scholarly journals Comparative Analysis of the Simple WISP and Some Prominent MCDM Methods: A Python Approach

Axioms ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 347
Author(s):  
Dragiša Stanujkić ◽  
Darjan Karabašević ◽  
Gabrijela Popović ◽  
Edmundas Kazimieras Zavadskas ◽  
Muzafer Saračević ◽  
...  
Keyword(s):  
Decision Making ◽  
Comparative Analysis ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Weighted Sum ◽  
Python Programming Language ◽  
Decision Making Problem ◽  
Product Method ◽  
Comparison Of The Results ◽  
Python Programming

This article presents a comparison of the results obtained using the newly proposed Simple Weighted Sum Product method and some prominent multiple criteria decision-making methods. For comparison, several analyses were performed using the Python programming language and its NumPy library. The comparison was also made on a real decision-making problem taken from the literature. The obtained results confirm the high correlation of the results obtained using the Simple Weighted Sum Product method and selected multiple criteria decision-making methods such as TOPSIS, SAW, ARAS, WASPAS, and CoCoSo, which confirms the usability of the Simple Weighted Sum Product method for solving multiple criteria decision-making problems.

scholarly journals Generalized Neutrosophic Soft Expert Set for Multiple-Criteria Decision-Making

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 437 ◽  
Author(s):  
Vakkas Uluçay ◽  
Memet Şahin ◽  
Nasruddin Hassan
Keyword(s):  
Decision Making ◽  
Comparative Analysis ◽  
Decision Process ◽  
Multiple Criteria Decision Making ◽  
Aggregation Operator ◽  
Multiple Criteria ◽  
Neutrosophic Set ◽  
Decision Making Problem

Smarandache defined a neutrosophic set to handle problems involving incompleteness, indeterminacy, and awareness of inconsistency knowledge, and have further developed it neutrosophic soft expert sets. In this paper, this concept is further expanded to generalized neutrosophic soft expert set (GNSES). We then define its basic operations of complement, union, intersection, AND, OR, and study some related properties, with supporting proofs. Subsequently, we define a GNSES-aggregation operator to construct an algorithm for a GNSES decision-making method, which allows for a more efficient decision process. Finally, we apply the algorithm to a decision-making problem, to illustrate the effectiveness and practicality of the proposed concept. A comparative analysis with existing methods is done and the result affirms the flexibility and precision of our proposed method.

A New Optimization Method for Supplier Evaluation in the Context of Volume Discount

2011 ◽  
pp. 64-75
Author(s):  
Reza Farzipoor Saen
Keyword(s):  
Decision Making ◽  
Supplier Selection ◽  
Selection Process ◽  
Multiple Criteria Decision Making ◽  
Optimization Method ◽  
Multiple Criteria ◽  
Supplier Evaluation ◽  
Imprecise Data ◽  
Common Criteria ◽  
Decision Making Problem

Supplier selection is a multiple criteria decision making problem that the selection process mainly involves evaluating a number of suppliers according to a set of common criteria for selecting suppliers to meet business needs. Suppliers usually offer volume discounts to encourage the buyers to order more. To select suppliers in the presence of both volume discounts and imprecise data, this chapter proposes an optimization method. A numerical example demonstrates the application of the proposed method.

Comparative analysis of evolutionary algorithms for multiple criteria decision making with interval-valued belief distributions

2020 ◽  
Vol 14 (3) ◽  
pp. 373-391
Author(s):  
Guangyan Lu ◽  
Wenjun Chang
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Comparative Analysis ◽  
Evolutionary Algorithms ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Swarm Optimization ◽  
Interval Valued

In multiple criteria decision making (MCDM) with interval-valued belief distributions (IVBDs), individual IVBDs on multiple criteria are combined explicitly or implicitly to generate the expected utilities of alternatives, which can be used to make decisions with the aid of decision rules. To analyze an MCDM problem with a large number of criteria and grades used to profile IVBDs, effective algorithms are required to find the solutions to the optimization models within a large feasible region. An important issue is to identify an algorithm suitable for finding accurate solutions within a limited or acceptable time. To address this issue, four representative evolutionary algorithms, including genetic algorithm, differential evolution algorithm, particle swarm optimization algorithm, and gravitational search algorithm, are selected to combine individual IVBDs of alternatives and generate the minimum and maximum expected utilities of alternatives. By performing experiments with different numbers of criteria and grades, a comparative analysis of the four algorithms is provided with the aid of two indicators: accuracy and efficiency. Experimental results indicate that particle swarm optimization algorithm is the best among the four algorithms for combining individual IVBDs and generating the minimum and maximum expected utilities of alternatives.

scholarly journals MOORA under Pythagorean Fuzzy Set for Multiple Criteria Decision Making

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Luis Pérez-Domínguez ◽  
Luis Alberto Rodríguez-Picón ◽  
Alejandro Alvarado-Iniesta ◽  
David Luviano Cruz ◽  
Zeshui Xu
Keyword(s):  
Decision Making ◽  
Multiobjective Optimization ◽  
Fuzzy Set ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Decision Makers ◽  
Ratio Analysis ◽  
Pythagorean Fuzzy Set ◽  
Decision Making Problem ◽  
Moora Method

The multiobjective optimization on the basis of ratio analysis (MOORA) method captures diverse features such as the criteria and alternatives of appraising a multiple criteria decision-making (MCDM) problem. At the same time, the multiple criteria problem includes a set of decision makers with diverse expertise and preferences. In fact, the literature lists numerous approaches to aid in this problematic task of choosing the best alternative. Nevertheless, in the MCDM field, there is a challenge regarding intangible information which is commonly involved in multiple criteria decision-making problem; hence, it is substantial in order to advance beyond the research related to this field. Thus, the objective of this paper is to present a fused method between multiobjective optimization on the basis of ratio analysis and Pythagorean fuzzy sets for the choice of an alternative. Besides, multiobjective optimization on the basis of ratio analysis is utilized to choose the best alternatives. Finally, two decision-making problems are applied to illustrate the feasibility and practicality of the proposed method.

New Method for Solving a General Multiple Criteria Decision-Making Problem Under Risk in Fuzzy Environment

2016 ◽  
Vol 15 (05) ◽  
pp. 1157-1179 ◽  
Author(s):  
N. Thillaigovindan ◽  
S. Anita Shanthi ◽  
J. Vadivel Naidu
Keyword(s):  
Decision Making ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Fuzzy Soft Set ◽  
Score Functions ◽  
Fuzzy Environment ◽  
Criterion Weight ◽  
Decision Making Problem ◽  
Criteria Weights ◽  
Interval Valued

This paper considers a multiple criteria decision-making (MCDM) problem under risk in fuzzy environment in its general form. There are m alternatives which need to be ranked on the basis of a set of n criteria. The alternatives and the criteria are evaluated based on a set of l characteristics. The entire data is presented in the form of interval valued intuitionistic fuzzy soft set of root type. In addition each criterion is assigned a subjective criterion weight based on expert’s evaluation and each characteristic is assigned a probability weight on the basis of decision maker’s knowlege and understanding of the importance of the characteristic. This problem may be called as a MCDM problem under risk in fuzzy environment in its general form. A method for ranking the alternatives using the new score functions, prospect theory and method of determining the optimum criteria weights is explained. An algorithm is developed for this purpose and its working illustrated with a suitable example.

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