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Hapsoro Agung Jatmiko
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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 6 Documents
 1 
Search results for , issue "Vol. 6 No. 1 (2025)" : 6 Documents clear
Inspection cost minimization by optimizing the number of inspectors in apparel manufacturing Ahmad, Shibbir; Kamruzzaman, M.
International Journal of Industrial Optimization Vol. 6 No. 1 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v6i1.9279

Abstract

Apparel manufacturing organizations aim to minimize costs, including inspection costs, but there is a research gap in optimizing the number of inspectors without compromising quality. This study focuses on reducing inspection costs by determining the minimum number of inspectors required. A mathematical model has been developed to calculate inspection costs based on the standard minute value and cost per minute. Additionally, a linear programming (LP) model is introduced to optimize the number of inspectors based on cost and inspection volume while considering their capacity and skill levels. Data from large, medium, and small-scale factories reveal that 30%–35% of inspectors exceed the standard requirement due to a lack of awareness among quality managers regarding inspection capacity, skills, and targets. Large-scale companies employ 25% more inspectors for operational flexibility, while medium and small-scale factories exceed standard requirements by 30% and 35%, respectively, to meet inspection demands. This study proposes an inspection cost reduction tool using LP to determine the optimal number of low-, medium-, and high-skilled inspectors per line for a given production target. Findings suggest that implementing this model can reduce the number of inspectors by 30%, leading to significant inspection cost savings without compromising quality.
Agri-food distribution optimization using modified simulated annealing algorithm considering stochastic market demand Dagne, Teshome Bekele
International Journal of Industrial Optimization Vol. 6 No. 1 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v6i1.9406

Abstract

In recent years, the total loss of agricultural fresh product distribution has increased from 20% to 60% of the total amount of harvested products due to their fixed shelf-life time. Consequently, it is essential to select a logistics distribution path that is reasonable for the transportation of fresh agricultural products. To minimize the loss in the distribution of agricultural products in logistics, this study developed an optimization model for agri-food logistic distribution that takes into account the uncertainty of market demand. A novel algorithm called modified simulated annealing (mSA) is introduced to solve a problem with multiple objectives that involves randomness. As a result, the proposed mSA successfully optimizes the availability of the right quantity, quality, and supply chain net profit. The effectiveness of the proposed solution methods is assessed by comparing them with the current state-of-the-art techniques. The findings confirm the effectiveness of the proposed mSA algorithm in tackling the problem across various dimensions. The mSA algorithm led to a decrease in the overall cost of distribution, surpassing the results achieved by SA algorithms. Additionally, the data gathered from the avocado distribution network in the Ethiopian market was used to test the validity of the suggested model. The results showed that as transportation time increased, the quality deterioration rate also increased.
Filling the gap in weighted set covering problem test instances: implications for both researchers and practitioners Vasko, Francis J.; Lu, Yun; Song, Myung Soon; Rando, Dominic
International Journal of Industrial Optimization Vol. 6 No. 1 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v6i1.10836

Abstract

Since 1990, the quality of approximate solution methods for solving weighted set covering problems (WSCPs) has been measured based on how well they solve 65 WSCPs available in Beasley’s OR-Library.  In a 2024 paper, it has been shown that guaranteed optimal solutions can easily be obtained for 55 weighted set covering problems (WSCPs) in Beasley’s OR-Library using general-purpose integer programming software.  These 55 WSCPs have 500 rows and 5,000 columns or less and were solved in a few seconds on a standard PC.  However, the remaining 10 WSCPs have 1000 rows and 10,000 columns and either required considerably more than 1000 seconds to obtain guaranteed optimums (data set NRG) or no optimums were obtained (NRH).  The purpose of this short paper is to try to quantify the solution times needed to solve WSCPs using general-purpose integer programming software that are larger than 500 rows and 5,000 columns, but less than 1,000 rows and 10,000 columns.  This is important because the size and solution time gap is so large that solution times go from a few seconds for the 55 “smaller” WSCPs to very large solution times for the two largest data sets.  To fill this gap, 40 new WSCP instances are defined and their solution times are analyzed to determine when to expect that WSCPs, based on size and density, can be solved to optimality in a timely manner using general-purpose integer programming software like Gurobi or CPLEX.
Localization for transportation and urban planning in smart cities: interest, challenges, and solutions Abbes, Fatma; Sami, Mnasri; Val, Thierry
International Journal of Industrial Optimization Vol. 6 No. 1 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v6i1.11027

Abstract

The concept of a smart city represents an innovative approach to urban development, aiming to enhance residents' quality of life by making cities more adaptable and efficient through the integration of advanced technologies. In recent years, the Internet of Things (IoT) has been widely applied in various smart city domains, including communication, healthcare, and transportation. However, localization has emerged as one of the key challenges in IoT implementation. Localization plays a crucial role in smart city development, as it is essential for effective urban planning, traffic management, and optimizing public transportation routes. Accurate location data enable personalized services for citizens, such as activity recommendations and real-time alerts about local events. Furthermore, by optimizing travel and improving resource management, localization contributes to urban sustainability by reducing waste and enhancing overall efficiency. This research makes several contributions. First, it examines the significance of localization in smart cities and highlights the associated challenges. Next, it explores various indoor and outdoor localization technologies, analyzing their advantages and disadvantages while providing a comparative assessment. The manuscript also classifies communication networks within smart cities, detailing their characteristics. Additionally, it discusses various machine-learning algorithms used to address localization challenges. Finally, it reviews related works in the field, providing insights into existing solutions and future research directions.
Advancements in recommender systems: a comprehensive analysis based on data, algorithms, and evaluation Ma, Xin; Li, Mingyue; Liu, Xuguang
International Journal of Industrial Optimization Vol. 6 No. 1 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v6i1.11107

Abstract

Systematic review and analysis of recommender systems (RSs) in emerging technologies, new scenarios, and diverse user needs are essential for understanding their development, strengthening research, and ensuring sustainability. Using 286 research papers from major databases, this study adopts a systematic review approach to summarize current challenges and future directions in RSs related to data, algorithms, and evaluation. Five key research topics emerge: algorithmic improvement, domain applications, user behavior & cognition, data processing & modeling, and social impact & ethics. Collaborative filtering and hybrid techniques dominate, but RS performance is constrained by eight data issues, twelve algorithmic issues, and two evaluation issues. Major challenges include cold start, data sparsity, data poisoning, interest drift, device-cloud collaboration, non-causal driven models, multitask conflicts, offline data leakage, and multi-objective balancing. Potential solutions include integrating physiological signals for multimodal modeling, mitigating data poisoning via user behavior analysis, evaluating generative recommendations through social experiments, fine-tuning pre-trained models for device-cloud resource allocation, enhancing causal inference with deep reinforcement learning, training multi-task models using probability distributions, implementing cross-temporal dataset partitioning, and evaluating RS objectives across the full lifecycle. The reviewed literature is sourced from major international databases, with future research aiming for broader exploration.
Optimization of throughput rate prediction in animal feed industry using crisp-dm and operational research approaches Lestari, Riri Indah; Andrawina, Luciana; Mufidah, Ilma
International Journal of Industrial Optimization Vol. 6 No. 1 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v6i1.11357

Abstract

The competitive animal feed industry requires efficient production planning to meet market demand, maximize resource use, and sustain profitability. Various raw materials, tools, and techniques are utilized to create animal feed, which results in various variants that might influence throughput rates and thereby alter the accuracy of yield projections. Data mining is applied to train and validate different algorithms to ascertain the most effective model for predicting throughput rates through machine learning. This study uses CRISP-DM to construct an enhanced predictive model for production throughput rate. Due to the model's improved prediction accuracy, scheduling and operational decision-making will be more efficient and cost-effective. The CRISP-DM framework is used to examine historical production data and forecast production levels. Advanced machine learning techniques train and evaluate the model to make accurate predictions that can be mathematically simulated using possible constraints. The findings show that throughput rate predictions are effectively generated by the predictive model that was created using data mining processes. Metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) are used to assess the model and identify the optimal model after attempting using different predictive machine learning techniques. With the linear regression algorithm and MAE values of 5,186, MSE of 1,585, and RMSE of 5,970.32, the best prediction model test results have been determined. An optimal scheduling simulation is conducted from the selected model, with the constraint of the customer's delivery requirements and the time capacity, specifically a maximum throughput rate prediction of 23.78 tons/hour. However, this study reveals how the data mining process is applied to the decision-making process with the use of operation research support so that the optimal production rate prediction is 22 tons/hour.

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