Solution of transportation problems under Pythagorean fuzzy framework using new score function

Sarita Gahlawat, Rajkumar Verma, Geeta Sachdev, Shalini Arora
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Abstract

The transportation problem is one of the most significant mathematical programming applications that appears in various real-world decision-making problems. In an actual scenario, the supply, demand, and cost parameters of a transportation problem cannot be exactly quantified due to market instability. To deal with such types of impreciseness, the researchers have widely used fuzzy numbers and their extensions. Pythagorean fuzzy set theory is a prominent tool for handling uncertain and vague information in complex decision-making situations. This paper aims to develop a solution approach to solve the transportation problem with uncertainty in input parameters by incorporating Pythagorean fuzzy numbers. To do so, first, a new score function is proposed to rank Pythagorean fuzzy numbers more efficiently. A comparative study highlights some flaws in existing score functions, which depicts the advantages of the proposed score function over existing ones. Afterward, we solve the Pythagorean fuzzy transportation problem using the proposed score function. The solution technique is demonstrated with the help of some numerical examples. In addition, a comparative study is also included to show the efficacy of the proposed approach over existing ones.

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在毕达哥拉斯模糊框架下利用新的得分函数解决运输问题
运输问题是最重要的数学程序设计应用之一,它出现在各种现实世界的决策问题中。在实际场景中,由于市场的不稳定性,运输问题的供应、需求和成本参数无法精确量化。为了解决这类不精确问题,研究人员广泛使用了模糊数及其扩展。毕达哥拉斯模糊集理论是在复杂决策情况下处理不确定和模糊信息的重要工具。本文旨在结合毕达哥拉斯模糊数,开发一种解决输入参数不确定的运输问题的方法。为此,首先提出了一种新的评分函数,以更有效地对毕达哥拉斯模糊数进行排序。通过比较研究,我们发现了现有评分函数的一些缺陷,从而发现了所提出的评分函数相对于现有评分函数的优势。随后,我们使用提出的评分函数解决了毕达哥拉斯模糊运输问题。借助一些数值示例演示了求解技术。此外,我们还进行了比较研究,以显示所提方法与现有方法相比的功效。
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