Optimalisasi Antrian Dengan Algoritma Genetika di PT. Bank Rakyat Indonesia (Persero) Tbk. Unit Kelapa Lima

  • Imelda Hendriani Eku Rimo(1*)
    Universitas Nusa Cendana
  • (*) Corresponding Author
Keywords: queuing system

Abstract

One of the most disturbing problems in queuing at the bank is if the bank provides insufficient number of tellers to serve the customers. It will be obviously put the customers in an uncomfortable situation. To solve this problem the bank could increase the number of tellers but it could bring some loss on the bank side if it then turns out to be excessive although it will meet the demand of the customers. Thus, to find the most optimal solution to solve the problem in queuing system we use genetic algorithm. The main issues raised in this research are how to apply the genetic algorithm in solving the problem of queuing system in PT. Bank Rakyat Indonesia (Persero) Tbk. Branch Kupang Unit Kelapa Lima with Turbo Pascal 7.0 assistance and how to determine the most optimal queuing system by using genetic algorithm with Turbo Pascal 7.0 program assistance that can be applied in PT. Bank Rakyat Indonesia (Persero) Tbk. Branch Kupang Unit Kelapa Lima.

The purpose of genetic algorithm is to find the individuals with the highest value of fitness, thus the fitness function for the queuing system problem is the inversion of sum between how often a teller does not have customer to take care and the customer’s waiting time in a queue by using the genetic algorithm we could see that the sum of the optimal teller in busy hour is 4 tellers.

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References

Maghfirah, Pasigai, M.A., & Abdi, N.A. (2019). Analisis Penerapan Sistem Antrian pada PT. Bank Rakyat Indonesia (Persero) Tbk. Kantor cabang Pembantu Unit Pallangga Kabupaten Gowa. Jurnal Ilmu Manajemen : Profitability Volume 3 No 2. Makasar : FEB UNISMUH.
Siswanto. (2007). Operation Reseach jilid II. Jakarta : Erlangga.
Siswanto, H.B. (2007). Pengantar Manajemen. Jakarta : Bumi Aksara.
Suyanto. (2005). Algoritma Genetika dalam Matlab.Yogyakarta: Andi Offset .
Widyastuty, N., & Hamzah, A. (2007). Penggunaan Algoritma Genetika Dalam Peningkatan Kinerja Fuzzy Clustering Untuk Pengenalan Pola. Journal of mathematics and natural sciences BIMIPA, vol.17, No.2. Yogyakarta : FMIPA UGM.

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Published
2020-11-20
How to Cite
Rimo, I. (2020). Optimalisasi Antrian Dengan Algoritma Genetika di PT. Bank Rakyat Indonesia (Persero) Tbk. Unit Kelapa Lima. FRAKTAL: JURNAL MATEMATIKA DAN PENDIDIKAN MATEMATIKA, 1(1), 49-55. https://doi.org/10.35508/fractal.v1i1.3051
Section
Articles