Opis fizyczny
Bibliogr. 29 poz.
Twórcy
autor
autor
  • Dep. of Computer Science and Management, Technical University of Koszalin, bachirena@wp.pl
Bibliografia
  • [1]Bellman R.E., Giertz M., (1975). On the analytical formalism offuzzy sets, Information Sciences, 5, pp. 149-156
  • [2]Ben-Ari M.(1990): Mathematical Logic for Computer Science, WNT, Warsaw.
  • [3]Bubnicki Z. (1990). Introduction to Expert Systems. PWN, Warsaw.
  • [4]Bubnicki Z. (1999). Learning processes and logic-algebraic method for the systems with knowledge representation. Systems analysis and management. Systems Research Inst. PAS
  • [5]Bubnicki Z. (2004) Application on uncertain variables in learning algorithms for uncertain systems. In: Advances of Computer Cybernetics. Proceedings of InterSymp 2005, Baden-Baden, Germany, pp. 25-29
  • [6]Chang S., Tsujimura Y., Gen M. and Tozawa T. (1995) An efficient approach for large scale project planning based on fuzzy Delphi method, Fuzzy Sets and Systems 76(2), pp. 277-288
  • [7]Chanas S., Zieliński P.,(2001): Critical path analysis in the network with fuzzy activity times. Fuzzy Sets and Systems., Vol 122, pp. 195-204.
  • [8]Chen-Tung C., Huang S.F.,(2006): Order-fulfillment ability analysis in the supplychain system with fuzzy operation times. International Journal Production Economics, Vol: 101, pp. 185-193.
  • [9]Chen-Tung C., Huang S.F.,(2006): Applying fuzzy method for mesuring criticality in project network. Information Science. Vol. 177, pp. 2448-2458.
  • [10]DePorter, E.L. and Ellis, K.P. (1990): Optimization of project networks with goal programming and fuzzy linear programming, Computer and Industrial Engineering 19 (1-4), pp. 500-504.
  • [11]Guiffrida A. L., Nagi R., (1998): Fuzzy Set Theory Applications in Production Management Research: A Literature Survey Journal of Intelligent Manufacturing, Springer, Vol. 9, No. 1, pp.39-56(18)
  • [12]Hapke, M., Jaszkiewicz, A., and Słowiński, R. (1994): Fuzzy project scheduling system for software development, Fuzzy Sets and Systems, 67(1), pp. 101-111.
  • [13]Hua K., Baoding L., (2007): Project scheduling problem with mixed uncertainty of randomness and fuzziness. European Journal of Operational Research. Vol.183, pp.135-147.
  • [14]Jacquet-Lagreze E., Montaut D., Partouche A.,(1998): The shift scheduling problem: different formulations and solution methods, Foundations of Computing And Decision Sciences, 23,4, 199-217.
  • [15]Kuchta D., (2001): Use of fuzzy numbers in project risk (criticality) assessment. International Journal of Project Management. Vol.19, pp. 305-310.
  • [16]Klir G. (2006): Uncertainty and Information: Foundations of Generalized Information Theory; Hoboken, NY: Wiley Interscience
  • [17]Mula J., Poler R., Garcia-Sabater J.P., (2006): Models for production planning under uncertainty: A review. International Journal Production Economics. Vol.103, pp. 271-285.
  • [18]Orski D. (2005): Quality of cascade operations control based on uncertain variables;Artificial Life and Robotics, Vol. 9, No. 1, pp. 32-35
  • [19]Orski D. (2006): Application of uncertain variables in decision problems for a complex of operations; IIHAS Transactions on Systems Research and Cybernetics, Vol. VI, No l.,pp. 19-24
  • [20]Piegat A.(1999): Fuzzy modelling and control, Exit, Warszawa.
  • [21]Serafini P., Simulated annealing for multiple objective optimization problems, in: G.H. Tzeng, H.F. Wang, V.P. Wen, P.L. Yu (eds.), Multiple Criteria Decision Making.Expand and Enrich the Domains of Thinking and Application, Springer, Berlin, 1994, 283-292.
  • [22]Shih-Pin C. (2007): Analysis of critical paths in a project network with fuzzy activity times. European Journal of Operational Research. Vol.183, pp. 442-459.
  • [23]Surendra S.R., (2005): Critical Chain Project Management (A Paradigm Shift in Project Management). Project Magazine.
  • [24]Soltani A., Haji R. (2007): A project scheduling method based on fuzzy theory. Journal of Industrial and Systems Engineering. Vol.l(l) pp.70 -80.
  • [25]Tomaszewski, M.A., (1992): Using advanced computer technologies to increase extension effectiveness. J. Dairy Science 75:3242
  • [26]Van Hentenryck P.,(1991): Constraint Logic Programming, Knowledge Engineering Review, 6, pp.151—194
  • [27]Van Roy P., Haridi S., (2005): Concepts, Techniques, and Models of Computer Programming, Helion, Gliwice.
  • [28]Yu G., Industrial Applications of Combinatorial Optimization, Kluwer Academic Publisher, Boston, 1998.
  • [29]Zadeh L.A.,(1965): Fuzzy sets, Information and Control, 8, pp. 338-353
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPP1-0088-0081
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