Sri, Kurnia Dwi Budi Maulana (2023) Analisis Performansi Algoritma Grey Wolf Optimizer Dan Algoritma Genetika Untuk Model Persediaan Multi Supplier Multi Buyer Dengan Pertimbangan Biaya Transportasi. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Pengendalian persediaan terintegrasi perlu dilakukan banyak pertimbangan terkait batasan yang ada untuk menemukan nilai optimal. Hal ini merupakan hal yang tidak mudah bila dilakukan perhitungan secara manual. Dengan adanya algoritma metaheuristik sebagai alat optimasi, dapat membantu untuk menemukan keputusan optimal. Model persediaan yang digunakan adalah Multi-Supplier Multi-Buyer (MSMB), dimana fungsi objektifnya adalah untuk memaksimasi Joint Total Profit (JTP). Biaya transportasi untuk pengiriman bahan baku dari supplier dan produk jadi ke buyer dipertimbangkan secara eksplisit ke dalam model. Penelitian ini mengusulkan Algoritma Grey Wolf Optimizer (GWO) dan Algoritma Genetika (GA) yang diklaim pada penelitian lain terkait optimasi persediaan dapat memberikan performa yang baik. Analisis dalam penelitian ini menggunakan studi kasus pada perusahaan skala mikro yang disertai analisis sensitivitas dan perbandingan hasil optimasi antar algoritma. Hasil penelitian ini menunjukkan bahwa biaya transportasi memiliki pengaruh besar terhadap Joint Total Profit (JTP), dan GWO memiliki performansi yang lebih baik dibandingkan GA dalam mengoptimasi model persediaan MSMB.
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Integrated inventory control requires a lot of consideration of existing constraints to find the optimal value. This is something that is not easy when the calculations are done manually. With a metaheuristic algorithm as an optimization tool, it can help to find the optimal decision. The inventory model used is Multi-Supplier Multi-Buyer (MSMB), where the objective function is to maximize Joint Total Profit (JTP). Transportation costs for the delivery of raw materials from suppliers and finished products to buyers are considered explicitly in the model. This study proposes the Gray Wolf Optimizer (GWO) and Genetic Algorithm (GA) algorithms which are claimed in other studies regarding inventory optimization to provide good performance. The analysis in this study uses case studies in micro-scale companies accompanied by sensitivity analysis and comparison of optimization results between algorithms. The results of this study indicate that transportation costs have a large effect on Joint Total Profit (JTP), and GWO has a better performance than GA in optimizing the MSMB inventory model.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Optimasi, Persediaan, Algoritma Metaheuristik, Vendor Managed Inventory (VMI), Algoritma Gray Wolf Optimizer, Algoritma Genetika |
| Subjects: | T Technology > T Technology (General) T Technology > T Technology (General) > T57.5 Data Processing T Technology > T Technology (General) > T57.6 Operations research--Mathematics. Goal programming T Technology > T Technology (General) > T57.84 Heuristic algorithms. T Technology > T Technology (General) > T58.62 Decision support systems T Technology > T Technology (General) > T58.8 Productivity. Efficiency |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Industrial Engineering > 26101-(S2) Master Thesis |
| Depositing User: | Sri Kurnia Dwi Budi Maulana |
| Date Deposited: | 13 Feb 2023 04:46 |
| Last Modified: | 13 Feb 2023 04:46 |
| URI: | http://repository.its.ac.id/id/eprint/96781 |
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