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Cantika Febrita
Muhammadiyah Malang University
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Low-carbon no-idle permutation flow shop schedulling problem: giant trevally optimizer vs African vultures optimization algorithm Dana Marsetiya Utama; Cantika Febrita
International Journal of Advances in Applied Sciences Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v12.i3.pp195-204

Abstract

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.
Co-Authors Dana Marsetiya Utama