In the literature, many multiple criteria decision making methods have been proposed. There are also a number of papers, which are devoted to comparison of their characteristics and performances. However, a definitive answer to questions: which method is most suitable and which method is most effective is still actual. Therefore, in this paper, the use of some prominent multiple criteria decision making methods is considered on the example of ranking Serbian banks. The objective of this paper is not to determine which method is most appropriate for ranking banks. The objective of this paper is to emphasize that the use of various multiple criteria decision making methods sometimes can produce different ranking orders of alternatives, highlighted some reasons which lead to different results, and indicate that different results obtained by different MCDM methods are not just a random event, but rather reality.Keywords: MCDM, SAW, MOORA, GRA, CP, VIKOR, TOPSIS * Corresponding author: dragisa.stanujkic@fmz.edu.rs S e r b i a n J o u r n a l o f M a n a g e m e n t Serbian Journal of Management 8 (2) (2013) 213 -241www.sjm06.com DOI:10.5937/sjm8-3774 1968), Compromise programming (Zeleny, 1973;Yu, 1973), Analytic Hierarchy Process (AHP) method (Saaty, 1980), Technique for Ordering Preference by Similarity to Ideal Solution (TOPSIS) method (Hwang & Yoon, 1981), Preference Ranking Organisation Method for Enrichment Evaluations (PROMETHEE) method (Brans & Vincke, 1985), Grey Relational Analysis (GRA) proposed by Deng (1989) as part of Grey system theory, ELimination and Choice Expressing REality (ELECTRE) method (Roy, 1991), COmplex PRoportional ASsessment (COPRAS) method (Zavadskas et al., 1994) (Brauers & Zavadskas, 2010a).In the past, these methods have been used to solve many problems, which are documented in many professional and scientific journals. Numerous prominent papers presented research in MCDM, which is why we omit the reference to them in this paper.The above-mentioned MCDM methods transform multiple criteria decision-making process, i.e., Multiple Criteria optimization, in a single criterion decision-making optimization, which is much easier to solve. A number of authors have been identifying different phases (stages) in MCDM process, from which, in order to more clearly point out the objectives of this study, the following phases are emphasized:-criteria weights determination, -normalization, -aggregation, and -selection. A typical MCDM problem can be precisely presented in the following form:( 1) where D is decision matrix, x ij is performance of i-th alternative with respect to j-th criterion, W is weight vector, w j is weight of j-th criterion, i = 1,2, … m; m is the number of compared alternatives, j = 1,2, ..., n; n is the number of the criteria.Information stored in a decision matrix is usually incommensurable, i.e. performance ratings in relation to different criteria are usually expressed using different units of measure. Therefore, data should be transformed into comparable values, usi...
Widespread availability of location aware devices (such as GPS receivers) promotes capture of detailed movement trajectories of people, animals, vehicles and other moving objects. We investigate spatio-temporal movement patterns in large tracking data sets, i.e. in large sets of polygonal paths. Specifically, we study so-called 'popular places', that is, regions that are visited by many entities. Given a set of polygonal paths with a total of [Formula: see text] vertices, we look at the problem of computing such popular places in two different settings. For the discrete model, where only the vertices of the polygonal paths are considered, we propose an [Formula: see text] algorithm; and for the continuous model, where also the straight line segments between the vertices of a polygonal path are considered, we develop an [Formula: see text] algorithm. We also present lower bounds and hardness results.
Abstract. We are given a trajectory T and an area A. T might intersect A several times, and our aim is to detect whether T visits A with some regularity, e.g. what is the longest time span that a GPS-GSM equipped elephant visited a specific lake on a daily (weekly or yearly) basis, where the elephant has to visit the lake most of the days (weeks or years), but not necessarily on every day (week or year). During the modelling of such applications, we encounter an elementary problem on bitstrings, that we call LDS (LongestDenseSubstring). The bits of the bitstring correspond to a sequence of regular time points, in which a bit is set to 1 iff the trajectory T intersects the area A at the corresponding time point. For the LDS problem, we are given a string s as input and want to output a longest substring of s, such that the ratio of 1's in the substring is at least a certain threshold. In our model, LDS is a core problem for many applications that aim at detecting regularity of T intersecting A. We propose an optimal algorithm to solve LDS, and also for related problems that are closer to applications, we provide efficient algorithms for detecting regularity.
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