A new maximal flow algorithm for solving optimization problems with linguistic capacities and flows

Autor: Muhammad Akram, Amna Habib, Tofigh Allahviranloo
Přispěvatelé: İstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü, Tofigh Allahviranloo / 0000-0002-3929-9762, Allahviranloo, Tofigh, Tofigh Allahviranloo / V-4843-2019, Tofigh Allahviranloo / 8834494700
Rok vydání: 2022
Předmět:
Zdroj: Information Sciences. 612:201-230
ISSN: 0020-0255
DOI: 10.1016/j.ins.2022.08.068
Popis: The maximal flow problems (MFPs) are among the most significant optimization problems in network flow theory with widespread and diverse applications. To represent qualitative aspects of uncertainty in the maximal flow model, which asks for the largest amount of flow transported from one vertex to another, the use of linguistic variables has effective means for experts in expressing their views. In this paper, we first define trapezoidal Pythagorean fuzzy numbers (TrPFNs) along with some new arithmetic operations which cover the gaps in previously defined operations. For defuzzification of TrPFNs, we introduce a ranking procedure based on value and ambiguity indices. This work puts forward a the-oretical framework for a new Pythagorean fuzzy maximal flow algorithm (PFMFA), which helps to solve different optimization problems with PF information by considering linguis-tic capacities and flows. The implementation of the algorithm is elaborated by considering two case studies. Firstly, we examine the maximum flow of a water distribution pipeline network in Pyigyitagon Township, Mandalay, Myanmar. Secondly, we compute maximum PF power flow in a 14-bus electricity network provided by the IEEE working group, con-cerning the example data from the University of Washington. The results illustrate the superiority of the proposed method and give a detailed analysis of flow connected with several practical performances. In addition, the Pythagorean fuzzy optimal flows corre-sponding to each network arc are compared and performance comparison of our method is investigated which shows the increasing and decreasing trends of backward and forward arcs of the network, respectively. Moreover, the runtime analysis of existing well-known maximal flow algorithms is provided. Finally, we present the advantages of our technique to promote its cogency. (c) 2022 Elsevier Inc. All rights reserved. WOS:000863321400005 Q1
Databáze: OpenAIRE
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