Permutation, no-wait, no-idle flow shop problems

Makuchowski, Mariusz

doi:10.1515/acsc-2015-0012

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Thus, the start of a job on the first machine may be delayed to satisfy the no-wait constraint. (Guevara-Guevara, Gómez-Fuentes, Posos-Rodríguez, Remolina-Gómez, & González-Neira, 2022) The difference between FSSP and NWPFSP in Figures 1 and 2, illustrated by Makuchowski (2015). (5)…”

Section: Methodsmentioning

confidence: 99%

No-Wait Flowshop Permutation Scheduling Problem : Fire Hawk Optimizer Vs Beluga Whale Optimization Algorithm

Baihaqi,

Utama

2023

JITI

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No-Wait Flowshop Permutation Scheduling Problem (NWPFSP) is a scheduling problem that states that every job completed on machine n must be processed immediately on the next machine. The NWPFSP problem is an extension of the flowshop problem. This article proposes two new algorithms fire hawk optimization and beluga whale optimization, to solve the NWPFSP problem and minimize makespan. The two new algorithms developed to solve the NWPFSP problem are tested on three different cases. Each algorithm was run 30 times and was compared using an independent sample t-test. The results were also compared with the Campbell Dudek Smtih algorithm. In addition, the effectiveness of the FHO and BWO algorithms was assessed against the CDS algorithm using the Relative Error Percentage (REP) method. The results show that the FHO and BWO algorithms are better at solving NWPFSP problems when compared to the CDS algorithm. However, the BWO algorithm is more recommended in cases with large data because it can provide better results.

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Section: Methodsmentioning

confidence: 99%

No-Wait Flowshop Permutation Scheduling Problem : Fire Hawk Optimizer Vs Beluga Whale Optimization Algorithm

Baihaqi,

Utama

2023

JITI

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“…Optimally solved Xu ( 2011) Kalczynski and Kamburowski (2007) Relationship between no-wait and no-idle F m|prmu|Cmax, F m|prmu, no-wait|Cmax and F m|prmu, no-idle|Cmax Makuchowski (2015) Table 5: Summary of waiting time and idle time as constraints Proof. Let S s be the semi-active schedule for π. Then,…”

Section: Semi-active Schedule Assumptionmentioning

confidence: 99%