A Brief Review on Multi-Attribute Decision Making in the Emerging Fields of Computer Science

Nath, Satyabrata; Das, Purnendu; Debnath, Pradip

doi:10.1007/978-3-031-10766-5_1

Cited by 1 publication

(1 citation statement)

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“…Mathematical optimization, an area of applied mathematics, is used to solve real-life issues by generating mathematical models to produce feasible outcomes. In today's world the significance of mathematical optimization and decision making can be explored in various fields [1][2][3][4][5]. Geometric Programming (GP) is a technique in the field of mathematical optimization and multi-objective decision making that is considered a significant optimization problem consisting of objective functions and constraints composed of monomials or posynomials that are designed to solve realworld engineering problems by generating feasible outcomes [6].…”

Section: Introductionmentioning

confidence: 99%

A Single-valued Pentagonal Neutrosophic Geometric Programming Approach to Optimize Decision Maker’s Satisfaction Level

Nath¹,

Das²,

Debnath³

2023

IJACSA

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Achieving the desired level of satisfaction for a decision-maker in any decision-making scenario is considered a challenging endeavor because minor modifications in the process might lead to incorrect findings and inaccurate decisions. In order to maximize the decision-maker's satisfaction, this paper proposes a Single-valued Neutrosophic Geometric Programming model based on pentagonal fuzzy numbers. The decision-maker is typically assumed to be certain of the parameters, but in reality, this is not the case, hence the parameters are presented as neutrosophic fuzzy values. The decision-maker, with this strategy, is able to achieve varying levels of satisfaction and dissatisfaction for each constraint and even complete satisfaction for certain constraints. Here the decision maker aims to achieve the maximum level of satisfaction while maintaining the level of hesitation and minimizing dissatisfaction in order to retain an optimum solution. Furthermore, transforming the objective function into a constraint adds one more layer to the Ndimensional multi-parametrizes and . The advantages of this multi-parametrized proposed method over the existing ones are proven using numerical examples.

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