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OPSI
Published by Universitas Pembangunan Nasional Veteran Yogyakarta
ISSN : 16932102     EISSN : 26862352     DOI : https://doi.org/10.31315/opsi
Core Subject : Engineering,
Industrial & Manufacturing Engineering
Jurnal OPSI adalah Jurnal Optimasi Sistem Industri yang diterbitkan oleh Jurusan Teknik Industri UPN “Veteran” Yogyakarta sebagai wahana publikasi hasil karya ilmiah, penelitian rekayasa teknologi di bidang Teknik Industri, Sistem Industri, Manajemen Industri dan Teknologi Informasi.
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Articles 273 Documents
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Large neighborhood search for route and fleet optimization in frozen food distribution Simbolon, Jonathan Andrepa; Trismi Ristyowati; Soepardi, Apriani; Irwan Soejanto; Yuli dwi Astanti; Puryani; Chaeron, Mochammad
OPSI Vol 18 No 2 (2025): OPSI - December 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i2.15743

Abstract

This study develops an optimization model to enhance the distribution efficiency of a frozen food distributor. The company faces operational inefficiencies due to excessive fleet capacity and conventional route assignment methods, which increase travel distances and overall distribution costs. To address these challenges, an extended Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) model is proposed, which integrates heterogeneous fleet characteristics and prioritizes customer service constraints. The model is solved using the Large Neighborhood Search (LNS) metaheuristic to determine optimal routing and fleet allocation strategies. The optimized model achieves a 15.95% reduction in total travel distance and a 21.84% decrease in total distribution costs compared with the company’s current operations. The findings confirm the effectiveness of the LNS-based CVRPTW approach in improving logistics performance and provide practical insights for companies seeking to minimize distribution costs through strategic route planning and fleet management.
Feature-based classification of sugarcane quality using the K-nearest neighbor algorithm Indrianti, Nur; Iqbal, Muhammad; Rustamaji, Heru Cahya; Ferriyan , Andrey; Mulyono , Panut; Ananta, Moh. Ais
OPSI Vol 18 No 2 (2025): OPSI - December 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i2.16000

Abstract

The rapid advancement of artificial intelligence has enabled practical, data-driven approaches to agricultural quality assessment. However, many existing methods rely on complex sensor systems that are costly and difficult to deploy in the field. This study proposes a lightweight and interpretable K-Nearest Neighbor (KNN) model for non-destructive evaluation of sugarcane milling feasibility using five easily measurable physical attributes: relative distance ratio, internode length, mean diameter, circumference, and weight per centimeter. Samples with Brix less than 16 are categorized as not feasible for milling, while Brix equal to or greater than 16 are classified as possible. A dataset of 1,889 Bululawang samples collected in Malang, East Java, Indonesia, was evaluated across twenty-two scenarios that varied the train-test split, normalization method, distance metric, and neighborhood size. The optimal configuration, consisting of an 80:20 split, Standard normalization, the Minkowski distance metric, and k=75, achieved an accuracy of 78%. The findings confirm that physical measurements can serve as effective predictors of sugarcane quality and support data-driven inspection and sustainable resource utilization in line with SDGs 2, 9, and 12.
Application of MEAD method for occupational disease risk control in hotel housekeeping department Saputra , Manggala Rasendriya; Lucitasari, Dyah Rachmawati; Al-Bana , Nuzila Putri
OPSI Vol 18 No 2 (2025): OPSI - December 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i2.16028

Abstract

Housekeeping activities in the hospitality industry are essential for service quality but are frequently associated with occupational health risks that may reduce productivity and increase employee health problems. Most existing prevention efforts focus on task-level hazards, with limited consideration of organizational and macro-level factors. Therefore, this study applies the Macroergonomic Analysis and Design (MEAD) method to address occupational disease risks in a hotel housekeeping department. Four major risk factors were identified: environmental exposure to chemical odors and waste; equipment and facility limitations, including restricted trolley mobility and inadequate Personal Protective Equipment (PPE); worker-related health complaints, reported by 67% of workers for skin and respiratory symptoms and by 50% for eye irritation, abrasions, and skin redness, with overlapping symptoms; and organizational factors related to insufficient operational supervision. Using the ten-stage MEAD framework, the housekeeping work system was redesigned through administrative controls, PPE provision, and safety awareness interventions. Post-implementation evaluation using Job Safety Analysis (JSA) showed a clear reduction in health complaints, with respiratory and skin irritation decreasing from frequent to rare and eye irritation and abrasions declining from occasional to non-occurring. These results demonstrate that MEAD is an effective framework for improving occupational health and safety in hotel housekeeping operations.

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