Improved makespan of the branch and bound solution for a fuzzy flow-shop scheduling problem using the maximization operator

Vinoba, V.; Selvamalar, N.

doi:10.26637/mjm0701/0018

Cited by 2 publications

(2 citation statements)

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“…In 1954, Johnson [4] developed a algorithm to solve a 2-machine flow shop problem which was a first leap towards the algorithmic approach to flow shop scheduling problems.In the literature,one could find the elaborate use of triangular and trapezoidal fuzzy numbers ( [7], [2], [3], [5]). Octagonal fuzzy numbers are also involved into active research in fuzzy flowshop scheduling by Selvamalar et al, ([8], [9], [10], [11], [12]). This paper deals with the Johnson's 2-machine flow shop problem in fuzzy environment and it has been extended to 3-machine case also under certain conditions.…”

Section: Introductionmentioning

confidence: 99%

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Extension of fuzzy Johnson algorithm to three machines fuzzy flow-shop scheduling problem

Vinoba¹,

Selvamalar²

2019

Malaya J. Mat

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Section: Introductionmentioning

confidence: 99%

“…,25) (0,2,4,6,8,10,12,14) (12,14,16,18,20,22,24,26) 2 (4,6,8,10,14,16,18,20) (6,7,10,11,12,15,16,19) (5,7,9,11,13,15,17 ,19) 3 (22,24,26,28,30,32,34,36) (3,5,7,9,13,15,17,19) (16,18,20,22,24,26,28,30) 4 (29,31,33,35,37,39,41,43) (0,2 12 12 w − Divergence(Ã) = ( f − c) 8 5…”

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Extension of fuzzy Johnson algorithm to three machines fuzzy flow-shop scheduling problem

Vinoba¹,

Selvamalar²

2019

Malaya J. Mat

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Multi-stage hybrid evolutionary algorithm for multiobjective distributed fuzzy flow-shop scheduling problem

Zhang

Hao

et al. 2023

MBE

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<abstract><p>In the current global cooperative production mode, the distributed fuzzy flow-shop scheduling problem (DFFSP) has attracted much attention because it takes the uncertain factors in the actual flow-shop scheduling problem into account. This paper investigates a multi-stage hybrid evolutionary algorithm with sequence difference-based differential evolution (MSHEA-SDDE) for the minimization of fuzzy completion time and fuzzy total flow time. MSHEA-SDDE balances the convergence and distribution performance of the algorithm at different stages. In the first stage, the hybrid sampling strategy makes the population rapidly converge toward the Pareto front (PF) in multiple directions. In the second stage, the sequence difference-based differential evolution (SDDE) is used to speed up the convergence speed to improve the convergence performance. In the last stage, the evolutional direction of SDDE is changed to guide individuals to search the local area of the PF, thereby further improving the convergence and distribution performance. The results of experiments show that the performance of MSHEA-SDDE is superior to the classical comparison algorithms in terms of solving the DFFSP.</p></abstract>

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Improved makespan of the branch and bound solution for a fuzzy flow-shop scheduling problem using the maximization operator

Cited by 2 publications

References 4 publications

Extension of fuzzy Johnson algorithm to three machines fuzzy flow-shop scheduling problem

Extension of fuzzy Johnson algorithm to three machines fuzzy flow-shop scheduling problem

Multi-stage hybrid evolutionary algorithm for multiobjective distributed fuzzy flow-shop scheduling problem

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