A variant of the permutation flow shop model with variable processing times

Finke, Gerd; Jiang, Houmin

doi:10.1016/s0166-218x(96)00121-7

Cited by 10 publications

(3 citation statements)

References 11 publications

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“…We have generalized the linear penalties for job overlapping and job delays to continuous non-decreasing functions. Also extensions to m-machine flowshops are possible (Espinouse, 1998;Finke and Jiang, 1997;Jiang, 1997). For the case of job deterioration, the optimal placement algorithms are essentially the same.…”

Section: Resultsmentioning

confidence: 99%

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General Flowshop Models: Job Dependent Capacities, Job Overlapping and Deterioration

Finke¹,

Espinouse²,

Jiang³

2002

Int Trans Operational Res

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Section: Resultsmentioning

confidence: 99%

“…We summarize briefly the results for C max from Finke and Jiang (1997) and report in detail the optimal placement algorithm for L max (these results are unpublished and part of Jiang (1997)). …”

Section: Job Deteriorationmentioning

confidence: 99%

General Flowshop Models: Job Dependent Capacities, Job Overlapping and Deterioration

Finke¹,

Espinouse²,

Jiang³

2002

Int Trans Operational Res

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“…The PFSP has been solved with different methods. For example, in [11] a greedy heuristic was used while in [8] and [10] DBA and B&B (branch and bound) algorithms were used respectively. The use of a GA (Genetic Algorithm) was explored in [12] with significant results.…”

Section: The Ga Methodsmentioning

confidence: 99%

An Evolutionary Approach for Solving the N-Jobs M-Machines Permutation Flow-Shop Scheduling Problem with Break-Down Times

Rosas-gonzález¹,

Clemente-guerrero²,

Caballero‐Morales³

et al. 2013

IJCA

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In this paper a Genetic Algorithm (GA) approach is presented to solve the N-Jobs M-Machines Permutation Flow-Shop Scheduling Problem (PFSP) with Breakdown times. In comparison with other methods that start with a solution obtained with the Johnson's Algorithm (or another greedy approach), the presented GA method starts with randomly generated solutions and within 100 iterations is able to obtain a solution better than other methods. Also, while in other works the sequence of jobs to be processed in the machines is obtained prior to the occurrence of breakdown times, the GA finds the solution considering from the beginning the occurrence of the breakdown times. Thus, the presented GA method considers the effect of the breakdown times in the overall process. A selection of standard 20×20 PFSPs was used for validation of the GA, finding that in 86% of the selected PFSPs the GA was able to provide job sequences with better makespans when compared with another method. The makespan improvements were statistically significant at the 0.10 and 0.01 levels. Then, evaluation of the GA was performed on one PFSP case with breakdown times. As in the validation cases with standard PFSPs, the GA outperformed the results obtained with another method.

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Flow Shop Scheduling Problem

Marinaki¹

2008

Encyclopedia of Optimization

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A variant of the permutation flow shop model with variable processing times

Cited by 10 publications

References 11 publications

General Flowshop Models: Job Dependent Capacities, Job Overlapping and Deterioration

General Flowshop Models: Job Dependent Capacities, Job Overlapping and Deterioration

An Evolutionary Approach for Solving the N-Jobs M-Machines Permutation Flow-Shop Scheduling Problem with Break-Down Times

Flow Shop Scheduling Problem

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