Ichtiarto, Bonivasius Prasetya
Unknown Affiliation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

Perancangan Optimasi Rute Pengiriman Buah Menggunakan Saving Matrix Dan Algoritma Genetika Untuk Meningkatkan Efisiensi Distribusi Jufri, Trizamsuar; Jaqin, Choesnul; Ichtiarto, Bonivasius Prasetya; Hernadewita, Hernadewita
Proceeding Mercu Buana Conference on Industrial Engineering Vol 7 (2025): SMART AND SUSTAINABLE INDUSRIES : DRIVING LOW-EMISSIONS AND RENEWABLE ENERGY TRANSFORM
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/MBCIE.2025.34457

Abstract

Distribusi produk pertanian, khususnya buah-buahan yang mudah rusak, memegang peranan vital dalam menjaga kualitas produk dan efisiensi logistik. Keterlambatan pengiriman menjadi masalah utama dalam distribusi buah di Provinsi Jawa Barat, dengan tingkat keterlambatan mencapai 15–20% dan tingkat kerusakan produk 10–15%. Penelitian ini bertujuan untuk membandingkan efektivitas dua metode optimasi rute distribusi, yaitu metode Saving Matrix dan Algoritma Genetika, dalam konteks pengiriman buah dari berbagai wilayah di Jawa Barat menuju Bandung. Penelitian ini mengintegrasikan parameter degradasi kualitas buah berdasarkan waktu pengiriman dan karakteristik komoditas, seperti masa simpan. Pendekatan mixed-method eksplanatori sekuensial digunakan dalam penelitian ini. Tahap kualitatif dilakukan melalui wawancara dan observasi terhadap delapan stakeholder industri logistik pertanian. Temuan kualitatif menjadi dasar perancangan model optimasi. Pada tahap kuantitatif, Saving Matrix dan Algoritma Genetika diimplementasikan menggunakan bahasa pemrograman Python dengan library seperti geopy, DEAP, dan matplotlib. Data jarak antar titik diperoleh dari koordinat geografis, dan model optimasi dikembangkan berdasarkan formulasi Vehicle Routing Problem (VRP) yang disesuaikan. Hasil penelitian menunjukkan bahwa Algoritma Genetika lebih unggul dalam konteks kompleksitas tinggi, dengan kemampuan mengurangi waktu pengiriman hingga 22% dan penurunan kerusakan stroberi dari 15% menjadi 7%. Sementara itu, Saving Matrix lebih unggul dari sisi kecepatan komputasi dan efisiensi awal, namun kurang adaptif terhadap variasi karakteristik produk dan medan geografis. Penelitian ini memberikan kontribusi pada praktik logistik pertanian dengan menawarkan solusi berbasis algoritma untuk distribusi buah yang lebih efisien dan adaptif terhadap kondisi riil. Hasil ini juga memperkaya literatur tentang penerapan heuristik dan evolusioner dalam konteks distribusi produk perishable di negara berkembang seperti Indonesia.
Forecasting Intermittent Demand For MRO Spare Parts Darmawan, Rachmat; Ichtiarto, Bonivasius Prasetya
Proceeding Mercu Buana Conference on Industrial Engineering Vol 2 (2020): ARAH PENGEMBANGAN RISET ENGINEERING DI ERA REVOLUSI INDUSTRI 4.0
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the face of current global economic challenges, maintaining efficiency is the core of inventory management and order fulfillment is crucial for any heavy equipment industry looking to change the supply chain. Along with the complexity of increasing costs, accelerating inventory fulfillment, delivery order commitment fulfills demand. It can be seen that some of the conditions caused by the recycling activity have caused a gap between the demand and the sales made. That the forecast is still far from expected to meet real sales demand, if it continues to allow for unrealized sales and market losses. The phenomena to be known in this study include the expectation of accurate predictions about the availability of spare parts so that it meets customer needs. Utilize this type of quantitative research, based on historical demand data that reflects the nature of demand patterns by improving the accuracy of stock stocks and the level of service related to operating activities against meeting target expectations and the reality of results obtained. Real demand data based on demand turnover ratio in 2017-2018, in selecting the best forecast model of trend analysis is done with the result of setting exponential growth giving the smallest value of MAPE 12,789 MAD 11,333 MSD 271,595. Trend analysis results show that data plots do not fluctuate normally, so the assumption test is performed to calculate the number of requests (demand size) and the time between arrival of requests (inter-demand interval). Testing the assumption of demand size (zt) following the ARIMA model (0,1,1), it is found that stationary data from outputs are produced for ACF plots and PACF replacement data have been considered. As an example of the safety stock calculation results with a 95% service level such as depleted, the spin-on oil filter reached 4,248 pcs from the previous forecast of 4,08 pcs. In the future, it will not only be forecasting of existing secondary data, but will be upgrading from business model to final delivery as the industry becomes more competitive.