International Journal of Industrial Optimization <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="" target="_blank" rel="noopener">2 issues per year</a> | <a href="" 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="" alt="" /></strong></td> </tr> <tr valign="top"> <td width="30%">ISSN</td> <td width="70%"><strong><a href="" target="_blank" rel="noopener">2714-6006</a> (printed)/<a title="e-ISSN" href="" 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="" 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=""><strong>Universitas Ahmad Dahlan</strong></a> i<strong>n collaboration with <a href=";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=";user=gopOqDAAAAAJ" target="_blank" rel="noopener">Google Scholar</a>, <a href=";and_facet_source_title=jour.1387120">Dimension</a>, <a href=";lang=pl">ICI</a>, <a href="">Sinta</a>, <a href="">ProQuest</a>, <a href=";colors=7&amp;lang=en&amp;jour_id=484076">Ebsco</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="" 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=";ouid=116281130901150997990&amp;rtpof=true&amp;sd=true" target="_blank" rel="noopener"><strong> IJIO TEMPLATE </strong></a>and Carefully <strong><a href="" 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=""></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=""></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="" 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> Short-term scheduling of hybrid thermal, pumped-storage, and wind plants using firefly optimization algorithm <p>This paper presents a novel method based on an enhanced firefly algorithm (EFA) to solve scheduling hybrid thermal, pumped-storage, and wind plants. Since the scheduling problem is inherently discrete, basic EFA and binary encoding/decoding techniques are used in the proposed EFA approach. Optimal power values of thermal and pumped-storage units are determined separately in the presence of uncertainty caused by wind speed. The proposed method is applied to a real plant, including four pumped-storage units, 34 thermal units with different characteristics, and one wind turbine plant. In addition, dynamic constraints of upstream and downstream sources and constraints regarding thermal and wind units are also considered for finding the optimal solution. In addition, the proposed EFA is successfully applied to a real plant, and the results are compared with those of the three available methods. The results show that the proposed method has converted to a more optimal cost than the other methods.</p> Alireza Moghaddas S. M. Hassan Hosseini Copyright (c) 2022 Alireza Moghaddas, S. M. Hassan Hosseini 2022-09-20 2022-09-20 3 2 80 97 10.12928/ijio.v3i2.5994 Ecpoc: an evolutionary computation-based proof of criteria consensus protocol <p>Recently, blockchain technology has been applied in many domains in our life. Blockchain networks typically utilize a consensus protocol to achieve consistency among network nodes in a decentralized environment. Delegated Proof of Stake (DPoS) is a popular mechanism adopted in many networks such as BitShares, EOS, and Cardano because of its speed and scalability advantages. However, votes that come from nodes on a DPoS network tend to support a set of specific nodes that have a greater chance of becoming block producers after voting rounds. Therefore, only a small group of nodes can be selected to become block producers. To address this issue, we propose a new protocol called Evolutionary Computation-based Proof of Criteria (ECPoC), which uses ten criteria to evaluate and select a new block procedure in each round. Next, a set of optimal weights used for maximizing the network’s decentralization level is identified through the use of evolutionary computation algorithms. The experimental results show that our consensus significantly enhances the degree of decentralization in the selection process of witness nodes compared to DPoS. As a result, ECPoC facilitates fairness between nodes and creates momentum for blockchain network development</p> Thang Nguyen Hoang-Nam Dinh Van-Thanh Nguyen Bao Son Do Thi Tam Nguyen Ba Lam Do Copyright (c) 2022 Thang Nguyen, Hoang-Nam Dinh, Van-Thanh Nguyen, Bao Son Do, Thi Tam Nguyen, Ba Lam Do 2022-09-20 2022-09-20 3 2 98 109 10.12928/ijio.v3i2.6049 Graphology analysis for detecting hexaco personality and character through handwriting images by using convolutional neural networks and particle swarm optimization methods <p>Graphology or handwriting analysis can be used to infer the traits of the writers by examining each stroke, space, pressure, and pattern of the handwriting. In this study, we infer a six-dimensional model of human personality (HEXACO) using a Convolutional Neural Network supported by Particle Swarm Optimization. These personalities include Honesty-Humility, Emotionality, eXtraversion, Agreeableness (versus Anger), Conscientiousness, and Openness to Experience. A digital handwriting sample data of 293 different individuals associated with 36 types of personalities were collected and derived from the HEXACO space. A convolutional neural network model called GraphoNet is built and optimized using Particle Swarm Optimization (PSO). The PSO is used to optimize epoch, minibatch, and droupout parameters on the GraphoNet. Although predicting 32 personalities is quite challenging, the GraphoNet predicts personalities with 71.88% accuracy using epoch 100, minibatch 30 and dropout 52% while standard AlexNet only achieves 25%. Moreover, GraphoNet can work with lower resolution (32 x 32 pixels) compared to standard AlexNet (227 x 227 pixels).</p> Alvin Barata Habibullah Akbar Marzuki Pilliang Anwar Nasihin Copyright (c) 2022 Alvin Barata, Habibullah Akbar, Marzuki Pilliang, Anwar Nasihin 2022-09-20 2022-09-20 3 2 110 120 10.12928/ijio.v3i2.6242 Minimizing production cost for kendang djembe production through goal programming model <p>Kendang djembe is a percussion instrument played by striking with the fingers and palms. The body of the kendang djembe is generally made of wood and shaped like a cup or mug, carved either by machine or traditionally only by hand. This problem affects the production costs, the employee working house, and the profit of kendang djembe. Customer orders and requirements determine the production process for kendang djembe. It leads to fluctuations in market demand, affecting production costs and the working hours of kendang djembe employees. As a result, employees work overtime when orders rush in, resulting in poor product finishes such as crude engraving and painting. This research aims to minimize the production cost of kendang djembe, maximize the employee working hours, and maximize the profit by using the goal programming method. Goal programming is applied to decide the number of kendang djembe, the minimum production cost, and the time for each kendang djembe. The result of this research is that CV. Maharani Abadi has to make 237 units of kendang djembe paintings, 1266 kendang djembe carvings, 870 kendang djembe painting carvings, and 91 kendang djembe deep carvings. CV. Maharani spent a production cost of Rp 399,413,400, with the employee working 1846.36 hours, and obtained a maximum profit of Rp 126,526,600. This research helps the company to avoid unprofitable options in the production process of kendang djembe.</p> Nabila Puspanola Sumiati Sumiati Copyright (c) 2022 Nabila Puspanola, Sumiati 2022-12-10 2022-12-10 3 2 121 130 10.12928/ijio.v3i2.6658 Redesign of water filter using design for manufacturing and assembly to minimize cost and time <p>Kembang Belor Village is one of the villages that have a water resource but are not yet precisely utilized and need a water filter. The current water filter is not suited for users’ needs because they cannot afford it. Therefore, this research intends to redesign the water filter. This research aims to reduce production costs and increase the efficiency of design and assembly time. The method employed is Design for Manufacturing and Assembly (DFMA). The results show that the production cost decreased by 50.83%; design efficiency increased by 37.5%; production, handling, and assembly time improved by 51.7%, 33.3%, and 52.7% for each; the number of ppm decreased from 142 to 95. For the contribution, few previous research uses the DFMA method to analyze and redesign the water filter. These findings contribute in helping the user recognize new options to design efficient water filters for future needs.</p> Ignadia Zain Balqis Rusindiyanto Rusindiyanto Endang Pudji Widjajati Copyright (c) 2022 Ignadia Zain Balqis, Rusindiyanto, Endang Pudji Widjajati 2022-12-22 2022-12-22 3 2 131 140 10.12928/ijio.v3i2.6647 Analysis of sag mill machine performance using overall equipment effectiveness and failure model and effects analysis method <p>The mining company uses a variety of grinding machines to process minerals, whereas the most common type of machine is the Semi-Autogenous Grinding SAG Mill machine. This machine is employed for the mining process of hard rock as raw material into gold, copper, and silver. However, the SAG Mill machines are often broken, even suddenly not working, with an average loss time of 97.30 hours which impacts a decrease in efficiency and production quality of up to 40%. It can cause losses that do not reach the production target. This research aims to measure the effectiveness of the SAG Mill machine and determine the failure using the OEE and FMEA methods. The results showed that the SAG Mill machine is still under standardized based on the Japan Institute of Plant Maintenance (JIPM), which is 85%. The FMEA method and RPN value apply to analyze downtime losses, and idling is the loss that highly affects the effectiveness of SAG Mill machines. Recommendations for the company are to increase the number of equipment that aims to prolong the machine's age and accelerate production. This research contributes to another solution to help maintenance managers by measuring the effectiveness and determining the failure of the SAG Mill machine</p> Sinta Luluk Fuadiya Endang Pudji Widjajati Copyright (c) 2022 Sinta Luluk Fuadiya, Endang Pudji Widjajati 2022-12-30 2022-12-30 3 2 141 153 10.12928/ijio.v3i2.6701