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All Journal Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Green Intelligent Systems and Applications
Nguyen , Huu Tho
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Optimizing Vehicle Routing for Perishable Products with Time Window Constraints: : A Case Study on Bread Distribution Fauzi, Rifqi; Priansyah, Adi; Puspadewa, Paskalis Krisna; Awal, Syifa Maulvi Zainun; Nguyen , Huu Tho; Rifai, Achmad Pratama
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 27 No. 1 (2025): June 2025
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.27.1.1-20

Abstract

This research investigates the application of optimization methods to the Capacitated Vehicle Routing Problem with Time Windows in the context of bread distribution for the efficiency of different approach for managing large-scale goods delivery. Managing this distribution requires considering complexities such as travel distance, vehicle capacity, and time windows. Specifically, it compares the performance of ALNS, SA, and GA in minimizing total travel distance while adhering to strict delivery windows. The research is conducted across different cases, each distinguished by varying levels of demand, nodes, and time windows for each case. Based on four cases, ALNS is the most effective method among the three methods in optimizing bread distribution. It was averagely 33.02% more efficient than SA and 57.21% than GA for minimizing travel distance and offering a robust solution, improving delivery efficiency across different case scenarios.
Classification of Metal Surface Defects Using Convolutional Neural Networks (CNN) Pratama, Dhika Wahyu; Ismail, Muchammad; Nurraudah, Restu; Rifai, Achmad Pratama; Nguyen , Huu Tho
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.581

Abstract

Metal surface quality inspection is an important step in ensuring that products meet predetermined industry standards. The manual methods used were often slow and prone to errors, so more efficient solutions were needed. The application of Machine Learning (ML)-based technologies, especially Convolutional Neural Networks (CNN), offered an innovative approach to overcome these challenges. CNN had the ability to automatically extract visual features from images with high accuracy, making it an effective tool in defect classification. This research used several CNN architectures, including MobileNetV2 and InceptionV3, as well as a model developed in-house, the K3 Model. Data augmentation, such as rotation and lighting adjustments, was applied to increase variation in the dataset and aid the model in generalization. The research results showed that the K3+Augmentation model achieved the highest accuracy of 100% in testing, with a very low loss of 0.0009. While MobileNetV2 offered better training speed, K3+Augmentation showed superior performance in detecting and classifying metal defects. These findings confirmed the potential of CNN in improving the efficiency of quality inspection in modern industry.
Co-Authors Achmad Pratama Rifai Awal, Syifa Maulvi Zainun Fauzi, Rifqi Muchammad Ismail Nurraudah, Restu Pratama, Dhika Wahyu Priansyah, Adi Puspadewa, Paskalis Krisna