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INDONESIA
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri
Published by Universitas Kristen Petra Surabaya
ISSN : 14112485     EISSN : 20877439     DOI : -
Core Subject : Engineering,
Industrial & Manufacturing Engineering
Jurnal Teknik Industri aims to: Promote a comprehensive approach to the application of industrial engineering in industries as well as incorporating viewpoints of different disciplines in industrial engineering. Strengthen academic exchange with other institutions. Encourage scientist, practicing engineers, and others to conduct research and other similar activities.
Arjuna Subject : -
Articles 412 Documents
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Design and Development of a Power-Free Smart Cooler Box Using Phase Change Materials for Last-Mile Cold Chain Logistics Zahra, Helvina Aulia; Rusdiansyah, Ahmad; Dewi, Ratna Sari; Kusumawardani, Rindi; Pramata, Azzah Dyah; Isnaini, Fadila
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 28 No. 1 (2026): June 2026
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.28.1.%p

Abstract

The growing demand for fresh products requires efficient and sustainable cold chain logistics, especially in last-mile delivery. Conventional refrigerated trucks utilized in last-mile logistics are hindered by high energy consumption, operational costs, and limited flexibility, creating a critical barrier for small-scale cold chain access. To address these inefficiencies, this research develops a Power-Free Smart Cooler Box that integrates Phase Change Material (PCM) technology with a high-performance composite structure to maintain frozen-grade temperatures without active power. The design methodology employed the Theory of Inventive Problem Solving (TRIZ) to systematically resolve the engineering contradiction between maximizing thermal endurance and minimizing system weight. The resulting configuration features a multi-layer insulation wall (Fiberglass/Vacuum Insulated Panel/Polyurethane) and a validated 6-sided PCM layout. This configuration enables the Smart Cooler Box to maintain an internal air temperature of ≤ −10 °C for more than 10 hours without external power. It is also equipped with a built-in IoT-based temperature sensor to ensure real-time traceability. These findings imply that the developed passive cooler provides a scientifically validated, zero-emission alternative that offers a flexible, energy-efficient, and environmentally friendly option for last-mile logistics while ensuring product safety in the final stage of delivery.
An Interpretable Data-Driven Framework for Smart Tunnel Boring Machine Performance Analysis and Energy–Cost Optimization Salam M., Chairul; Khanifa, Arrina
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 28 No. 1 (2026): June 2026
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.28.1.24 - 48

Abstract

Tunnel Boring Machine (TBM) operations are governed by complex and nonlinear interactions among geological variability, machine control parameters, and energy consumption, posing significant challenges for reliable performance prediction and operational optimization. Conventional empirical and physics-based approaches often struggle to capture regime-dependent behavior and parameter coupling under heterogeneous excavation conditions. To address these limitations, this study proposes an integrated and interpretable data-driven framework that combines ensemble machine learning, time-series modeling, unsupervised regime identification, multi-objective optimization, and explainable artificial intelligence within a unified analytical architecture. A multisource dataset encompassing geotechnical, operational, environmental, energy, and economic parameters was analyzed using Extreme Gradient Boosting (XGBoost), Random Forest, Gradient Boosting Regression, and recurrent neural networks. Among these, XGBoost demonstrated superior predictive capability, achieving the highest coefficient of determination and consistently lower prediction errors compared with baseline models. Unsupervised clustering identified distinct operational regimes—efficient, intermediate, and aggressive—enabling a structured evaluation of energy–cost trade-offs. Regime-aware optimization further indicated substantial potential for reducing both energy consumption and operational costs relative to high-intensity operating conditions. Sensitivity analysis using SHAP, mutual information, ANOVA, and Sobol indices revealed strong interaction effects among thrust force, torque, and rock strength parameters, highlighting the coupled nature of TBM excavation mechanics. The proposed framework extends conventional predictive modeling approaches by translating data-driven insights into interpretable, regime-based operational strategies. It provides a scalable methodological foundation for the future development of digital twin applications in TBM systems and contributes to more energy-efficient, cost-effective, and sustainable tunneling operations in complex underground environments.

Page 42 of 42 | Total Record : 412

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