Multi-objective interval type-2 fuzzy linear programming problem with vagueness in coefficient

IF 8.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shokouh Sargolzaei, Hassan Mishmast Nehi
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Abstract

One of the most widely used fuzzy linear programming models is the multi-objective interval type-2 fuzzy linear programming (IT2FLP) model, which is of particular importance due to the simultaneous integration of multiple criteria and objectives in a single problem, the fuzzy nature of this type of problems, and thus, its closer similarity to real-world problems. So far, many studies have been done for the IT2FLP problem with uncertainties of the vagueness type. However, not enough studies have been done regarding the multi-objective interval type-2 fuzzy linear programming (MOIT2FLP) problem with uncertainties of the vagueness type. As an innovation, this study investigates the MOIT2FLP problem with vagueness-type uncertainties, which are represented by membership functions (MFs) in the problem. Depending on the localisation of vagueness in the problem, that is, vagueness in the objective function vector, vagueness in the technological coefficients, vagueness in the resources vector, and any possible combination of them, various problems may arise. Furthermore, to solve problems with MOIT2FLP, first, using the weighted sum method as an efficient and effective method, each of the MOIT2FLP problems is converted into a single-objective problem. In this research, these types of problems are introduced, their MFs are stated, and different solution methods are suggested. For each of the proposed methods, the authors have provided an example and presented the results in the corresponding tables.

Abstract Image

系数模糊的多目标区间 2 型模糊线性规划问题
多目标区间-2 型模糊线性规划(IT2FLP)模型是应用最广泛的模糊线性规划模型之一,由于在单一问题中同时集成多个标准和目标、这类问题的模糊性质以及与现实世界问题的相似性,该模型显得尤为重要。迄今为止,针对具有模糊类型不确定性的 IT2FLP 问题已经进行了很多研究。然而,对于具有模糊类型不确定性的多目标区间 2 型模糊线性规划(MOIT2FLP)问题的研究还不够多。作为一项创新,本研究探讨了具有模糊型不确定性的 MOIT2FLP 问题,问题中的模糊型不确定性由成员函数(MF)表示。根据问题中模糊性的定位,即目标函数向量中的模糊性、技术系数中的模糊性、资源向量中的模糊性以及它们的任何可能组合,可能会出现各种问题。此外,要利用 MOIT2FLP 解决问题,首先要利用加权和法这一高效方法,将 MOIT2FLP 的每个问题转化为单目标问题。本研究介绍了这些类型的问题,阐述了它们的 MF,并提出了不同的求解方法。对于每种建议的方法,作者都提供了一个示例,并在相应的表格中给出了结果。
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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
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