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International Journal of Industrial Optimization (IJIO)
Published by Universitas Ahmad Dahlan
ISSN : 27146006     EISSN : 27233022     DOI : https://doi.org/10.12928/ijio.v1i1.764
Core Subject : Science, Engineering,
Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering
The Journal invites original articles and not simultaneously submitted to another journal or conference. The whole spectrums of Industrial Engineering are welcome but are not limited to Metaheuristics, Simulation, Design of Experiment, Data Mining, and Production System. 1. Metaheuristics: Artificial Intelligence, Genetic Algorithm, Particle Swarm Optimization, etc. 2. Simulations: Markov Chains, Queueing Theory, Discrete Event Simulation, Simulation Optimization, etc. 3. Design of experiment: Taguchi Methods, Six Sigma, etc. 4. Data Mining: Clustering, Classification, etc. 5. Production Systems: Plant Layout, Production Planning, and Inventory Control, Scheduling, System Modelling, Just in Time, etc.
Arjuna Subject : Teknik (semua kategori) - Tenik Industri dan Manufaktur
Articles 94 Documents
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Application of deep learning for predicting ignition delay in hydrogen combustion engines Molana, Maysam; Biglar, Abbas; Darougheh, Nadia; Zoldak, Philip
International Journal of Industrial Optimization Vol. 6 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

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Abstract

This study investigates the use of deep learning techniques to forecast ignition delays in hydrogen combustion systems, with a focus on optimizing hydrogen combustion processes in industrial applications such as stationary power generation and the automotive industry. The work utilizes experimental data from a rapid compression machine (RCM) and a shock tube. Two large datasets were created through 0-D simulations and experimental measurements, covering a wide range of conditions. The study involves the development of two artificial neural network (ANN) models, one for RCM and another for shock tube data, each with distinct architectures. The ANN models were trained, tested, and evaluated using thoughtfully divided datasets. The results demonstrate the effectiveness of the developed ANN models in predicting ignition delays with remarkable accuracy. Comparative analyses with 0-D simulations and experimental measurements reveal that the ANN models predict ignition delays "1000 times faster" than traditional simulation methods. This speed improvement is crucial for real-time industrial applications, allowing engineers to quickly optimize combustion parameters, adjust engine settings, and make operational decisions in a fraction of the time. The study highlights the potential of these ANN models to optimize hydrogen combustion processes, improving combustion efficiency, reducing operational costs, and enhancing resource utilization in industrial settings. This progress can play a significant role in optimizing hydrogen-powered internal combustion engines by increasing fuel efficiency, reducing emissions, and enhancing overall engine performance. In the automotive and power generation sectors, the quick predictive abilities of ANN models can support more effective energy production, decrease operational expenses, and lessen environmental harm.
Smart door lock design and development using the pahl and beitz approach Rizky Reynaldy Brahmana; Mochamad Tutuk Safirin
International Journal of Industrial Optimization Vol. 6 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

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Abstract

Security vulnerabilities in conventional locks and existing smart locks necessitate innovative solutions that integrate robust authentication mechanisms. This study addresses the research gap by developing a smart door lock system that uniquely combines Indonesia's government-issued e-KTP (embedded with an RFID chip) and a capacitive touch sensor for multi-factor authentication, enhancing security while ensuring universal accessibility. The design process employs the Pahl and Beitz systematic engineering methodology, emphasizing iterative optimization through planning, conceptual design, embodiment design, and detail design phases. Key specifications, including e-KTP compatibility, cost-effectiveness <IDR 1 million, and energy efficiency, were prioritized. Prototype evaluations revealed that the final design achieved superior functionality, scoring 87/100 in a multi-criteria assessment. The assessment considered components, space, aesthetics, cost, and manufacturability. The system integrates an Arduino Nano Microcontroller, a 9V battery with a 17-day lifespan, and IoT connectivity for real-time feedback. Comparative analysis demonstrates a 40–60% cost reduction compared to commercial alternatives, alongside tamper-resistant advantages from e-KTP integration system, modularity potential, and rechargeable battery. This study underscores the viability of leveraging national ID systems in IoT security frameworks, offering policymakers and manufacturers actionable insights for scalable, standardized smart home solutions.
Development of an energy management system for palm oil refinery facilities: implementing a systems approach Febrian, Febrian; Pratoto, Adjar
International Journal of Industrial Optimization Vol. 6 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

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Abstract

This study aims to develop a proactive Energy Management System (EnMS) for a palm oil refinery using a comprehensive systems-based approach implemented carefully during the plant design phase. Unlike conventional methods that rely mainly on historical operational data, this research deliberately utilizes engineering design specifications together with simulation modeling to estimate accurate energy consumption baselines and formulate an ISO 50001-compliant EnMS. A regression-based analysis is systematically applied to define reliable Energy Performance Indicators (EnPIs), using production volume and running hours as key variables influencing overall energy utilization. The resulting analytical model estimates a Specific Energy Consumption (SEC) of 2.168 MWh/MT—significantly higher than the 0.45 MWh/MT BAT benchmark—primarily due to assumptions of full-capacity, simultaneous operation under conservative conditions. To support continuous energy performance improvement, the system incorporates PDCA-based review mechanisms and establishes progressive energy-saving targets: an initial 10% reduction, followed by 1–2% annual incremental improvements. Validation through structured feedback sessions from plant management confirmed the system's strong alignment with operational needs, feasibility within industrial contexts, and readiness for phased implementation. Ultimately, this study contributes a novel, simulation-based framework for integrating EnMS during the design stage, offering a scalable and adaptable model for energy-intensive industries that aim to enhance efficiency and achieve long-term sustainability from the outset.
A smart city infrastructure implementation framework – insights from smart street lighting implementation optimization Anityasari, Maria; Amrizal, Rizki; Widodo, Erwin; Anam, Sjamsjul; Chew, Boon Cheong
International Journal of Industrial Optimization Vol. 6 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v6i2.13307

Abstract

In recent years, the concept of smart cities and infrastructure has gained momentum as a solution to challenges such as population growth, resource management, and environmental sustainability. Rapid urbanization in many developing countries highlights the need for efficient infrastructure planning and management. This framework offers a structured approach for decision-making and resource allocation, enabling prioritization of investments to maximize limited resources while supporting development goals. The framework is tested through an analysis of the Smart Street Lighting Systems (SSLS) in Surabaya, Indonesia, addressing the city's intention to upgrade street lighting to reduce maintenance costs and energy consumption. Currently, the street lighting system faces issues including a high rate of broken or damaged lights and inefficiencies in handling complaints. However, limited funding and varied regional needs constrain any comprehensive upgrade. The proposed framework integrates the Analytical Hierarchy Process (AHP) to prioritize regions as weighting inputs, Mixed Integer Goal Programming (MIGP) to optimize the distribution of SSLS and conventional LED lighting across regions, and Cost-Benefit Analysis (CBA) to evaluate financial feasibility. Results recommend purchasing 11,915 new SSLS units with region-specific distributions, achieving a financially viable Benefit-Cost Ratio (BCR) of 2.059. These findings demonstrate practical implementation of smart city principles, balancing cost-efficiency, service performance, and stakeholder priorities. Policymakers can use this framework to maximize impact within budget constraints. This framework serves as a viable template for other regions and countries embarking on smart city infrastructure implementation.

Page 10 of 10 | Total Record : 94

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