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Compromise in scheduling objects procedures basing on ranking lists

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Języki publikacji
EN
Abstrakty
EN
In our work possibility of support of ranking objects (tasks) is analyzed on base of group of lists. We can get these lists both from experts or with help of approximating and simple (according to complexity) algorithms. To support analyze we can use elements of neighborhood theory, preferential models, and rough sets theory. This supporting process is used for creation final list of tasks sequence. Usually, these problems are connected with distribution, classification, prediction, strategy of games as well as compromise searching operations. The utilization preference and domination models permits to crisp inferences and to force the chronological location of object. In some situations we have deal with dynamic character of filling lists resulting from continuous tasks succeeding and continuous their assigning to executive elements. The utilization the theory of neighborhood permits to locate objects in range of compromised solutions consist in closing to dominating proposal group. Main task for us is find the best compromise in aspect to final objects location. We want to defined advantages and drawback of methods basing on mention theories and analyze possibilities of their cooperation or mutual completions.
Rocznik
Strony
177--186
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
autor
Bibliografia
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  • [4] Piech H., Analysis of possibilities and effectiveness of combine rough set theory and neibourhood theories for solving dynamic scheduling problem, IEEE Computer Society, P3674, Washington, Tokyo 2009, 296-302.
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  • [16] Greco S., Matarazzo B., Słowiński R., Stefanowski J., An algorithm for induction of decision rules with dominance principle, Rough Sets and Current Trends in Computing, LNAI, Springer-Verlag, Berlin 2005, 304-313.
  • [17] Sysło M.M., Deo N., Kowalik J.S., Algorytmy optymalizacji dyskretnej, WN PWN, Warszawa 1995.
  • [18] Słowiński R., Brzezińska I., Greco S., Application of Bayesian Confirmation Measures for Mining Rules from Suport Confidence Pareto Optimal Set, ICAISC LNSC, 4029, Springer, Heilderberg 2006, 1018-1026.
  • [19] Talbi E.D., Geneste L., Grabot B., Prévitali R., Hostachy P., Application of optimization techniques to parameter set-up in scheduling, Computers in Industry 2004, 55, 2, October, 105-124.
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  • [21] Kent R.E., Rough concept analysis: A synthesis of rough sets and formal concept analysis, Fundamanta Informaticae 1996, 27, 169-181.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPC6-0003-0019
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