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All Journal International Journal of Electrical and Computer Engineering Rekayasa Mesin Sinergi MESIN Turbo : Jurnal Program Studi Teknik Mesin International Journal of Electrical, Energy and Power System Engineering (IJEEPSE) Indonesian Journal of Engagement, Community Services, Empowerment and Development (IJECSED) Lamahu: Jurnal Pengabdian Masyarakat Terintegrasi JUARA: JURNAL WAHANA ABDIMAS SEJAHTERA Kocenin Jurnal Pengabdian Kepada Masyarakat Advance Sustainable Science, Engineering and Technology (ASSET)
Sally Cahyati
IPB
Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Bottleneck Analysis and Improvement in Apparel Manufacturing Production Processes Using Integration Design of Experiments and Discrete Event Simulation Muchtar, Diki; Moengin, Parwadi; Surjasa, Dadang; Cahyati, Sally
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.3288

Abstract

Bottlenecks in apparel manufacturing often cause unbalanced production flows, increased waiting times, and reduced system performance. This study aims to analyze and eliminate bottlenecks by integrating Design of Experiments (DOE) and Discrete Event Simulation (DES). Four workstations (X1–X4) were selected as experimental factors, while system performance was evaluated using bottleneck indicators across six production stages (Y1–Y6). DOE was used to design capacity scenarios, and DES assessed system performance under each configuration. Results show that partial capacity increases at selected workstations are insufficient to fully eliminate bottlenecks. Complete elimination was achieved only in specific scenarios (Experiments 13–16), where all bottleneck indicators reached zero. Among these, Experiment 13 was identified as the optimal solution, as it eliminated all bottlenecks with the minimum additional capacity. These findings indicate that targeted capacity enhancement at critical workstations is an effective and economical strategy. The integration of DOE and DES proves to be a reliable data-driven approach for identifying bottlenecks and selecting optimal capacity improvements. This study also provides a structured and replicable framework for bottleneck analysis in apparel manufacturing, contributing to the limited application of DOE–DES integration in this sector.
A risk-constrained SARSA–FIS hybrid decision architecture with adaptive exploration control Fat, Joni; Moengin, Parwadi; Astuti, Pudji; Cahyati, Sally
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1531-1542

Abstract

Algorithmic trading systems operate in highly dynamic and uncertain environments where learning-based decision agents must balance adaptability with strict risk control. Reinforcement learning (RL) methods provide adaptive policy optimization but often suffer from unstable exploration and limited interpretability in financial markets. This study proposes a risk-constrained SARSA–FIS hybrid decision architecture with adaptive exploration control for algorithmic trading. The framework integrates a compact SARSA-based reinforcement learning environment with a Sugeno-type fuzzy inference system (FIS) that converts reinforcement signals into interpretable trading decisions. Exploration follows a decaying ε-greedy policy with a drawdown-triggered reset mechanism to maintain bounded risk exposure during learning. The system was implemented as a MetaTrader 5 Expert Advisor and evaluated on the GBPUSD currency pair using historical market data. Experimental results show that the hybrid framework improves trading performance compared with a rule-based baseline. During a six-month out-of-sample evaluation, the system achieved a net profit of 90 USD and a profit factor of 1.35, compared with 10 USD and 1.02 for the baseline. Extended one-year testing confirmed stable profitability and controlled drawdown behavior. The results demonstrate that integrating reinforcement learning, fuzzy decision mapping, and explicit risk constraints provides a practical approach for developing adaptive trading agents.
Co-Authors Agtirey, Novan Rojabil Ahju, Rosalind Astuti , Pudji Dadang Surjasa, Dadang Diki Muchtar Diki Fat, Joni Febriyanto Febriyanto Himawan, Riswanda Laksana, Dek Ngurah Laba Larasati Rizky Putri Leo, Joseph Andrew Mahendra, Daniel Rizky Maya Indrasti Notoprayitno Medy Rioma, Medy Minarti, Astari Nora Azmi Nurhikmah Budi Hartanti Parwadi Moengin Permatasari , Rosyida PUDJI ASTUTI Puspa, Sofia Debi Putra, Reky Amanda Ridhoarto, Muhammad Alwan Rositayanti Hadisoebroto Rosyida Permatasari, Rosyida Suroso Suroso Susetyarto, Martinus Bambang Triwulandari , Triwulandari