International Journal of Industrial Optimization http://journal2.uad.ac.id/index.php/ijio <table class="data" width="100%" bgcolor="#f0f0f0"> <tbody> <tr valign="top"> <td width="30%">Journal title</td> <td width="70%"><strong>International Journal of Industrial Optimization</strong></td> </tr> <tr valign="top"> <td width="30%">Initials</td> <td width="70%"><strong>IJIO</strong></td> </tr> <tr valign="top"> <td width="30%">Abbreviation</td> <td width="70%"><em><strong><strong>Int. J. Ind. Optim.</strong></strong></em></td> </tr> <tr valign="top"> <td width="30%">Frequency</td> <td width="70%"><strong><a href="http://journal2.uad.ac.id/index.php/ijio/management/settings/context//index.php/ijio/issue/archive" target="_blank" rel="noopener">2 issues per year</a> | <a href="http://journal2.uad.ac.id/index.php/ijio/management/settings/context//index.php/ijio/issue/archive" target="_blank" rel="noopener">February and September</a></strong></td> </tr> <tr valign="top"> <td width="30%">DOI</td> <td width="70%"><strong>Prefix 10.12928</strong><strong> by <img src="http://journal2.uad.ac.id/index.php/ijio/management/settings/context//public/site/images/dyoyo/CROSREFF_Kecil2.png" alt="" /></strong></td> </tr> <tr valign="top"> <td width="30%">ISSN</td> <td width="70%"><strong><a href="https://portal.issn.org/resource/ISSN/2714-6006#" target="_blank" rel="noopener">2714-6006</a> (printed)/<a title="e-ISSN" href="https://portal.issn.org/resource/ISSN/2723-3022" target="_blank" rel="noopener">2723-3022</a> (online)</strong></td> </tr> <tr valign="top"> <td width="30%">Editor-in-chief</td> <td width="70%"><a href="https://www.scopus.com/authid/detail.uri?authorId=55489479200" target="_blank" rel="noopener"><strong>Hayati Mukti Asih</strong></a></td> </tr> <tr valign="top"> <td width="30%">Publisher</td> <td width="70%"><a href="https://uad.ac.id/en/"><strong>Universitas Ahmad Dahlan</strong></a> i<strong>n collaboration with <a href="https://app.powerbi.com/view?r=eyJrIjoiYzhlYjMyZTMtMzVmMS00YzNmLTkyY2YtZWMyNzBmZjY5YjUyIiwidCI6IjM0NjI3ODc0LWVkM2EtNDk3Yy04ZmI5LTE2Y2U3ZTk3NjRmMSIsImMiOjEwfQ%3D%3D&amp;pageName=ReportSection">BKSTI (Badan Kerjasama Penyelenggara Pendidikan Tinggi Teknik Industri)</a></strong></td> </tr> <tr valign="top"> <td width="30%">Citation Analysis</td> <td width="70%"><strong><a href="https://scholar.google.com/citations?hl=en&amp;user=gopOqDAAAAAJ" target="_blank" rel="noopener">Google Scholar</a>, <a href="https://app.dimensions.ai/discover/publication?search_mode=content&amp;and_facet_source_title=jour.1387120">Dimension</a>, <a href="https://journals.indexcopernicus.com/search/details?id=67231&amp;lang=pl">ICI</a>, <a href="https://sinta.kemdikbud.go.id/journals/profile/8798">Sinta</a>, <a href="https://www.proquest.com/publication/publications_5340591?accountid=188440">ProQuest</a>, <a href="https://ezb.uni-regensburg.de/detail.phtml?bibid=UBE&amp;colors=7&amp;lang=en&amp;jour_id=484076">Ebsco</a>, <a href="http://journal2.uad.ac.id/index.php/ijio/citedness">Scopus</a></strong></td> </tr> </tbody> </table> <hr /> <div style="text-align: justify;">The <strong>International Journal of Industrial Optimization</strong> (IJIO) is an international journal published by Universitas Ahmad Dahlan, Indonesia. This is an open-access and peer-reviewed journal which is published twice a year (<a href="http://journal2.uad.ac.id/index.php/ijio/management/settings/context//index.php/ijio/issue/archive" target="_blank" rel="noopener">2 issues/year</a>) in February and September. The first volume of IJIO was launched in February 2020. IJIO covers theoretical and empirical questions in industrial optimization. The span of coverage ranges from the basic implementation of production systems and simulation to advanced problems such as data mining and metaheuristics. Authors are encouraged to submit manuscripts that connect the gaps between research, development, and implementation.</div> <div style="text-align: justify;"> </div> <div style="text-align: justify;"> <div style="text-align: justify;"><strong>Before submission</strong>,<br />You have to make sure that your paper is prepared using the<a href="https://docs.google.com/document/d/1svR1_x8pyLQYMZGhIOCTKORdXg05iPfe/edit?usp=sharing&amp;ouid=116281130901150997990&amp;rtpof=true&amp;sd=true" target="_blank" rel="noopener"><strong> IJIO TEMPLATE </strong></a>and Carefully <strong><a href="http://journal2.uad.ac.id/index.php/ijio/management/settings/context//index.php/ijio/about/submissions#authorGuidelines" target="_blank" rel="noopener">read the submission guidelines</a>. </strong>Submit your paper <strong>ONLY in English. </strong>If you have problems with the journal, please contact us at <a href="http://journal2.uad.ac.id/index.php/ijio/management/settings/context/mailto:hayati.asih@ie.uad.ac.id">hayati.asih@ie.uad.ac.id</a></div> </div> Universitas Ahmad Dahlan en-US International Journal of Industrial Optimization 2714-6006 <p><strong>License and Copyright Agreement</strong></p> <p>In submitting the manuscript to the journal, the authors certify that:</p> <ul> <li>They are authorized by their co-authors to enter into these arrangements.</li> <li>The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal. Please also carefully read the International Journal of Industrial Optimization (IJIO) Author Guidelines at <a href="http://journal2.uad.ac.id/index.php/ijio/management/settings/distribution//index.php/ijio/about/submissions#onlineSubmissions">http://journal2.uad.ac.id/index.php/ijio/about/submissions#onlineSubmissions</a></li> <li>That it is not under consideration for publication elsewhere,</li> <li>That its publication has been approved by all the author(s) and by the responsible authorities tacitly or explicitly of the institutes where the work has been carried out.</li> <li>They secure the right to reproduce any material that has already been published or copyrighted elsewhere.</li> <li>They agree to the following license and copyright agreement.</li> </ul> <p><strong>Copyright</strong></p> <p>Authors who publish with the International Journal of Industrial Optimization (IJIO) agree to the following terms:</p> <ol start="1"> <li>Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a <a href="http://creativecommons.org/licenses/by-sa/4.0/" target="_blank" rel="noopener">Creative Commons Attribution License (CC BY-SA 4.0)</a> that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.</li> <li>Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.</li> <li>Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.</li> </ol> Sustainable waste solutions: Optimizing location-allocation of 3R waste management sites in Gondokusuman, Yogyakarta, Indonesia through multi-maximal covering location approach http://journal2.uad.ac.id/index.php/ijio/article/view/9251 <p>Developing a Multi-Maximal Covering Location Model (MMCLM) for waste management in Gondokusuman Sub-district, Yogyakarta, Indonesia, is urgently needed. The closure of the Piyungan landfill has resulted in the need for additional Reduce, Reuse, and Recycle Waste Management Sites (3R-WMSs) to reduce waste that the landfill cannot accommodate. The primary objective of this model is to optimize the location and allocation of demand volume nodes, representing the resident population, to a specific set of 3R-WMS. These demand nodes are located at different distances from 3R-WMSs, including high and low coverage areas. The research in the Gondokusuman Sub-district employed MMCLM with facility capacity constraints and was developed using mixed integer linear programming methodology. The study identified five optimal locations for a 3R-WMS establishment that comprehensively cover all demand nodes (15301) and population clusters (45903) in the sub-district, including both high (5085) and low coverage areas (10216). This research represents a significant step forward in developing a sustainable environment by ensuring easy access to reducing, reusing, and recycling-based waste management facilities for residents.</p> Achmad Chairdino Leuveano Puji Handayani Kasih Muhammad Ihsan Ridho Ahmad Rif’an Khoirul Lisan Muhammad Zeeshan Rafique Ariff Azly Muhamed Copyright (c) 2024 Achmad Chairdino Leuveano, Puji Handayani Kasih, Muhammad Ihsan Ridho, Ahmad Rif’an Khoirul Lisan, Muhammad Zeeshan Rafique , Ariff Azly Muhamed https://creativecommons.org/licenses/by-sa/4.0 2024-01-30 2024-01-30 1 15 Decision support system in determining the location of new supermarket branches using the copras method http://journal2.uad.ac.id/index.php/ijio/article/view/9061 <p>Supermarkets are one of the ideal and profitable retail business sectors to try because they are located in various urban and rural areas. This causes many people to be interested in setting up a supermarket. However, determining a strategic location is not easy and requires many strategic location considerations. The research objective is to develop a Decision Support System (DSS) to determine the location of new supermarket branches using the Complex Proportional Assessment (COPRAS) method, which is expected to be helpful for management and supermarket partners as a business strategy. The COPRAS method excels in calculating alternative utilities and selecting the best alternative. There are nine criteria (land rental price, distance to competitors, security, distance to education, warehouse distance, cleanliness, land area, building price, crowd) and five alternative locations (Juanda, Hos Cokroaminoto, Bayangkara, Batoro Katong, Sumoroto) are considered. This research created a web-based DSS that selects the best location for supermarket, with Juanda (A1) ranked first and scored 100, followed by Somoroto (location A5) with a score of 99.861, Bayangkara (A3) with a score of 97.099, Batoro Katong (A4) with a score of 91.293, and HOS Cokroaminoto (A2) with a score of 88.877. From the results of the COPRAS calculation, it can be concluded that Juanda is the best location to build a new supermarket branch location. This result provides a valuable tool for management and supermarket partners seeking to make informed decisions about branch expansion strategies.</p> Siti Lathifah Tsaqila Sri Winiarti Ida Widaningrum Copyright (c) 2024 Siti Lathifah Tsaqila, Sri Winiarti , Ida Widaningrum https://creativecommons.org/licenses/by-sa/4.0 2024-01-30 2024-01-30 16 30 Queuing analysis and optimization of public vehicle transport stations: A case of South West Ethiopia region vehicle stations http://journal2.uad.ac.id/index.php/ijio/article/view/7963 <p>Modern urban environments present a dynamically growing field where, notwithstanding shared goals, several mutually conflicting interests frequently collide. However, it has a big impact on the city's socioeconomic standing, waiting lines and queues are common occurrences. This results in extremely long lines for vehicles and people on incongruous routes, service coagulation, customer murmuring, unhappiness, complaints, and looking for other options, sometimes illegally. The root cause is corruption, which leads to traffic jams, stops and packs vehicles beyond their safe carrying capacity, and violates passengers' human rights and freedoms. This study focused on optimizing the time passengers had to wait in public vehicle stations. This applied research employed both data-gathering sources and mixed approaches. Then, 166 samples of key informants of transport stations were taken using the Slovin sampling formula. The time vehicles, including the drivers and auxiliary drivers ‘Weyala', had to wait was also studied. To maximize the service level at vehicle stations, a queuing model was subsequently devised ‘Menaharya’. Time, cost, and quality encompass performance, scope, and suitability for the intended purposes. The study also focused on determining the minimal response time required for passengers and vehicles queuing to reach their ultimate destinations within the transportation stations in Tepi, Mizan, and Bonga. A new bus station system was modeled and simulated by Arena simulation software in the chosen study area. 84% improvement on cost reduced by 56.25%, time 4 hours to 1.5 hours, quality, safety and designed load performance calculations employed. Stakeholders are asked to implement the model and monitor the results obtained.</p> Mequanint Birhan Copyright (c) 2024 Mequanint Birhan https://creativecommons.org/licenses/by-sa/4.0 2024-01-30 2024-01-30 31 44 Research on bearing fault diagnosis technology based on machine learning http://journal2.uad.ac.id/index.php/ijio/article/view/8106 <p>As industrial equipment complexity continues to rise, the importance of bearings within these systems has become more critical, given their pivotal role in equipment functionality. Bearing faults can result in severe production accidents and safety issues. Hence, there is an urgent need for advanced bearing fault diagnosis technology. This study concentrates on rolling bearings, analyzing their structural characteristics and key parameters to classify fault types—inner race faults, rolling element faults, and outer race faults. Utilizing a dataset of 80 sets of bearing factory data, time and frequency domain analyses are conducted, establishing seven feature parameters (five in the time domain and two in the frequency domain). This data is organized into a 7-dimensional matrix for subsequent analysis and model development. The K-Means algorithm is chosen for its effectiveness in automatically recognizing fault patterns in rolling bearings. Training on the 7-dimensional matrix identifies four clustering centers corresponding to normal conditions, inner race faults, rolling element faults, and outer race faults. The fault diagnosis system is implemented using Python, and algorithm optimization improves efficiency. The study concludes with insights drawn from the analysis and proposes optimization methods, which contributing to advancing bearing fault diagnosis technology, particularly addressing industrial equipment reliability and safety concerns.</p> Yu Xia XiaoJun Guo ErChuan Su LingPei Kong Copyright (c) 2024 Yu Xia, XiaoJun Guo, ErChuan Su, LingPei Kong https://creativecommons.org/licenses/by-sa/4.0 2024-02-28 2024-02-28 45 59 Modeling and simulation of friction stir welding process: A neural approach http://journal2.uad.ac.id/index.php/ijio/article/view/9010 <p>Friction Stir Welding (FSW) stands out as a groundbreaking method in solid-state joining for aluminum alloys, presenting an innovative way to achieve joints of exceptional quality. This research delves into the application of FSW for bonding, focusing on plates that are 6mm thick and made from aluminum alloys Al6063, Al5083, and AL6061, aiming to produce a variety of FSW joints. To evaluate the quality of these joints, the study compares mechanical properties such as tensile strength, safe bending strength, and bending toughness necessary for achieving a 90° bend. The investigation leverages welding data to formulate a neural model, starting with using a conventional feedforward neural model (CFNM). It tackles the limitations of CFNM, including its intensive training requirements and the challenge of dealing with unknown configurations, by proposing a new, more adaptable neural network model known as FNNM. When comparing the two models, it becomes evident that CFNM is constrained by a root mean square error (RMSE) of 7-15%, whereas FNNM marks a significant improvement with a minimal RMSE of 1-3%. This indicates that FNNM improves accuracy and effectively navigates the complexities of modeling with unknown parameters. Through this study, insightful contributions are made to understanding FSW in joining aluminum alloys and developing an advanced neural model capable of predicting the outcomes of welding with greater precision.</p> Devendra Kumar Chaturvedi Atul Suri Copyright (c) 2024 Devendra Kumar Chaturvedi, Atul Suri https://creativecommons.org/licenses/by-sa/4.0 2024-02-28 2024-02-28 60 80 Multi-objective elitist spotted hyena resource optimized flexible job shop scheduling http://journal2.uad.ac.id/index.php/ijio/article/view/8743 <p>The job shop scheduling problem (JSSP) has drained a lot of consideration since it is one of the most important optimization problems in the manufacturing domain. The scheduling method is crucial for optimizing the objective of minimizing makespan among thousands of jobs, but evaluating machine capacity for achieving this goal remains challenging despite the development of various population-based optimization algorithms for job shop scheduling problems. To improve the efficiency of Job shop scheduling, a novel Multi-objective Elitist Spotted Hyena Monotonic Scheduling (MESHS) technique is introduced. The proposed MESHS technique includes two major processes: machine selection and operation sequences. The number of jobs is considered for solving the scheduling problem. First, the machine selection is performed by applying the Multi-objective Elitist Spotted Hyena optimization technique. The optimization technique selects the optimal machines parallelly based on multiple objective functions such as energy consumption, CPU utilization, and job completion time. The fitness of every machine is calculated based on these multiple objective functions using Levenberg–Marquardt method. Then the Elitist strategy is applied to select the optimal machine based on fitness. After the machine selection, the rate-monotonic preemptive scheduling is modeled to provide a robust operation sequence by assigning high-priority jobs to the optimal machines. As a result, efficient job scheduling is achieved with minimum time. Finally, the experimental valuation is carried out using a benchmark OR-Library dataset with different factors such as job shop scheduling efficiency, job scheduling time, makespan, and memory consumption concerning a number of jobs.</p> A. N. Senthilvel T. Hemamalini G. Geetha Copyright (c) 2024 A. N. Senthilvel, T. Hemamalini , G. Geetha https://creativecommons.org/licenses/by-sa/4.0 2024-02-28 2024-02-28 81 92