An algorithmic method to extend TOPSIS for decision-making problems with interval data

Jahanshahloo, G. R.; Lotfı, Farhad Hosseinzadeh; Izadikhah, Mohammad

doi:10.1016/j.amc.2005.08.048

Cited by 497 publications

(239 citation statements)

References 5 publications

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“…Practically, the TOPSIS and its extensions are used to solve many theoretical and real-world problems, such as decision making with fuzzy data [16] or interval data [17], decision support analysis for material selection of metallic bipolar plates [24], evaluating initial training aircraft under a fuzzy environment [29], or inter-company comparison [6]. The normalized matrix R is transformed to a weighted decision matrix Z such that z ij =w j ·r ij (1≤i≤m, 1≤j≤n), where w j denotes the weight of C j such that…”

Section: Topsismentioning

confidence: 99%

Extended TOPSISs for Belief Group Decision Making

Fu¹

2008

JSSM

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Section: Topsismentioning

confidence: 99%

Extended TOPSISs for Belief Group Decision Making

Fu¹

2008

JSSM

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“…Multi-Criteria Decision-Making (MCDM), also called as Multi-Criteria analysis, is often applied in the decision-making with multiple objectives in the field of Operations Research [1,2,3]. Especially, these objectives are conflicting with each other under the preference structure supplied by the decision-maker.…”

Section: Introductionmentioning

confidence: 99%

“…Handling the uncertainty by interval number is receiving considerable attention by the recent researchers. The Extended VIKOR method for decision making problem with interval numbers by M. K. Sayadi [11] is compared with the extended TOPSIS method proposed by Jahanshahloo [3] in obtaining the compromise solution.…”

Section: Introductionmentioning

confidence: 99%

“…There are many literatures correlate with the MCDM problems and the techniques for these problems in various applications [1,2,3,4,5,6,7,8,9]. These methods were divided into four categories by Guitoni and Martel given as follows [4,5]: (1) elementary approaches, including Lexicographic method, weighted sum, Disjunctive method, Conjunctive methods and Maxi-min method; (2) the single synthesizing criterion methods, including multi-attribute value theory (MAVT),TOPSIS, simple multi-attribute rating technique (SMART),UTA (utility theory additive), MAUT (multi-attribute utility theory), EVAMIX,AHP (analytical hierarchy process), Fuzzy maxi-min and Fuzzy weighted sum; (3) the outranking synthesizing methods, including PROMETHEE, ELECTRE, ORESTE ,MELCHIOR and REGIME; and (4) the mixed methods, including Fuzzy conjunctive method , Fuzzy disjunctive method, QUALIFLEX and Martel and Zaras method.…”

Section: Introductionmentioning

confidence: 99%

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MCDM based on Reciprocal Judgment Matrix: a Comparative Study of E-VIKOR and E-TOPSIS Algorithmic Methods with Interval Numbers

Dou¹,

Zhang²,

Jiang³

et al. 2014

Appl. Math. Inf. Sci.

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Abstract:Interval number is a useful tool to handle the uncertainty brought by human factors in multi-criteria decision-making (MCDM) process. When faced with MCDM problems in real-world, the experts may be uncomfortable giving precise upper and lower bounds of the interval ratios on multi-criteria. Based on the reciprocal judgment matrices given by the experts through pairwise comparisons, the satisfaction degree of the multiple alternatives on single criterion is defined and the interval ratios was elicited by a linear programming model. The TOPSIS and VIKOR methods are extended with interval number and algorithmic E-VIKOR and E-TOPSIS methods are proposed. Finally, a numerical example of ranking indirect-fire weapon system alternative is given and a comparative experimental study is carried out based on the experts reciprocal judgment matrices generated by Monte Carlo simulation. The result illustrates the feasibilities and distinctive features of the two algorithmic methods.

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“…In this field, issues like organizational performance, financial measurement, investment projects, customer satisfaction and competitive advantages are justified with TOPSIS. An algorithm was introduced to determine the most preferable choice among all possible choices to compare 15 bank branches in Iran based on financial ratios, when data was interval [8]. Chang, Lin, Lin, and Chiang (2010) proposed the extended TOPSIS method with different distance approaches to evaluate the performance of 82 Taiwanese mutual funds for almost three years.…”

Section: Introductionmentioning

confidence: 99%

Ranking and Aggregation of Factors Affecting Companies’ Attractiveness

Dikopoulou

Papageorgiou

Nápoles

et al. 2015

Proceedings of the 5th International Symposium on Knowledge Acquisition and Modeling

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Abstract-Today, more and more businesses are growing constantly trying to stand and maintain in the forefront of their competitive advantage by devising various ways to satisfy their customers or even to create a better "image" to make the company more attractive not only to the consumers but also to the future employees. The difficulty in this case is not the collection of the participants' preferences, but how the conducted knowledge will be reflect better the participants' consensus. Therefore, based on the above, each company will be more competitive, if the factors that influenced the company will be improved. In this paper, Factor Analysis was performed to ascertain which of the given factors are the most important. Then, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to rank and find the most significant factors that influence the public opinion when they are searching for a job, without expert knowledge. Two other Multi-criteria Decision Making methods the Weighted Sum Model (WSM) and Sum Ranking System (SRS), were considered to rank the factors and then all ranking results were compared. Finally, an aggregation of all evaluations was accomplished, extracting the people consensus.

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An algorithmic method to extend TOPSIS for decision-making problems with interval data

Cited by 497 publications

References 5 publications

Extended TOPSISs for Belief Group Decision Making

Extended TOPSISs for Belief Group Decision Making

MCDM based on Reciprocal Judgment Matrix: a Comparative Study of E-VIKOR and E-TOPSIS Algorithmic Methods with Interval Numbers

Ranking and Aggregation of Factors Affecting Companies’ Attractiveness

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