A modified Aquila optimizer algorithm for optimization energy-efficient no-idle permutation flow shop scheduling problem
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 de...
| Main Authors: | , |
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| Format: | Article |
| Language: | Indonesian |
| Published: |
Universitas Serang Raya
2023-12-01
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| Series: | Jurnal Sistem dan Manajemen Industri |
| Subjects: | |
| Online Access: | https://e-jurnal.lppmunsera.org/index.php/JSMI/article/view/6446 |
| _version_ | 1797305055885393920 |
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| author | Dana Marsetiya Utama Nabilah Sanafa |
| author_facet | Dana Marsetiya Utama Nabilah Sanafa |
| author_sort | Dana Marsetiya Utama |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T00:19:25Z |
| format | Article |
| id | doaj.art-c3ba1e1f4ca744c8a5ecf73feaa26a21 |
| institution | Directory Open Access Journal |
| issn | 2580-2887 2580-2895 |
| language | Indonesian |
| last_indexed | 2024-03-08T00:19:25Z |
| publishDate | 2023-12-01 |
| publisher | Universitas Serang Raya |
| record_format | Article |
| series | Jurnal Sistem dan Manajemen Industri |
| spelling | doaj.art-c3ba1e1f4ca744c8a5ecf73feaa26a212024-02-16T14:10:15ZindUniversitas Serang RayaJurnal Sistem dan Manajemen Industri2580-28872580-28952023-12-01729511510.30656/jsmi.v7i2.64466446A modified Aquila optimizer algorithm for optimization energy-efficient no-idle permutation flow shop scheduling problemDana Marsetiya Utama0Nabilah Sanafa1Unirversity of Muhammadiyah MalangUniversity of Muhammadiyah MalangIncreasing 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.https://e-jurnal.lppmunsera.org/index.php/JSMI/article/view/6446aquila optimizer (ao)consumption energyflow shopno idle |
| spellingShingle | Dana Marsetiya Utama Nabilah Sanafa A modified Aquila optimizer algorithm for optimization energy-efficient no-idle permutation flow shop scheduling problem Jurnal Sistem dan Manajemen Industri aquila optimizer (ao) consumption energy flow shop no idle |
| title | A modified Aquila optimizer algorithm for optimization energy-efficient no-idle permutation flow shop scheduling problem |
| title_full | A modified Aquila optimizer algorithm for optimization energy-efficient no-idle permutation flow shop scheduling problem |
| title_fullStr | A modified Aquila optimizer algorithm for optimization energy-efficient no-idle permutation flow shop scheduling problem |
| title_full_unstemmed | A modified Aquila optimizer algorithm for optimization energy-efficient no-idle permutation flow shop scheduling problem |
| title_short | A modified Aquila optimizer algorithm for optimization energy-efficient no-idle permutation flow shop scheduling problem |
| title_sort | modified aquila optimizer algorithm for optimization energy efficient no idle permutation flow shop scheduling problem |
| topic | aquila optimizer (ao) consumption energy flow shop no idle |
| url | https://e-jurnal.lppmunsera.org/index.php/JSMI/article/view/6446 |
| work_keys_str_mv | AT danamarsetiyautama amodifiedaquilaoptimizeralgorithmforoptimizationenergyefficientnoidlepermutationflowshopschedulingproblem AT nabilahsanafa amodifiedaquilaoptimizeralgorithmforoptimizationenergyefficientnoidlepermutationflowshopschedulingproblem AT danamarsetiyautama modifiedaquilaoptimizeralgorithmforoptimizationenergyefficientnoidlepermutationflowshopschedulingproblem AT nabilahsanafa modifiedaquilaoptimizeralgorithmforoptimizationenergyefficientnoidlepermutationflowshopschedulingproblem |