Salma Septiani Rahman, - and Khusnul Novianingsih, - and Endang Cahya Mulyaning A, - (2025) IMPLEMENTASI HYBRID PARTICLE SWARM OPTIMIZATION DAN CUCKOO SEARCH ALGORITHM (HPSO-CSA) PADA PENYELESAIAN UNCAPACITATED FACILITY LOCATION PROBLEM. S1 thesis, Universitas Pendidikan Indonesia.
Abstract
Uncapacitated Facility Location Problem (UFLP) merupakan permasalahan penentuan lokasi pembangunan fasilitas untuk melayani sejumlah pelanggan tanpa batasan kapasitas. Tujuan utama dari UFLP adalah meminimumkan total biaya, yang terdiri atas biaya pembangunan fasilitas dan biaya pelayanan pelanggan. UFLP termasuk dalam kategori masalah kombinatorial yang sulit diselesaikan dengan metode eksak karena kompleksitas komputasinya. Penelitian ini mengusulkan pendekatan hybrid CSA dan PSO (HPSO-CSA) untuk menggabungkan kelebihan keduanya. CSA digunakan untuk menentukan solusi awal yang selanjutnya solusi tersebut akan digunakan sebagai partikel awal dalam PSO. Optimisasi dilanjutkan hingga solusi optimal ditemukan. Hasil pengujian menunjukkan bahwa pendekatan HPSO-CSA mampu menghasilkan solusi optimal secara efisien pada data berukuran kecil hingga menengah. Sementara pada data berukuran besar, selisih rata-rata cost yang dihasilkan algoritma hibrid dan CSA mulai mengecil. Meskipun demikian, Algoritma HPSO-CSA tetap kompetitif dengan menghasilkan rata-rata cost yang lebih kecil dari algoritma penyusunnya. Uncapacitated Facility Location Problem (UFLP) is a problem of determining the location of facility construction to serve a number of customers without capacity constraints. The main objective of UFLP is to minimize the total cost, which consists of facility construction cost and customer service cost. UFLP belongs to the category of combinatorial problems that are difficult to solve by exact methods due to its computational complexity. This study proposes a hybrid CSA and PSO (HPSO-CSA) approach to combine the advantages of both. CSA is used to determine the initial solution which will then be used as the initial particle in PSO. Optimization is continued until the optimal solution is found. The test results show that the HPSO-CSA approach is able to produce optimal solutions efficiently on small to medium sized data. While on large data, the difference in average cost generated by hybrid and CSA algorithms began to shrink. Nevertheless, the HPSO-CSA algorithm remains competitive by producing a smaller average cost than its constituent algorithms.
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| Item Type: | Thesis (S1) |
|---|---|
| Additional Information: | https://scholar.google.com/citations?view_op=new_articles&hl=en&imq=SALMA+SEPTIANI+RAHMAN# ID SINTA Dosen Pembimbing Khusnul Novianingsih: 258640 Endang Cahya Mulyaning A: 6121877 |
| Uncontrolled Keywords: | Uncapacitated Facility Location Problem, Cuckoo Search Algorithm, Particle Swarm Optimization, Optimisasi, HPSO-CSA. Uncapacitated Facility Location Problem, Cuckoo Search Algorithm, Particle Swarm Optimization, Optimization, HPSO-CSA. |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics |
| Divisions: | Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam > Program Studi Matematika - S1 |
| Depositing User: | Salma Septiani Rahman |
| Date Deposited: | 08 May 2025 03:26 |
| Last Modified: | 08 May 2025 03:26 |
| URI: | http://repository.upi.edu/id/eprint/133139 |
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