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All Journal Teknotan: Jurnal Industri Teknologi Pertanian Jurnal Sistem Teknik Industri Journal of Research in Industrial Engineering and Management
Nur Faizatus Sa'idah
Institut Teknologi Bandung
Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Other

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Discrete-Event Control and Predictive Optimization of Fuel Tankers and Pumps Allocation Adrian Ramanda; Nur Faizatus Sa'idah; Rully Tri Cahyono
Journal of Research in Industrial Engineering and Management Vol 1 No 1 (2023): May 2023
Publisher : Program Studi Teknik Industri, Fakultas Teknologi Industri, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61221/jriem.v1i1.8

Abstract

This paper deals with a dynamic scheduling model of a general fuel tankers and pumps operations. The models are dynamics because the ships’ arrival time is allowed to vary. The goal of the study is to determine the optimal berth allocation/scheduling and the policy recommendations, which minimize ships’ total waiting time, berth occupancy ratio, and ships’ charter cost. Discrete-event Systems (DES) modeling is chosen due to aperiodicity in ships’ arrival time and asynchrony operations’ time among different berthing positions. Two DES models are developed, i.e.: (1) multiple cost allocation problem (MBAP) for the supply jetty and (2) simple berth allocation problem (SBAP) for the consignment jetty. Furthermore, we use model predictive control (MPC) to optimize the DES model, and we also provide mathematical analysis of the proposed algorithm. Numerical examples examining two cases (tidal and non-tidal) in each of the two models are presented to illustrate the optimal solution. The problem encountered is that current berth allocation is not working efficiently, as indicated by the average waiting time for ships at the supply jetties (jetties 1 and 2) which is above the standard (14 hours), while the consignment jetty (jetty 3) is well below standard.
Pengendali Model Prediktif Terdistribusi untuk Meningkatkan Ketahanan dan Efisiensi Rantai Pasok Gula Semut Wangsaputra, Rachmawati; Sa'idah, Nur Faizatus; Pangestuti, Monika Windiana
Teknotan: Jurnal Industri Teknologi Pertanian Vol 19, No 3 (2025): TEKNOTAN, Desember 2025
Publisher : Fakultas Teknologi Industri Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jt.vol19n3.15

Abstract

Dalam keberjalanannya, sistem rantai pasok Gula Semut menghadapi perubahan level permintaan, tidak sama dengan level permintaan saat perencanaan.  Kemampuan sistem rantai pasok dalam  memenuhi keinginan pelanggan dan tetap efisien dalam situasi permintaan yang berubah-ubah merupakan titik penting menjaga ketahanan rantai pasok.   Metoda Pengendali Model Prediktif Terdistribusi (PMPT) dan Colaborative Planning Forecasting dan Replenishment (CPFR) berpotensi menangani perubahan level permintaan terutama yang bersifat fluktuatif.   Objek penelitian adalah rantai pasok Gula Semut PT Binar Dini Mandiri Indonesia (PT BDMI), berlokasi di kabupaten Banyumas; penghasil kelapa terbesar di provinsi Jawa Tengah.  PT BDMI sering mengalami kerugian karena tidak mampu memenuhi permintaan saat terjadi perubahan.  Rumusan masalah penelitian adalah bagaimana mengimplementasikan CPFR dan PMPT sehingga kinerja ketahanan dan efisiensi kerja rantai pasok meningkat.  Metodologi meliputi: rumusan masalah, pengelolaan berdasarkan  CPFR dan PMPT, pengujian menggunakan 4 skenario, analisis, kesimpulan.  Pengolahan data dilakukan menggunakan MATLAB 2025 dan Excel Solver.  Ukuran kinerja ketahanan adalah tingkat pemenuhan permintaan sedangkan untuk efisiensi adalah ongkos pengadaan dan ongkos simpan.  Hasil menunjukkan skenario-0 memiliki ukuran kinerja tertinggi dari sisi ketahanan karena  rantai pasok dirancang berdasarkan kemampuan awal, sedang  pada skenario 1-2-3, semakin fluktuatif perubahan  permintaan, kinerja ketahanan  dan efisiensi akan menurun.  Kuantifikasinya perbandingan antar penggunaan PMPT dan tidak menunjukkan bahwa  PMPT memberikan tingkat ketahanan lebih tinggi yang tidak menggunakan PMPT.  PMPT dapat meningkatkan  ketahanan sekitar 9,9 % dan efisiensi kerja sebesar 1,2 % untuk  rantai pasok tetapi saat  perubahan permintaan sangat fluktuatif, tingkat pemenuhan permintaan mulai turun.  PMPT menunjukkan kemampuan  menghadapi perubahan dan juga tetap menjaga efisiensi.  Kesimpulan metoda PMPT terbukti memang  lebih dapat fleksibel menangani fluktuasi demand.
A Generalized Eyring-Weibull Model for Degradation Analysis of Lithium-Ion Batteries Under Multi-Stressor Conditions Nur Faizatus Sa'idah; Hafidlotul Fatimah Ahmad
Jurnal Sistem Teknik Industri Vol. 28 No. 3 (2026): JSTI Volume 28 Number 3 July 2026
Publisher : TALENTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jsti.v28i3.26011

Abstract

Degradation analysis of lithium-ion batteries is a critical aspect of understanding the aging behaviors and reliability of energy storage systems. Although purely data-driven approaches are widely utilized for their high short-term predictive accuracy, they often function as black-box models that fail to capture the underlying physicochemical mechanisms of capacity fade under dynamic operational conditions. To address these limitations, this study uses a Generalized Eyring-Weibull model for lithium-ion battery degradation analysis under multi-stressor conditions. The proposed analytical approach explicitly integrates three key operational stress variables simultaneously: temperature, State of Charge (SoC), and discharge current (C-rate). Model validation was performed using the NASA Prognostics Center of Excellence (PCoE) battery dataset across distinct discharge profiles (2A and 4A). Physical parameters—including activation energy (Ea) and stress exponents—were extracted via L-BFGS-B optimization, while the long-term capacity fade trajectory was tracked using Miner’s Rule. The evaluation results demonstrate that this generalized physics-based model accurately maps actual degradation trends, yielding high R2 values on State of Health (SoH) tracking. Furthermore, the model showcases superior robustness in capturing accelerated aging from high current loads using a single universal equation without requiring separate data retraining. By bridging empirical data with fundamental kinetic principles, this study provides an interpretable and robust analytical methodology for advanced predictive maintenance strategies.
Co-Authors Adrian Ramanda Hafidlotul Fatimah Ahmad Pangestuti, Monika Windiana Rully Tri Cahyono Wangsaputra, Rachmawati