Ramdani, Dadan (2024) Optimasi masalah penugasan dengan Alternate Method, Average Sum Method dan Subtract Row and Add One Assignment Method (SRAOA) menggunakan software POM-QM. Sarjana thesis, UIN Sunan Gunung Djati Bandung.
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Abstract
INDONESIA : Dalam sebuah perusahaan sangat dibutuhkan penugasan tenaga kerja secara tepat untuk menghasilkan solusi yang optimal baik memaksimumkan pendapatan maupaun meminimalkan biaya yang dikeluarkan untuk produksi. Dalam penelitian ini membahas mengenai penyelesaian masalah penugasan dengan menggunakan Alternate Method, Average Sum Method dan Subtract Row and Add One Assignment Method (SRAOA) serta Software POM-QM. Penelitian yang bertujuan untuk memahami cara penentuan solusi optimal dan mengetahui perbandingan hasil Solusi optimal ketiga metode. Serta menganalisis Alternate Method dan Average Sum Method belum mendapatkan solusi optimal ketika dibandingkan dengan Subtract Row and Add One Assignment Method. Data yang digunakan dalam studi kasus ini berupa empat data sekunder yaitu data berukuran 10x10 minimasi seimbang, 7x8 minimasi tidak seimbang, 8x8 maksimasi seimbang dan 8x9 maksimasi tidak seimbang serta masing-masing 100 data random yang diperoleh dari program Python. Studi kasus 1 menggunakan software POM-QM mendapatkan solusi optimal sebesar 39, studi kasus 2 menggunakan tiga metode manual mendapatkan solusi optimal yang sama yaitu sebesar 115, pada studi kasus 3 menggunakan tiga metode manual mendapatkan solusi optimal yang berbeda yaitu menggunakan Alternate Method sebesar 648, menggunakan Average Sum Method sebesar 642, dan menggunakan Subtract Row and Add One Assignment Method sebesar 650. Kemudian pada studi kasus 4 menggunakan software POM-QM mendapatkan solsui optimal sebesar 657. Dari ketiga metode manual yang digunakan terbukti bahwa metode SRAOA merupakan metode yang paling efektif untuk menyelesaikan masalah penugasan karena hasilnya lebih optimal pada kasus maksimasi maupun minimasi serta iterasi pengerjaan yang relatif lebih sedikit. ENGLISH : In a company, it is essential to assign labor optimally to achieve the best solutions, whether maximizing revenue or minimizing production costs. This research discusses solving assignment problems using the Alternate Method, the Average Sum Method, and the Subtract Row and Add One Assignment Method (SRAOA), as well as the POM-QM software. The research aims to understand how to determine optimal solutions and compare the results of the three methods' optimal solutions. It also analyzes that the Alternate Method and Average Sum Method have not yet achieved optimal solutions when compared to the Subtract Row and Add One Assignment Method. The data used in this case study consists of four secondary data sets: a balanced 10x10 minimization, an unbalanced 7x8 minimization, a balanced 8x8 maximization, and an unbalanced 8x9 maximization, as well as 100 random data sets obtained from a Python program. In Case Study 1, the POM-QM software yielded an optimal solution of 39; in Case Study 2, all three manual methods achieved the same optimal solution of 115; in Case Study 3, the three manual methods yielded different optimal solutions: the Alternate Method yielded 648, the Average Sum Method yielded 642, and the Subtract Row and Add One Assignment Method yielded 650. Finally, in Case Study 4, the POM-QM software yielded an optimal solution of 657. Among the three manual methods used, it was proven that the SRAOA method is the most effective for solving assignment problems because its results are more optimal in both maximization and minimization cases, with relatively fewer iterations.
Item Type: | Thesis (Sarjana) |
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Uncontrolled Keywords: | Masalah Penugasan; Alternate Method; Average Sum Method, Subtract Row and Add One Assignment Method; Software POM-QM; |
Subjects: | Applied mathematics Applied mathematics > Mathematical Optimization Applied mathematics > Special Topics of Applied Mathematics |
Divisions: | Fakultas Sains dan Teknologi > Program Studi Matematika |
Depositing User: | dadan ramdani |
Date Deposited: | 23 Aug 2024 01:47 |
Last Modified: | 23 Aug 2024 01:47 |
URI: | https://digilib.uinsgd.ac.id/id/eprint/93958 |
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