Intuitionistic Fuzzy Multi-Objective Transportation Model during Pandemic COVID-19

IF 0.7 4区数学 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Advances in Complex Systems Pub Date : 2023-10-05 DOI:10.1142/s1793962323500514

Divya Sharma, Dinesh C. S. Bisht, Pankaj Kumar Srivastava

引用次数: 0

Abstract

The given paper discusses an intuitionistic fuzzy discrete time-space optimization model while taking into account the COVID-19 pandemic. The novelty of this study is characterized by the proposal of a distinctive multi-objective intuitionistic fuzzy transportation model in which the demand function varies with time as the number of infected, exposed and susceptible persons increase. Forecasting of demand is executed using the epidemic diffusion model. Also, a new approach to defuzzify triangular intuitionistic fuzzy numbers, depending upon the concept of the golden ratio, is presented. Intuitionistic fuzzy programming approach is used to obtain the pareto-optimal solution. A real-life numerical illustration is explained using the proposed methodology to examine its practical suitability. Outperforming the existing traditional method of solving a transportation problem verifies its proficiency.

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COVID-19大流行期间的直觉模糊多目标运输模型

本文讨论了一种考虑COVID-19大流行的直觉模糊离散时空优化模型。本研究的新颖之处在于提出了一个独特的多目标直觉模糊运输模型，其中需求函数随着感染、暴露和易感人数的增加而随时间变化。利用疫情扩散模型对需求进行预测。同时，基于黄金分割的概念，提出了一种三角直觉模糊数去模糊化的新方法。采用直觉模糊规划方法求解pareto最优解。一个现实生活中的数值说明解释使用提出的方法，以检查其实际适用性。通过超越现有的求解交通问题的传统方法，验证了该方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Advances in Complex Systems 综合性期刊-数学跨学科应用

CiteScore

1.40

自引率

0.00%

发文量

121

审稿时长

6-12 weeks

期刊介绍： Advances in Complex Systems aims to provide a unique medium of communication for multidisciplinary approaches, either empirical or theoretical, to the study of complex systems. The latter are seen as systems comprised of multiple interacting components, or agents. Nonlinear feedback processes, stochastic influences, specific conditions for the supply of energy, matter, or information may lead to the emergence of new system qualities on the macroscopic scale that cannot be reduced to the dynamics of the agents. Quantitative approaches to the dynamics of complex systems have to consider a broad range of concepts, from analytical tools, statistical methods and computer simulations to distributed problem solving, learning and adaptation. This is an interdisciplinary enterprise.