cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Hapsoro Agung Jatmiko
Contact Email
hapsoro.jatmiko@ie.uad.ac.id
Phone
+6289675274807
Journal Mail Official
ijio@ie.uad.ac.id
Editorial Address
Universitas Ahmad Dahlan, 4th Campus Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191 Phone: +62 (274) 563515, 511830, 379418, 371120 ext. 4902, Fax: +62 274 564604
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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
 6   7   8   9   10 
Product pricing based on customer perception quality and service convenience using interval type-2 fuzzy logic system Purnomo, Muhammad Ridwan Andi; Saputro, Iswoyo Seno
International Journal of Industrial Optimization Vol. 5 No. 2 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v5i2.10825

Abstract

In the competitive landscape of customer goods, particularly in the wrapping paper industry, pricing strategies are critical to achieving market success. This study presents a novel approach to product pricing by integrating customer perception quality and service and convenience factors using interval type-2 fuzzy logic system (IT2FLS). The customer perception quality factor is subdivided into material quality and aesthetics design sub-factors while the service and convenience factor comprise web-based ordering system as well as the web-based post-sale customer engagement. The methodology involves collecting data through customer surveys and expert evaluations to quantify the perceived importance and performance of each sub-factor. The IT2FLS is employed to handle the inherent uncertainty and imprecision in experts’ judgment, providing a robust framework for aggregating these qualitative assessments into a comprehensive pricing model. This IT2FLS allows for the dynamic adjustment of pricing based on fluctuating customer perceptions and service levels. The outcome of the proposed IT2FLS is a pricing factor that serves as a multiplier for the standard product price established by the company. The new product prices have been validated also considering historical data and it was found that the prices remain acceptable to customers without drastically impacting sales. This study contributes to the body of knowledge on pricing strategies by offering a sophisticated, mathematically grounded approach that accounts for the complex, fuzzy nature of customer preferences. The proposed model not only enhances pricing accuracy but also provides a flexible tool for managers to adapt pricing strategies in real-time based on customer feedback and service performance.
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.
Joint production and human replacement optimization policy for a deteriorating manufacturing system Hanan Majria; Honorine Angue Mintsa; , Guy-Richard Kibouka; Jean-Pierre Kenne
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.v6i2.11284

Abstract

This article examines the integration of production and human resource and human resource management, considering the operator as a production unit whose efficiency decreases over time, in an unreliable production system marked by significant deterioration. This deterioration impacts the reliability and continuity of the production unit in two main ways. To mitigate the impact of this deterioration, a replacement action can be implemented based on the system's current state. The objective of this study is to establish an effective production policy and replacement strategy to meet customer demand. We employ a combination of stochastic dynamic programming and numerical methods to solve this optimal control problem. Additionally, a numerical example is presented to demonstrate the applicability of the proposed approach and to explore the interaction between a specific production strategy and human resource management. The main contribution of this research lies in the development of innovative methods and solutions aimed at optimizing the performance of a complex system through stochastic optimal control. The impact of the new approach, based on a logical implementation, is discussed following a sensitivity analysis of the numerical example. The results include a comparative study between recent research and the proposed policy. Lastly, an implementation chart is created to assist decision-makers in determining production rates and managing human resources effectively to meet customer demand.
A simulation framework for emergency evacuation, considering navigation errors Nahum, Oren E.; Mayost, Omri
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.v6i2.11406

Abstract

The rapidly growing tourism industry has brought forth substantial safety concerns, particularly in the context of emergency evacuations necessitated by natural and human-induced disasters. Tourists often lack the necessary orientation, information, and preparedness, rendering them vulnerable during such crises. While research has extensively explored tourist behavior and evacuation procedures independently, the intersection of these two fields remains underexamined.  This study introduces a simulation model utilizing MATSim to characterize the behaviors of tourists during emergencies, highlighting navigation errors and decision-making processes. Two novel routers - “Random Walk” and “Landmark Assisted” - have been developed to better reflect tourist navigation challenges. A case study in Conegliano, Italy, demonstrates the effectiveness of these routers under two evacuation policies: predefined and undefined destinations. Results indicate significant disparities in evacuation times: optimal routes average 50 minutes, while random navigation extends this to 544 minutes. The Landmark Assisted router improves evacuation to 73 minutes, underscoring the importance of identifiable landmarks. Additionally, managing intersections further reduces evacuation times. This simulation framework serves as a decision-making tool for evaluating evacuation policies, providing insights into optimizing resource allocation and enhancing overall efficacy in emergency scenarios. Future research should focus on developing optimization algorithms for intersection management selection, reinforcing the practical applicability of this model in real-world contexts.
Collaborative digital marketing and supply chain management for micro, small and medium enterprises Agus Mansur; Razel Thimoty; Syafa Thania Prawibowo; Wahyudi Sutrino; Fadhil Adita Ramadhan; Tiara Febian
International Journal of Industrial Optimization Vol. 6 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study explores specific supply chain challenges faced by Batik Ayu Arimbi, a small-scale business in the batik industry, particularly how the manual calculations in its make-to-stock system impact its financial accuracy and operational efficiency. The company has been experiencing financial losses due to these inaccuracies. To address these issues, this research proposes improvements in supply chain management by implementing Business Process Model Notation (BPMN) to streamline process visualization and coordination among the artisans, showrooms, and production houses. The use of digital marketing platforms, particularly Instagram, is also suggested to optimize marketing efforts and achieve sales targets. This study contributes to the literature by emphasizing novel BPMN implementation aspects and demonstrating the effectiveness of digital marketing for MSMEs in the textile sector. The qualitative data was collected through semi-structured interviews, which provided insights into current practices and areas for improvement. The findings show that increased cooperation between supply chain actors reduces inventory errors by 20% and increases the accuracy of financial tracking by 15%, thereby reducing operational risks. Furthermore, the digital marketing strategies increased customer engagement rates by 25%, directly contributing to sales growth. The findings further suggest that the proposed solutions not only resolve the identified problems but also provide a scalable model for enhancing resilience in Batik Ayu Arimbi’s supply chain operations as one of MSMEs. In conclusion, boosting sustainability and performance in MSMEs requires improved supply chain collaboration and the strategic application of digital tools.

Page 9 of 10 | Total Record : 94

 6   7   8   9   10 

Filter by Year

2020 2025


Reset
Filter By Issues
All Issue Vol. 6 No. 2 (2025) Vol. 6 No. 1 (2025) Vol. 5 No. 2 (2024) Vol. 5 No. 1 (2024) Vol. 4 No. 2 (2023) Vol. 4, No. 1 (2023) Vol. 3 No. 2 (2022) Vol. 3 No. 1 (2022) Vol 2, No 2 (2021) Vol. 2 No. 2 (2021) Vol. 2 No. 1 (2021) Vol 2, No 1 (2021) Vol 1, No 2 (2020) Vol. 1 No. 2 (2020) Vol. 1 No. 1 (2020) Vol 1, No 1 (2020) More Issue