Genetic algorithms for the two-stage bicriteria flowshop problem

Neppalli, Venkata R; Chen, Chuen-Lung; Gupta, Jatinder N. D.

doi:10.1016/0377-2217(95)00275-8

Cited by 96 publications

(37 citation statements)

References 25 publications

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“…However, the proposed methods are shown to solve 24 jobs maximum. In Neppalli et al 16 two genetic algorithms were proposed for solving the two machine bicriteria flow-shop problem also in a lexicographical way as in Rajendran 14 . The first algorithm is based in the VEGA (Vector Evaluated Genetic Algorithm) of Schafer 17 .…”

Section: Lexicographic Approachesmentioning

confidence: 99%

“…The criteria to optimize are composed of several lexicographic pairs involving makespan, weighted flowtime and weighted tardiness. The proposed methods are compared against the GA of Neppalli et al 16 and the results discussed. Gupta et al 20 proposed nine heuristics for the two machine case minimizing flowtime subject to optimum makespan, i.e., Lex (C max , F).…”

Section: Lexicographic Approachesmentioning

confidence: 99%

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Review on Job-Shop and Flow-Shop Scheduling Using

Parveen

Ullah

2011

J. mech. eng.

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Section: Lexicographic Approachesmentioning

confidence: 99%

Section: Lexicographic Approachesmentioning

confidence: 99%

Review on Job-Shop and Flow-Shop Scheduling Using

Parveen

Ullah

2011

J. mech. eng.

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“…• bacterial foraging optimization assisted evolutionary algorithm (Kim & Cho, 2005;Neppalli & Chen, 1996),…”

Section: How To Hybridize the Self-adaptive Evolutionary Algorithmsmentioning

confidence: 99%

Hybridization of Evolutionary Algorithms

Fister¹,

Mernik²,

Brest³

2011

Evolutionary Algorithms

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“…If prior knowledge exists or can be generated at a low computational cost, with good initial estimates may generate better solutions with faster convergence [20,24,38,52,70].…”

Section: Evolutionary Algorithms Incorporating Prior Knowledgementioning

confidence: 99%

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

Groşan

Abraham

2007

Studies in Computational Intelligence

150

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Summary. Evolutionary computation has become an important problem solving methodology among many researchers. The population-based collective learning process, selfadaptation, and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques. Even though evolutionary computation has been widely accepted for solving several important practical applications in engineering, business, commerce, etc., yet in practice sometimes they deliver only marginal performance. Inappropriate selection of various parameters, representation, etc. are frequently blamed. There is little reason to expect that one can find a uniformly best algorithm for solving all optimization problems. This is in accordance with the No Free Lunch theorem, which explains that for any algorithm, any elevated performance over one class of problems is exactly paid for in performance over another class. Evolutionary algorithm behavior is determined by the exploitation and exploration relationship kept throughout the run. All these clearly illustrates the need for hybrid evolutionary approaches where the main task is to optimize the performance of the direct evolutionary approach. Recently, hybridization of evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty, and vagueness. In this chapter, first we emphasize the need for hybrid evolutionary algorithms and then we illustrate the various possibilities for hybridization of an evolutionary algorithm and also present some of the generic hybrid evolutionary architectures that has evolved during the last couple of decades. We also provide a review of some of the interesting hybrid frameworks reported in the literature.

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Genetic algorithms for the two-stage bicriteria flowshop problem

Cited by 96 publications

References 25 publications

Review on Job-Shop and Flow-Shop Scheduling Using

Review on Job-Shop and Flow-Shop Scheduling Using

Hybridization of Evolutionary Algorithms

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

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