Dynamic ranking function to optimize transshipment costs in intuitionistic Type-2 and Type-1 fuzzy environments

Systems and Soft Computing Pub Date : 2024-09-29 DOI:10.1016/j.sasc.2024.200153

Tarun Kumar , Sadhna Chaudhary , Kapil Kumar , Kailash Dhanuk , M.K. Sharma

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引用次数: 0

Abstract

In the dynamic realm of organizational logistics, accurately minimizing transportation and transshipment costs is crucial, yet often challenging due to inherent uncertainties. This paper introduces a novel application of fuzzy logic to provide a more precise analysis of these costs. Specifically, it develops an innovative ranking function for trapezoidal fuzzy numbers (TrFNs) for Type-2 and Type-1 fuzzy environments, a tool yet unexplored in existing literature. The main contributions of this paper are the idea that a ranking function for TrFNs can significantly improve decision-maker's freedom in cost analysis due to an adherence on all (a, b, c d) parameters of TrFN. A new decision-oriented ranking method for these fuzzy numbers is developed which consists of an inventive algorithm. The method is also considered for intuitionistic TrFNs and applied to solve transshipment costs in fuzzy area. To verify the proposed methodology's efficiency, effectiveness and accuracy a numerical example in Wolfram Mathematica 9.0 is demonstrated showing superior computational performance over existing methods.

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在直观 2 类和 1 类模糊环境中优化转运成本的动态排序函数

在动态的组织物流领域，准确地将运输和转运成本降至最低至关重要，但由于固有的不确定性，这往往具有挑战性。本文介绍了一种新颖的模糊逻辑应用，可对这些成本进行更精确的分析。具体来说，它为梯形模糊数 (TrFN) 开发了一种创新的排序功能，适用于 2 类和 1 类模糊环境，这是一种在现有文献中尚未开发的工具。本文的主要贡献在于：由于坚持使用梯形模糊数的所有（a, b, c d）参数，梯形模糊数的排序函数可以显著提高决策者在成本分析中的自由度。针对这些模糊数开发了一种新的面向决策的排序方法，其中包括一种创造性的算法。该方法也适用于直观 TrFN，并被应用于解决模糊区域的转运成本问题。为了验证所提方法的效率、有效性和准确性，在 Wolfram Mathematica 9.0 中演示了一个数值示例，显示出优于现有方法的计算性能。

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

Systems and Soft Computing

CiteScore

2.20

自引率

0.00%

发文量