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Title: Nature inspired metaheuristic algorithms for solving permutation flow shop scheduling problems
Researcher: Sanjeev kumar R
Keywords: Engineering and Technology,Engineering,Engineering Mechanical
Flow shop
University: Anna University
Completed Date: 2018
Abstract: Scheduling is a decision making process with a view to optimizing newlineone or more scheduling objectives. Flow shop scheduling is very important in newlineboth fields of production management and combinatorial optimization. In newlineproduction scheduling, permutation flow shop scheduling is a part. Owing to the newlinehigh computational complexity, it is quite difficult to achieve an optimal solution newlinewith the traditional techniques. In this connection, suitable techniques (or) newlinealgorithms are required to solve the permutation flow shop scheduling problems. newlineIn this research, Nature Inspired algorithms are proposed to solve the newlinethree types of problems are permutation flow shop scheduling problems, newlinepermutation flow shop scheduling with setup time problems and permutation newlineflow shop scheduling with setup time and due date problems. The proposed NI newlinealgorithms are Artificial Bee Colony Algorithm, Artificial Immune System newlineAlgorithm and Modified Gravitational Emulation Local Search Algorithm. newlineThe effectiveness of the algorithm is measured in terms of Average newlineRelative Error Percentage by comparing the value of Artificial Bee Colony, newlineArtificial Immune System and Modified Gravitational Emulation Local Search newlineAlgorithms for the bench mark problem instances. MGELS algorithm produced newlinebetter results for all the three types of problems and its total AREP value is 5.20. newlineANNOVA method is used to conduct the statistical test. The statistical result newlineshows that there is a significant optimal solution difference in the algorithms for newlinevarious problems. newline newline
Pagination: xiv, 121p.
Appears in Departments:Faculty of Mechanical Engineering

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02_certificates.pdf185.76 kBAdobe PDFView/Open
03_abstract.pdf4.8 kBAdobe PDFView/Open
04_acknowledgment.pdf19.48 kBAdobe PDFView/Open
05_contents.pdf195.2 kBAdobe PDFView/Open
06_chapter1.pdf323.27 kBAdobe PDFView/Open
07_chapter2.pdf147.66 kBAdobe PDFView/Open
08_chapter3.pdf333.6 kBAdobe PDFView/Open
09_chapter4.pdf208.55 kBAdobe PDFView/Open
10_chapter5.pdf606.23 kBAdobe PDFView/Open
11_chapter6.pdf589.89 kBAdobe PDFView/Open
12_conclusion.pdf12.08 kBAdobe PDFView/Open
13_references.pdf152.48 kBAdobe PDFView/Open
14_publications.pdf84.8 kBAdobe PDFView/Open

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