@phdthesis{repoupi137913,
            year = {2025},
          school = {Universitas Pendidikan Indonesia},
           month = {August},
           title = {IMPLEMENTASI ALGORITMA GENETIKA ADAPTIF UNTUK MENYELESAIKAN FUZZY INVENTORY ROUTING PROBLEM},
            note = {https://scholar.google.com/citations?user=9QMxjcEAAAAJ\&hl=en

ID SINTA Dosen Pembimbing:
Khusnul Novianingsih: 258640
Sumanang Muhtar Gozali: 6121808},
             url = {https://repository.upi.edu/},
        abstract = {Penelitian ini membahas fuzzy inventory routing problem (FIRP) dengan ketidakpastian permintaan, yaitu permasalahan penentuan jumlah pengiriman dan rute distribusi yang melibatkan biaya transportasi,  biaya penyimpanan, serta biaya kehilangan penjualan (lost sales). Tujuan utama penelitian ini adalah meminimalkan total biaya operasional sekaligus menentukan rute distribusi yang efisien. Algoritma genetika adaptif (AGA) yang memodifikasi nilai crossover rate dan mutation rate secara adaptif berdasarkan perkembangan nilai fitness, diimplementasikan untuk menyelesaikan FIRP secara lebih optimal. Proses optimasi dilakukan dengan membangkitkan populasi awal, menghitung nilai fitness setiap solusi, dan melakukan proses seleksi, crossover, serta mutasi secara adaptif hingga mencapai solusi terbaik. Hasil implementasi menunjukkan bahwa model FIRP dengan AGA menghasilkan nilai fungsi objektif minimum dengan rute distribusi optimal pada kasus distribusi LPG. Hasil ini mengindikasikan bahwa AGA mampu memberikan solusi efisien untuk FIRP dengan permintaan yang tidak pasti.

This study discusses the fuzzy inventory routing problem (FIRP) with demand uncertainty, namely the problem of determining the number of deliveries and distribution routes involving transportation costs, storage costs, and lost sales costs. The main objective of this study is to minimize the total operational costs while determining efficient distribution routes. An adaptive genetic algorithm (AGA) that adaptively modifies the crossover rate and mutation rate based on the development of fitness values is implemented to solve FIRP more optimally. The optimization process is carried out by generating an initial population, calculating the fitness value of each solution, and carrying out selection, crossover, and mutation processes adaptively until the best solution is achieved. The implementation results show that the FIRP model with AGA produces a minimum objective function value with an optimal distribution route for LPG distribution. These results indicate that AGA can provide an efficient solution for FIRP with uncertain demand.},
          author = {Marina Tampubolon, - and Khusnul Novianingsih, - and Sumanang Muhtar Gozali, -},
        keywords = {Optimasi, Fuzzy Inventory Routing Problem, Algoritma Genetika Adaptif.

Optimization, Fuzzy Inventory Routing Problem, Adaptive Genetic Algorithm.}
}