Application of Greedy Algorithm to Solve Integer Knapsack Problem (Case Study: Indah Logistik Cargo Mataram)

Setiawati Setiawati; Elyin Fitrawati; Razma Rizqiyah Awwaliyah; Baiq Nadiva Alivia; Syamsul Bahri

doi:10.29303/semeton.v1i1.210

Authors

Setiawati Setiawati Universitas Mataram
Elyin Fitrawati Universitas Mataram
Razma Rizqiyah Awwaliyah Universitas Mataram
Baiq Nadiva Alivia Universitas Mataram
Syamsul Bahri Universitas Mataram

DOI:

https://doi.org/10.29303/semeton.v1i1.210

Keywords:

optimization, greedy algorithm, distribution, integer knapsack, logistics

Abstract

Distribution is one form of problem that can be solved using the optimization process. There are various things that can be optimized in distribution problems, including maximizing the weight of goods to be distributed so that it can save distribution costs and provide benefits for the company. One of the companies engaged in the distribution of goods is Indah Logistik Cargo, Mataram branch, where in the distribution process there are goods with quantities that exceed the capacity of the shipping equipment. Therefore, it is necessary to select goods to be delivered with the maximum weight but not exceeding the capacity that provides greater benefits to the company. This problem is one of the integer knapsack problems. In this study, researchers used greedy algorithms, namely greedy by profit, greedy by weigth and greedy by density to optimize profits. From the research results, it is obtained that the method that has a greater profit in the distribution process at Indah Logistik Cargo Mataram is greedy by density, which amounts to 𝑅𝑝 2,603,138.3.

References

R. N. Devita and A. P. Wibawa, “Sains, Aplikasi, Komputasi dan Teknologi Informasi Teknik-teknik optimasi knapsack problem,” Sains, Apl. Komputasi dan Teknol. Inf. Vol 2, No 1, April 2020, pp. 35-40, vol. 2, no. 1, p. 35, 2020.

K. Anisa and N. Aslami, “Pengaruh Ketepatan Waktu Terhadap Pendistribusian Barang Di Unit Logistik UPT. Balai Yasa Pulubrayan,” Ekon. Bisnis Manaj. dan Akunt., vol. 3, no. 8.5.2017, pp. 2003–2005, 2022.

B. P. Silalahi, M. Novanto, and P. T. Supriyo, “Algoritme Migrating Birds Optimization dan Algoritme Particle Swarm Optimization: Penyelesaian Masalah Knapsack 0-1,” PYTHAGORAS J. Pendidik. Mat., vol. 17, no. 1, pp. 266–280, 2022, doi: 10.21831/pythagoras.v17i1.35660.

E. Hapidah and F. Muhtarulloh, “Penyelesaian Masalah Knapsack (0-1) Menggunakan Algoritma Greedy Knapsack Problem Solving (0-1) Using Greedy Algorithms,” Senter, no. November 2020, pp. 306–313, 2020.

M. Syahlan, A. Syam, F. M. Faqi, and ..., “Sistem Informasi Jasa Wedding Organizer Dengan Fitur Pemilihan Paket Otomatis Menggunakan Algoritma Greedy Pada Koya Wedding,” SISITI Semin. Ilm. ..., vol. IX, no. 2, pp. 190–202, 2020, [Online]. Available: http://ejurnal.dipanegara.ac.id/index.php/sisiti/article/view/695%0Ahttp://ejurnal.dipanegara.ac.id/index.php/sisiti/article/download/695/586

N. W. Y. Agus Ambarwari, “Penerapan Algoritma Greedy Pada Permasalahan Knapsack Untuk Optimasi Pengangkutan Peti Kemas,” J. Optimasi, vol. 12, no. January, p. 44, 2016, doi: 10.13140/RG.2.1.2775.6569.

S. Palgunadi, J. K. Putri, J. Informatika, U. S. Maret, J. Informatika, and U. S. Maret, “Metode penggergajian live sawing dikenal juga dengan istilah plane sawn . Batang kayu di gergaji menurut rangkaian pemotongan yang sejajar dalam satu garis atau parallel cuts . Ketebalan dari setiap pasang parallel cuts mengikuti suatu urutan yang spesifi,” no. 1, pp. 189–194, 2015.

N. A. Ali, “Penerapan Algoritma Genetika dan Perbandingannya Dengan Algoritma Greedy dalam Peneyelesaian Knapsack Problem Skripsi,” 2017.

A. Roihan, K. Nasution, and M. Z. Siambaton, “Implementasi Algoritma Greedy Kombinasi dengan Perulangan pada Aplikasi Penjadwalan Praktikum,” sudo J. Tek. Inform., vol. 1, no. 2, pp. 42–50, 2022, doi: 10.56211/sudo.v1i2.8.

S. Basriati, E. Safitri, and M. Ermanita, “Aplikasi Algoritma Greedy Terhadap Permasalahan Integer Knapsack pada Toko Surya Muda Pekanbaru,” J. Sains Mat. dan Stat., vol. 6, no. 2, p. 97, 2020, doi: 10.24014/jsms.v6i2.10554.