A systematic literature review on energy-efficient hybrid flow shop scheduling

Utama, Dana Marsetiya; Primayesti, Meri Dines; Umamy, Sabila Zahra; Kholifa, Bunga Milenia Nur; Yasa, Arnelia Dwi

doi:10.1080/23311916.2023.2206074

Cited by 10 publications

(4 citation statements)

References 105 publications

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“…Some of these studies specifically discuss No-idle, a research Al-Imron et al [28] that aims to minimize energy consumption using the Grey Wolf Optimizer Algorithm. Some studies also analyze Flow Shop Scheduling by using a multi-operator hybrid genetic algorithm [29], multiobjective distributed reentrant permutation flow shop scheduling with sequence-dependent setup time [30], A decision support system for road freight transportation route selection with new fuzzy numbers [31], A systematic literature review on energy-efficient hybrid flow shop scheduling [32], A novel hybrid Archimedes optimization algorithm for energy-efficient hybrid flow shop scheduling [5], and design of decision support system for road freight transportation routing using multilayer zero-one goal programming [33]. Previous research that addresses NIPFSP issues is presented in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous algorithms have been proposed to solve NIPFSP, as demonstrated by some research cited above. Many of these algorithms are inspired by animals' hunting and prey-searching behavior [32]. AO has been employed in several previous studies to address various problems.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, this study aims to modify the AO algorithm to reduce NIPFSP energy consumption. An optimal energy consumption value using the AO algorithm will be compared with the Grey Wolf Optimizer (GWO) algorithm [32]. Make span and energy consumption are the objective functions used in this research [34].…”

Section: Introductionmentioning

confidence: 99%

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A modified Aquila optimizer algorithm for optimization energy-efficient no-idle permutation flow shop scheduling problem

Utama,

Sanafa

2023

j. sist. manaj. ind.

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Increasing energy consumption has faced challenges and pressures for modern manufacturing operations. The production sector accounts for half of the world's total energy consumption. Reducing idle machine time by employing No-Idle Permutation Flow Shop Scheduling (NIPFSP) is one of the best decisions for reducing energy consumption. This article modifies one of the energy consumption-solving algorithms, the Aquila Optimizer (AO) algorithm. This research contributes by 1) proposing novel AO procedures for solving energy consumption problems with NIPFSP and 2) expanding the literature on metaheuristic algorithms that can solve energy consumption problems with NIPFSP. To analyze whether the AO algorithm is optimal, we compared by using the Grey Wolf Optimizer (GWO) algorithm. It compares these two algorithms to tackle the problem of energy consumption by testing four distinct problems. Comparison of the AO and GWO algorithm is thirty times for each case for each population and iteration. The outcome of comparing the two algorithms is using a t-test on independent samples and ECR. In all case studies, the results demonstrate that the AO algorithm has a lower energy consumption value than GWO. The AO algorithm is therefore recommended for minimizing energy consumption because it can produce more optimal results than the comparison algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A modified Aquila optimizer algorithm for optimization energy-efficient no-idle permutation flow shop scheduling problem

Utama,

Sanafa

2023

j. sist. manaj. ind.

Self Cite

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“…The high use of carbon emissions has become a concern in the world, and one of the sectors that contribute the highest carbon emissions is the manufacturing industry [1]. According to research [2], high carbon emissions can be caused by high energy consumption from machine operation [3], and the energyefficiency problems in the manufacturing sector are closely related to scheduling [4]. There are two practical techniques to reduce carbon emissions in manufacturing: using energy-efficient equipment [5] and implementing an optimal low-carbon production strategy [6].…”

Section: Introductionmentioning

confidence: 99%

Low-carbon no-idle permutation flow shop schedulling problem: giant trevally optimizer vs African vultures optimization algorithm

Marsetiya Utama,

Febrita

2023

IJAAS

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Greenhouse gas emissions continue to increase due to increased energy consumption. One of the largest emission-contributing sectors is the manufacturing industry. Therefore, the manufacturing industry is required to minimize carbon emissions. One of the efforts to solve the emission problem is to minimize machine downtime throughout the production procedure, which stands for no-idle permutation flowshop scheduling (NIPFSP). This article uses two metaheuristic algorithms, giant trevally optimizer (GTO) and African vultures optimization algorithm (AVOA), to solve the carbon emission problem. Both algorithms are tested on 3 cases with 30 runs for every population and iteration. To compare the outcomes of each algorithm, an independent sample t-test was employed. The results show that the GTO algorithm has better results than the AVOA algorithm on small and large case data. The findings indicate that both the GTO and AVOA algorithms yield comparable results when applied to medium-sized research datasets, suggesting their effectiveness in such scenarios.

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