The impact of task complexity on information use in multi-attribute decision making

1993 ◽  
Vol 6 (2) ◽  
pp. 95-111 ◽  
Author(s):  
Danielle Timmermans
1998 ◽  
Vol 19 (3) ◽  
pp. 196-206 ◽  
Author(s):  
Marie‐Laure Bouchet ◽  
Tracy Hopkins ◽  
Margaret Kinnell ◽  
Cliff McKnight

2020 ◽  
Vol 25 (2) ◽  
pp. 26 ◽  
Author(s):  
Muhammad Akram ◽  
Shumaiza ◽  
José Alcantud

The Analytical Hierarchy Process (AHP) is arguably the most popular and factual approach for computing the weights of attributes in the multi-attribute decision-making environment. The Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) is an outranking family of multi-criteria decision-making techniques for evaluating a finite set of alternatives, that relies on multiple and inconsistent criteria. One of its main advantages is the variety of admissible preference functions that can measure the differences between alternatives, in response to the type and nature of the criteria. This research article studies a version of the PROMETHEE technique that encompasses multipolar assessments of the performance of each alternative (relative to the relevant criteria). As is standard practice, first we resort to the AHP technique in order to quantify the normalized weights of the attributes by the pairwise comparison of criteria. Afterwards the m-polar fuzzy PROMETHEE approach is used to rank the alternatives on the basis of conflicting criteria. Six types of generalized criteria preference functions are used to measure the differences or deviations of every pair of alternatives. A partial ranking of alternatives arises by computing the positive and negative outranking flows of alternatives, which is known as PROMETHEE I. Furthermore, a complete ranking of alternatives is achieved by the inspection of the net flow of alternatives, and this is known as PROMETHEE II. Two comparative analysis are performed. A first study checks the impact of different types of preference functions. It considers the usual criterion preference function for all criteria. In addition, we compare the technique that we develop with existing multi-attribute decision-making methods.


2021 ◽  
Author(s):  
Yaojun Ren ◽  
Xiujiu Yuan ◽  
Ruojing Lin

Abstract BackgroundWith the rapid development of economy and the acceleration of urbanization, the garbage produced by urban residents also increases with the increase of population. In many big cities, the phenomenon of "garbage siege" has seriously affected the development of cities and the lives of residents. Sanitary landfill is an important way of municipal solid waste disposal. However, due to the restriction of social, environmental and economic conditions, landfill site selection has become a very challenging task. In addition, landfill site selection is full of uncertainty and complexity due to the lack of cognitive ability of decision-makers and the existence of uncertain information in the decision-making process.MethodsA novel multi-attribute decision making method based on q-rung orthopair probabilistic hesitant fuzzy power weight Muirhead mean (q-ROPHFPWMM) operator is proposed in this paper, which can solve the problem of landfill site selection well. This method uses probability to represent the hesitance of decision maker and retains decision information more comprehensively. The negative effect of abnormal data on the decision result is eliminated by using the power average operator. Muirhead mean operator is used to describe the correlation between attributes. ResultsAn example of landfill site selection is given to verify the effectiveness of the proposed method, and the advantages of the proposed method are illustrated by parameter analysis and comparative analysis. ConclusionThe q-ROPHFPWMM operator can describe the correlation between any evaluation factors and effectively reduce the impact of unreasonable evaluation information given by decision makers on the results. In addition, this method has a wider space for information expression, gives the decision maker a great degree of freedom in decision-making.


Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 459 ◽  
Author(s):  
Qaisar Khan ◽  
Peide Liu ◽  
Tahir Mahmood ◽  
Florentin Smarandache ◽  
Kifayat Ullah

The power Bonferroni mean (PBM) operator is a hybrid structure and can take the advantage of a power average (PA) operator, which can reduce the impact of inappropriate data given by the prejudiced decision makers (DMs) and Bonferroni mean (BM) operator, which can take into account the correlation between two attributes. In recent years, many researchers have extended the PBM operator to handle fuzzy information. The Dombi operations of T-conorm (TCN) and T-norm (TN), proposed by Dombi, have the supremacy of outstanding flexibility with general parameters. However, in the existing literature, PBM and the Dombi operations have not been combined for the above advantages for interval-neutrosophic sets (INSs). In this article, we first define some operational laws for interval neutrosophic numbers (INNs) based on Dombi TN and TCN and discuss several desirable properties of these operational rules. Secondly, we extend the PBM operator based on Dombi operations to develop an interval-neutrosophic Dombi PBM (INDPBM) operator, an interval-neutrosophic weighted Dombi PBM (INWDPBM) operator, an interval-neutrosophic Dombi power geometric Bonferroni mean (INDPGBM) operator and an interval-neutrosophic weighted Dombi power geometric Bonferroni mean (INWDPGBM) operator, and discuss several properties of these aggregation operators. Then we develop a multi-attribute decision-making (MADM) method, based on these proposed aggregation operators, to deal with interval neutrosophic (IN) information. Lastly, an illustrative example is provided to show the usefulness and realism of the proposed MADM method. The developed aggregation operators are very practical for solving MADM problems, as it considers the interaction among two input arguments and removes the influence of awkward data in the decision-making process at the same time. The other advantage of the proposed aggregation operators is that they are flexible due to general parameter.


2019 ◽  
Vol 31 (4) ◽  
pp. 558-577 ◽  
Author(s):  
Lotta-Maria Sinervo ◽  
Petra Haapala

Purpose The highest decision-making body in a municipality is the council, whose members are elected every fourth year. Therefore, local politicians are in the key position in using financial information in decision making. The purpose of this paper is to study Finnish local politicians’ use of financial information. Design/methodology/approach This paper studies the speech of local politicians as they describe financial operations and financial position of the municipalities they represent. Here, the words of local politicians are considered an expression of their use of financial information. The use of financial information includes its impact on decision making. The focus is on the impact of different individual characteristics of financial information use in speech about financial operations and financial position. The paper employs qualitative empirical data collected through semi-structured thematic interviews with members of the Finnish local government. Data are analyzed with descriptive statistical methods. Findings Local politicians use financial information when they talk about financial operations and financial position of their municipality. Individual characteristics, such as political experience, affect the use of financial information. Experienced local politicians are familiar with financial information and use it more systematically than inexperienced politicians. Contrasted to expectations, financial expertise has a negative impact on financial information use. Moreover, in general, the ideology of politicians does not affect their use of financial information. Originality/value The paper sheds light on the value of individual characteristics of financial information use. It develops and tests hypotheses based on previous research on the impact of individual attributes of local politicians in the Finnish local government.


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