Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/1648
Title: A fuzzy logic based methodology for multi-objective hybrid flow shop scheduling with multi-processor tasks problems and solving with an efficient genetic algorithm
Authors: Engin, Orhan
Yılmaz, Mustafa Kerim
Keywords: efficient genetic algorithm
fuzzy due date
fuzzy processing time
Hybrid flow shop scheduling
multi-processor tasks problems
simulated annealing
Computer circuits
Fuzzy logic
Genetic algorithms
Machine shop practice
Scheduling
Efficient genetic algorithms
Fuzzy due-date
Fuzzy processing time
Fuzzy-Logic
Hybrid flow shop scheduling
Logic-based methodology
Multi objective
Multi-processor task problem
Multiprocessor tasks
Scheduling problem
Simulated annealing
Issue Date: 2022
Publisher: IOS Press BV
Abstract: In the conventional scheduling problem, the parameters such as the processing time for each job and due dates are usually assumed to be known exactly, but in many real-world applications, these parameters may very dynamically due to human factors or operating faults. During the last decade, several works on scheduling problems have used a fuzzy approach including either uncertain or imprecise data. A fuzzy logic based tool for multi-objective Hybrid Flow-shop Scheduling with Multi-processor Tasks (HFSMT) problem is presented in this paper. In this study, HFSMT problems with a fuzzy processing time and a fuzzy due date are formulated, taking O?uz and Ercan's benchmark problems in the literature into account. Fuzzy HFSMT problems are formulated by three-objectives: the first is to maximize the minimum agreement index and the second is to maximize the average agreement index, and the third is to minimize the maximum fuzzy completion time. An efficient genetic algorithm(GA) is proposed to solve the formulated fuzzy HFSMT problems. The feasibility and effectiveness of the proposed method are demonstrated by comparing it with the simulated annealing (SA) algorithm in the literature. © 2022 - IOS Press. All rights reserved.
URI: https://doi.org/10.3233/JIFS-2191203
https://hdl.handle.net/20.500.13091/1648
ISSN: 10641246
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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