Прегледај по Аутор "Takači, Aleksandar"

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    An Extension of Maximal Covering Location Problem based on the Choquet Integral
    (Obuda University, Hungary, 2016) Takači, Aleksandar; Štajner-Papuga, Ivana; Drakulić, Darko; Marić, Miroslav
    The aim of this paper is to demonstrate the applicability of the Choquet integral, a well-known fuzzy integral, in the Maximal Covering Location Problem (MCLP). Possible benefits of the used integral, which is based on monotone set functions, include the flexibility of а monotone set function, which is in the core of the Choquet integral, for modeling the Decision Maker's behavior. Various mathematical models of the Maximal Covering Location Problem are given. The approach, based on the Choquet integral versus the standard approach, is thoroughly discussed and illustrated by several examples.
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    Fuzzy Covering Location Problems with Different Aggregation Operators
    (Faculty of Sciences and Mathematics, University of Niš, Serbia, 2017) Drakulić, Darko; Takači, Aleksandar; Marić, Miroslav
    Covering location problems is well-known and very important class of combinatorial optimization problems. Standard models for covering location problems cannot encompass real-life problems, because real-life problems contain some degree of uncertainty. The use of fuzzy sets in modeling covering location problems allows the implementation of these conditions. Depending on the type of problems, it is necessary to use different aggregation operators in calculating solution’s quality. The aim of this study is introducing of fuzzy sets with different corresponding conorms in modeling most known types of covering location problems.
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    NEW MODEL OF MAXIMAL COVERING LOCATION PROBLEM WITH FUZZY CONDITIONS
    (Slovak Academy of Sciences, 2016) Drakulić, Darko; Takači, Aleksandar; Marić, Miroslav
    The objective of Maximal Covering Location Problem is locating facilities such that they cover the maximal number of locations in a given radius or travel time. MCLP is applied in many different real-world problems with several modi cations. In this paper a new model of MCLP with fuzzy conditions is presented. It uses two types of fuzzy numbers for describing two main parameters of MCLP { coverage radius and distances between locations. First, the model is de ned, then Particle Swarm Optimization method for solving the problem is described and tested.