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            <front>

                <journal-meta>
                                    <journal-id></journal-id>
            <journal-title-group>
                                                                                    <journal-title>Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi</journal-title>
            </journal-title-group>
                                        <issn pub-type="epub">2147-5881</issn>
                                                                                            <publisher>
                    <publisher-name>Pamukkale Üniversitesi</publisher-name>
                </publisher>
                    </journal-meta>
                <article-meta>
                                        <article-id/>
                                                                <article-categories>
                                            <subj-group  xml:lang="en">
                                                            <subject>Engineering</subject>
                                                    </subj-group>
                                            <subj-group  xml:lang="tr">
                                                            <subject>Mühendislik</subject>
                                                    </subj-group>
                                    </article-categories>
                                                                                                                                                        <title-group>
                                                                                                                        <trans-title-group xml:lang="tr">
                                    <trans-title>Kaynak atölyesi çizelgeleme probleminin değişken iş istasyonu kısıtlaması altında optimizasyonu: Arena simülasyonu tabanlı genetik algoritma ile endüstriyel bir uygulama</trans-title>
                                </trans-title-group>
                                                                                                                                                                                                <article-title>Optimization of welding job-shop scheduling problem under variable workstation constraint: an industrial application with Arena simulation based genetic algorithm</article-title>
                                                                                                    </title-group>
            
                                                    <contrib-group content-type="authors">
                                                                        <contrib contrib-type="author">
                                                                <name>
                                    <surname>Karaoglan</surname>
                                    <given-names>Aslan Deniz</given-names>
                                </name>
                                                                    <aff>BALIKESİR ÜNİVERSİTESİ</aff>
                                                            </contrib>
                                                                                </contrib-group>
                        
                                        <pub-date pub-type="pub" iso-8601-date="20220228">
                    <day>02</day>
                    <month>28</month>
                    <year>2022</year>
                </pub-date>
                                        <volume>28</volume>
                                        <issue>1</issue>
                                        <fpage>139</fpage>
                                        <lpage>147</lpage>
                        
                        <history>
                                    <date date-type="received" iso-8601-date="20210114">
                        <day>01</day>
                        <month>14</month>
                        <year>2021</year>
                    </date>
                                                    <date date-type="accepted" iso-8601-date="20210414">
                        <day>04</day>
                        <month>14</month>
                        <year>2021</year>
                    </date>
                            </history>
                                        <permissions>
                    <copyright-statement>Copyright © 2013, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi</copyright-statement>
                    <copyright-year>2013</copyright-year>
                    <copyright-holder>Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi</copyright-holder>
                </permissions>
            
                                                                                                <trans-abstract xml:lang="tr">
                            <p>İş atölyesi planlaması, &#039;emek yoğun proje tipi üretim&#039; için zor bir konudur. Çünkü bu tür bir üretimde, gerçek işlem süreleri üretim bitene kadar tam olarak bilinmez ve bu işlem süreleri siparişin teknik özelliklerine göre değişir. İşlem sürelerini tahmin etmek için olasılık dağılımlarını kullanmak uygun bir yöntemdir. Bu makale, emek-yoğun proje tipi çalışan kaynak atölyesinin değişken iş istasyonu kısıtlamaları altında planlanması için endüstriyel bir uygulama sunmaktadır. Bu kısıt, ürünlerin boyuna bağlı olarak ortaya çıkan özel bir üretim şeklinin sonucudur. Amaç, bir grup bekleyen iş emrinin tamamlanma süresini en aza indirmektir. Genetik algoritma (GA) bu amaçla, atölyeye girmeyi bekleyen iş emirlerinin atölyeye giriş sırasını oluşturmak ve bunları 6 özdeş kaynak istasyonuna göndermek için kullanılır. Atölyenin dinamik koşulları, Arena simülasyon programı ile simüle edilir. Algoritmanın girdi verileri olarak stokastik işlem süreleri kullanılır. Kaynak iş istasyonu çizelgeleme için değişken iş istasyonu kısıtlaması altında stokastik işleme sürelerinin kullanılması daha önce araştırılmamıştır. Deneysel sonuçlara göre, GA ve Arena simülasyonu birlikte, değişken iş istasyonu kısıtlaması altında bu tür problemlerde bir grup işin toplam tamamlanma zamanını etkili bir şekilde azaltmaktadır. GA destekli Arena çizelgesi, bu sorun için GA kullanmadan önerilen çizelgeden daha iyi performans gösterir. Simülasyon sonuçları, bekleyen siparişlerin toplam üretim süresinin, GA kullanılmadan önerilen çizelgelerle karşılaştırıldığında yaklaşık % 9,25 oranında azaldığını göstermektedir</p></trans-abstract>
                                                                                                                                    <abstract><p>Job-shop scheduling is a difficult issue for &#039;labor-intensive project type manufacturing&#039;. Because in this type of production, the actual processing times are not exactly known until the production is finished and these processing times vary depending on the order’s technical specifications. It is an appropriate method to use probability distributions to forecast the processing times. This paper provides an industrial application for the scheduling of a labor-intensive project type working welding job-shop under variable workstation constraints. This constraint is consequence of a special production type that is depending on the length of the products. The aim is minimizing the makespan of a group of waiting orders. Genetic algorithm (GA) is used for this purpose to establish the entry sequence of the job-shop&#039;s waiting orders and dispatching them to the 6 identical welding stations. The dynamic conditions of the job-shop are simulated by the Arena simulation program. Stochastic processing times are used as the input data of the algorithm. Using stochastic processing times under variable workstation constraint for welding job-shop scheduling is not investigated previously. According to the experimental results, GA and Arena simulation together effectively reduces the makespan in this type of problem under variable workstation constraint. The GA aided Arena schedule outperforms the schedules proposed without using GA for this problem. Simulation results indicate that the total manufacturing time of pending orders is nearly 9.25% reduced when compared with the schedules proposed without using GA.</p></abstract>
                                                            
            
                                                                                        <kwd-group>
                                                    <kwd>Arena simulation</kwd>
                                                    <kwd>  Genetic algorithm</kwd>
                                                    <kwd>  Labor-intensive
project type production</kwd>
                                                    <kwd>  Makespan minimization</kwd>
                                                    <kwd>  Variable
workstation constraint</kwd>
                                                    <kwd>  Welding shop scheduling problem</kwd>
                                            </kwd-group>
                            
                                                <kwd-group xml:lang="tr">
                                                    <kwd>Arena simülasyonu</kwd>
                                                    <kwd>  Genetik algoritma</kwd>
                                                    <kwd>  Emekyoğun proje tipi üretim</kwd>
                                                    <kwd>  Tamamlanma zamanı minimizasyonu</kwd>
                                                    <kwd>  Değişken iş istasyonu kısıtı</kwd>
                                                    <kwd>  Kaynak atölyesi çizelgeleme problemi</kwd>
                                            </kwd-group>
                                                                                                                                        </article-meta>
    </front>
    <back>
                            <ref-list>
                                    <ref id="ref1">
                        <label>1</label>
                        <mixed-citation publication-type="journal">[1] Jia Z, Lu X, Yang J, Jia D. “Research on job-shop scheduling problem based on genetic algorithm”. International Journal of Production Research, 49(12), 3585-3604, 2011.</mixed-citation>
                    </ref>
                                    <ref id="ref2">
                        <label>2</label>
                        <mixed-citation publication-type="journal">[2] Azadeh A, Negahban A, Moghaddam M. “A hybrid computer simulation-artificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems”. International Journal of Production Research, 50(2), 551-566, 2012.</mixed-citation>
                    </ref>
                                    <ref id="ref3">
                        <label>3</label>
                        <mixed-citation publication-type="journal">[3] Huang XW, Zhao XY, Ma XL. “An improved genetic algorithm for job-shop scheduling problem with process sequence flexibility”. International Journal of Simulation Modelling, 13(4), 510-522, 2014.</mixed-citation>
                    </ref>
                                    <ref id="ref4">
                        <label>4</label>
                        <mixed-citation publication-type="journal">[4] Aydemir E, Koruca HI. “A new production scheduling module using priority-rule based genetic algorithm”. International Journal of Simulation Modelling, 14(3), 450-462, 2015.</mixed-citation>
                    </ref>
                                    <ref id="ref5">
                        <label>5</label>
                        <mixed-citation publication-type="journal">[5] Ba L, Li Y, Yang MS, Gao XQ, Liu Y. “Modelling and simulation of a multi-resource flexible job-shop scheduling”. International Journal of Simulation Modelling, 15(1), 157-169, 2016.</mixed-citation>
                    </ref>
                                    <ref id="ref6">
                        <label>6</label>
                        <mixed-citation publication-type="journal">[6] Deng Q, Gong G, Gong X, Zhang L, Liu W, Ren Q. “A bee evolutionary guiding nondominated sorting genetic algorithm II for multiobjective flexible job-shop scheduling”. Computational Intelligence and Neuroscience, 2017. https://doi.org/10.1155/2017/5232518.</mixed-citation>
                    </ref>
                                    <ref id="ref7">
                        <label>7</label>
                        <mixed-citation publication-type="journal">[7] Ocaktan MAB, Kucukkoc I, Karaoglan AD, Cicibas A, Buyukozkan K. “Scheduling Customized Orders: A Case Study at BEST Transformers Company”. 6th International Conference on Mechanics and Industrial Engineering (ICMIE’17), Rome, Italy, 8-10 June, 2017.</mixed-citation>
                    </ref>
                                    <ref id="ref8">
                        <label>8</label>
                        <mixed-citation publication-type="journal">[8] Zhang W, Wen JB, Zhu YC, Hu Y. “Multi-objective scheduling simulation of flexible job-shop based on multipopulation genetic algorithm”. International Journal of Simulation Modelling, 16(2), 313-321, 2017.</mixed-citation>
                    </ref>
                                    <ref id="ref9">
                        <label>9</label>
                        <mixed-citation publication-type="journal">[9] Hu HX, Lei WX, Gao X, Zhang Y. “job-shop scheduling problem based on improved cuckoo search algorithm”. International Journal of Simulation Modelling, 17(2), 337-346, 2018.</mixed-citation>
                    </ref>
                                    <ref id="ref10">
                        <label>10</label>
                        <mixed-citation publication-type="journal">[10] Jiang T, Zhang C, Zhu H, Deng G. “Energy-efficient scheduling for a job shop using grey wolf optimization algorithm with double-searching mode”. Mathematical Problems in Engineering, 2018. https://doi.org/10.1155/2018/8574892</mixed-citation>
                    </ref>
                                    <ref id="ref11">
                        <label>11</label>
                        <mixed-citation publication-type="journal">[11] Jiang T, Zhang C, Zhu H, Gu J, Deng G. “Energy-efficient scheduling for a job shop using an improved whale optimization algorithm”. Mathematics, 2018. https://doi.org/10.3390/math6110220</mixed-citation>
                    </ref>
                                    <ref id="ref12">
                        <label>12</label>
                        <mixed-citation publication-type="journal">[12] Seng DW, Li JW, Fang XJ, Zhang XF, Chen J. “low-carbon flexible job-shop scheduling based on improved nondominated sorting genetic algorithm-II”. International Journal of Simulation Modelling, 17(4), 712-723, 2018.</mixed-citation>
                    </ref>
                                    <ref id="ref13">
                        <label>13</label>
                        <mixed-citation publication-type="journal">[13] Karaoglan AD, Ocaktan MAB, Ocaktan DG, Oral A, Kundakci SS, Tuncer C. “Scheduling customized orders by considering the ergonomic constraints: a case study at Yemtar company”. 7th International Conference on Mechanics and Industrial Engineering (ICMIE’18), Madrid, Spain, 16-18 August, 2018.</mixed-citation>
                    </ref>
                                    <ref id="ref14">
                        <label>14</label>
                        <mixed-citation publication-type="journal">[14] Zhang HP, Ye JH, Yang XP, Muruve NW, Wang JT. ”Modified binary particle swarm optimization algorithm in lotsplitting scheduling involving multiple techniques”. International Journal of Simulation Modelling, 17(3), 534-542, 2018.</mixed-citation>
                    </ref>
                                    <ref id="ref15">
                        <label>15</label>
                        <mixed-citation publication-type="journal">[15] Zhong Q, Yang H, Tang T. “Optimization algorithm simulation for dual-resource constrained job-shop scheduling”. International Journal of Simulation Modelling, 17(1), 147-158, 2018.</mixed-citation>
                    </ref>
                                    <ref id="ref16">
                        <label>16</label>
                        <mixed-citation publication-type="journal">[16] Sel C, Hamzadayi A. &quot;A simulated annealing approach based simulation-optimisation to the dynamic job-shop scheduling problem”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 24(4), 665-674, 2018.</mixed-citation>
                    </ref>
                                    <ref id="ref17">
                        <label>17</label>
                        <mixed-citation publication-type="journal">[17] Fu HC, Liu P. “A multi-objective optimization model based on non-dominated sorting genetic algorithm”. International Journal of Simulation Modelling, 18(3), 510-520, 2019.</mixed-citation>
                    </ref>
                                    <ref id="ref18">
                        <label>18</label>
                        <mixed-citation publication-type="journal">[18] Tang H, Chen R, Li Y, Peng Z, Guo S, Du Y. “Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop”. Applied Soft Computing, 78, 176-194, 2019.</mixed-citation>
                    </ref>
                                    <ref id="ref19">
                        <label>19</label>
                        <mixed-citation publication-type="journal">[19] Liao J, Lin C. “Optimization and simulation of job-shop supply chain scheduling in manufacturing enterprises based on particle swarm optimization”. International Journal of Simulation Modelling, 18(1), 187-196, 2019.</mixed-citation>
                    </ref>
                                    <ref id="ref20">
                        <label>20</label>
                        <mixed-citation publication-type="journal">[20] Wang Y, Yang O, Wang SN. “A solution to single-machine inverse job-shop scheduling problem”. International Journal of Simulation Modelling, 18(2), 335-343, 2019.</mixed-citation>
                    </ref>
                                    <ref id="ref21">
                        <label>21</label>
                        <mixed-citation publication-type="journal">[21] Zhang Z, Guan ZL, Zhang J, Xie X. “A novel job-shop scheduling strategy based on particle swarm optimization and neural network”. International Journal of Simulation modeling, 18(4), 699-707, 2019.</mixed-citation>
                    </ref>
                                    <ref id="ref22">
                        <label>22</label>
                        <mixed-citation publication-type="journal">[22] Zhu J, Shao ZH, Chen C. “An improved whale optimization algorithm for job-shop scheduling based on quantum computing”. International Journal of Simulation Modelling, 18(3), 521-530, 2019.</mixed-citation>
                    </ref>
                                    <ref id="ref23">
                        <label>23</label>
                        <mixed-citation publication-type="journal">[23] Karaoglan AD, Cetin E. Industrial Engineering in the Big Data Era. Editors: Calisir F, Cevikcan E, Akdag HC. Part I: Industrial Engineering, Artificial Bee Colony Algorithm for Labor Intensive Project Type Job Shop Scheduling Problem: A Case Study, 79-88, Cham, Switzerland, Springer, 2019.</mixed-citation>
                    </ref>
                                    <ref id="ref24">
                        <label>24</label>
                        <mixed-citation publication-type="journal">[24] Shi DL, Zhang BB, Li Y. “A multi-objective flexible job-shop scheduling model based on fuzzy theory and immune genetic algorithm”. International Journal of Simulation Modelling, 19(1), 123-133, 2020.</mixed-citation>
                    </ref>
                                    <ref id="ref25">
                        <label>25</label>
                        <mixed-citation publication-type="journal">[25] Gu J, Jiang T, Zhu H, Zhang C. “Low-carbon job shop scheduling problem with discrete genetic-grey wolf optimization algorithm”. Journal of Advanced Manufacturing Systems, 19(1), 1-14, 2020.</mixed-citation>
                    </ref>
                                    <ref id="ref26">
                        <label>26</label>
                        <mixed-citation publication-type="journal">[26] Vital-Soto A, Azab A, Mohammed FB. “Mathematical modeling and a hybridized bacterial foraging optimization algorithm for the flexible job-shop scheduling problem with sequencing flexibility”. Journal of Manufacturing Systems, 54, 74-93, 2020.</mixed-citation>
                    </ref>
                                    <ref id="ref27">
                        <label>27</label>
                        <mixed-citation publication-type="journal">[27] Rao Y, Meng R, Zha J, Xu X. “Bi-objective mathematical model and improved algorithm for optimisation of welding shop scheduling problem”. International Journal of Production Research, 58(9), 2767-2783, 2020.</mixed-citation>
                    </ref>
                                    <ref id="ref28">
                        <label>28</label>
                        <mixed-citation publication-type="journal">[28] Holland JH. Adaptation in Natural and Artificial Systems, Cambridge, Massachusetts, USA, MIT Press, 1975.</mixed-citation>
                    </ref>
                                    <ref id="ref29">
                        <label>29</label>
                        <mixed-citation publication-type="journal">[29] Goldberg DE. Genetic Algorithms in Search, Optimization and Machine Learning, Boston, MA, USA, Addison-Wesley Longman Publishing, 1989.</mixed-citation>
                    </ref>
                                    <ref id="ref30">
                        <label>30</label>
                        <mixed-citation publication-type="journal">[30] Lim C, Sim E, “Production planning in manufacturing/remanufacturing environment using genetic algorithm”. Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation (GECCO’05), Washington, USA, 25-29 June, 2005.</mixed-citation>
                    </ref>
                                    <ref id="ref31">
                        <label>31</label>
                        <mixed-citation publication-type="journal">[31] Wall M. A Genetic Algorithm for Resource-Constrained Scheduling. PhD Thesis, Massachusetts Institute of Technology, Cambridge-Massachusetts, USA, 1996.</mixed-citation>
                    </ref>
                                    <ref id="ref32">
                        <label>32</label>
                        <mixed-citation publication-type="journal">[32] Kachitvichyanukul V. &quot;Comparison of three evolutionary algorithms: GA, PSO, and DE&quot;. Industrial Engineering &amp; Management Systems, 11(3), 215-223, 2012.</mixed-citation>
                    </ref>
                                    <ref id="ref33">
                        <label>33</label>
                        <mixed-citation publication-type="journal">[33] Rosetti MD. Simulation Modeling and Arena. 2nd ed. New Jersey, Hoboken, USA, John Wiley &amp; Sons Inc, 2010.</mixed-citation>
                    </ref>
                            </ref-list>
                    </back>
    </article>
