<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.4 20241031//EN"
        "https://jats.nlm.nih.gov/publishing/1.4/JATS-journalpublishing1-4.dtd">
<article  article-type="research-article"        dtd-version="1.4">
            <front>

                <journal-meta>
                                                                <journal-id>gummfd</journal-id>
            <journal-title-group>
                                                                                    <journal-title>Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi</journal-title>
            </journal-title-group>
                            <issn pub-type="ppub">1300-1884</issn>
                                        <issn pub-type="epub">1304-4915</issn>
                                                                                            <publisher>
                    <publisher-name>Gazi Üniversitesi</publisher-name>
                </publisher>
                    </journal-meta>
                <article-meta>
                                        <article-id pub-id-type="doi">10.17341/gazimmfd.1355533</article-id>
                                                                <article-categories>
                                            <subj-group  xml:lang="en">
                                                            <subject>Satisfiability and Optimisation</subject>
                                                            <subject>Industrial Engineering</subject>
                                                    </subj-group>
                                            <subj-group  xml:lang="tr">
                                                            <subject>Memnuniyet ve Optimizasyon</subject>
                                                            <subject>Endüstri Mühendisliği</subject>
                                                    </subj-group>
                                    </article-categories>
                                                                                                                                                        <title-group>
                                                                                                                        <trans-title-group xml:lang="en">
                                    <trans-title>Genetic algorithm based on weighted goal programming for doctor rostering problem</trans-title>
                                </trans-title-group>
                                                                                                                                                                                                <article-title>Doktor nöbet cetveli çizelgeleme problemi için ağırlıklı hedef programlama tabanlı genetik algoritma</article-title>
                                                                                                    </title-group>
            
                                                    <contrib-group content-type="authors">
                                                                        <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0000-0003-3719-7575</contrib-id>
                                                                <name>
                                    <surname>Yalçın</surname>
                                    <given-names>Anıl</given-names>
                                </name>
                                                                    <aff>KÜTAHYA DUMLUPINAR ÜNİVERSİTESİ</aff>
                                                            </contrib>
                                                    <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0000-0003-2676-1628</contrib-id>
                                                                <name>
                                    <surname>Deliktaş</surname>
                                    <given-names>Derya</given-names>
                                </name>
                                                                    <aff>KÜTAHYA DUMLUPINAR ÜNİVERSİTESİ</aff>
                                                            </contrib>
                                                                                </contrib-group>
                        
                                        <pub-date pub-type="pub" iso-8601-date="20240520">
                    <day>05</day>
                    <month>20</month>
                    <year>2024</year>
                </pub-date>
                                        <volume>39</volume>
                                        <issue>4</issue>
                                        <fpage>2567</fpage>
                                        <lpage>2586</lpage>
                        
                        <history>
                                    <date date-type="received" iso-8601-date="20230908">
                        <day>09</day>
                        <month>08</month>
                        <year>2023</year>
                    </date>
                                                    <date date-type="accepted" iso-8601-date="20240203">
                        <day>02</day>
                        <month>03</month>
                        <year>2024</year>
                    </date>
                            </history>
                                        <permissions>
                    <copyright-statement>Copyright © 1986, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi</copyright-statement>
                    <copyright-year>1986</copyright-year>
                    <copyright-holder>Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi</copyright-holder>
                </permissions>
            
                                                                                                <trans-abstract xml:lang="en">
                            <p>In the healthcare sector, undisrupted service is essential for hospitals. Therefore, shift work plays a vital role in satisfying constraints such as coverage requirements and government regulations. The doctor rostering problem is classified as an NP-hard problem due to its complexity and scale. In addition to the fairness of assignments, including hospital management policies, and government regulations, many related factors must be taken into account during the scheduling process in this scheduling problem.This study aims to generate a rostering system that can satisfy the requirements of the hospital, ensure fairness amongst the doctors, and take preferences into account. A genetic algorithm based on a weighted goal programming model was proposed to solve the doctor rostering problem. The proposed model was applied to the Internal Diseases Department and the Lateral Branches Department of Kütahya Evliya Çelebi Education and Research Hospital. 15 different scenarios were constructed, considering different problem scales and different preference patterns of the doctors that may occur in the the future. It is approved that the proposed algorithm can be applied to different problem scales and conditions. The parameters of the proposed algorithm were calibrated with an experimental design method. In this study, two main contributions were presented. A model with new constraints was introduced for researchers. In addition, a genetic algorithm based on weighted goal programming was proposed to solve the problem and applied to a real-world case study.</p></trans-abstract>
                                                                                                                                    <abstract><p>Sağlık hizmeti alanında, hastaneler için kesintisiz hizmet esastır. Bu nedenle, vardiyalı çalışma, talep kısıtları ve devlet düzenlemeleri gibi kısıtların karşılanabilmesi açısından oldukça önemli bir rol oynamaktadır. Doktor nöbet cetveli çizelgeleme problemi, problemin karmaşıklığı ve büyüklüğü sebebiyle NP-zor problem grubu içerisinde tanımlanmaktadır. Bu çizelgeleme probleminde, atamaların adilliğine ek olarak, hastane yönetim politikaları ve hükümet düzenlemeleri dâhil olmak üzere ilgili pek çok faktör hesaba katılmalıdır.Bu çalışma, hastane gereksinimlerini, doktorlar arasındaki adilliği karşılayabilen ve doktor tercihlerini göz önünde bulundurabilen bir nöbet cetveli çizelgeleme sistemi oluşturmayı amaçlamıştır. Ele alınan nöbet cetveli çizelgeleme probleminin çözümü için bir ağırlıklı hedef programlama-tabanlı genetik algoritma önerilmiştir. Önerilen model Kütahya Evliya Çelebi Eğitim ve Araştırma Hastanesi Dahiliye Departmanı ve İç Hastalıkları Departmanı’na uygulanmıştır. Gelecekte, oluşabilecek problem boyutları, şartları ve farklı tercih modelleri düşünülerek 15 farklı senaryo oluşturulmuştur. Bu senaryolarla önerilen algoritmanın farklı durumlarda da uygulanabilir olduğu gösterilmiştir. Önerilen algoritmanın parametreleri, bir deneysel tasarım yöntemiyle kalibre edilmiştir. Bu çalışma ile iki ana katkıda bulunulmuştur. Araştırmacılar için yeni kısıtlara sahip bir model önerilmiştir. Ek olarak, problemin çözümü için bir ağırlıklı hedef programlama-tabanlı genetik algoritma önerilerek gerçek-hayat problemine uygulanmıştır.</p></abstract>
                                                            
            
                                                                                        <kwd-group>
                                                    <kwd>Ağırlıklı hedef programlama</kwd>
                                                    <kwd>  deney tasarımı</kwd>
                                                    <kwd>  doktor nöbet cetveli çizelgeleme</kwd>
                                                    <kwd>  genetik algoritma</kwd>
                                                    <kwd>  duyarlılık analizi</kwd>
                                            </kwd-group>
                            
                                                <kwd-group xml:lang="en">
                                                    <kwd>Doctor rostering</kwd>
                                                    <kwd>  experimental design</kwd>
                                                    <kwd>  genetic algorithm</kwd>
                                                    <kwd>  weighted goal programming</kwd>
                                                    <kwd>  sensitivity analysis</kwd>
                                            </kwd-group>
                                                                                                                                        </article-meta>
    </front>
    <back>
                            <ref-list>
                                    <ref id="ref1">
                        <label>1</label>
                        <mixed-citation publication-type="journal">1.	Ernst, A. T., Jiang, H., Krishnamoorthy, M., Owens, B., &amp; Sier, D., An annotated bibliography of personnel scheduling and rostering, Ann. Oper. Res., 127 (1–4), 21–144, 2004.</mixed-citation>
                    </ref>
                                    <ref id="ref2">
                        <label>2</label>
                        <mixed-citation publication-type="journal">2.	Ernst, A. T., Jiang, H., Krishnamoorthy, M., &amp; Sier, D., Staff scheduling and rostering: A review of applications, methods and models, Eur. J. Oper. Res., 153 (1), 3–27, 2004.</mixed-citation>
                    </ref>
                                    <ref id="ref3">
                        <label>3</label>
                        <mixed-citation publication-type="journal">3.	Glover, F., &amp; McMillan, C., The general employee scheduling problem: An integration of MS and AI, Computers and Operations Research, 13 (5), 563–773, 1986.</mixed-citation>
                    </ref>
                                    <ref id="ref4">
                        <label>4</label>
                        <mixed-citation publication-type="journal">4.	Puente, J., Gomez, A., Fernandez, I., &amp; Priore P., Medical doctor rostering problem in a hospital emergency department by means of genetic algorithms, Comput. Ind. Eng., 56, 1232-1242, 2009.</mixed-citation>
                    </ref>
                                    <ref id="ref5">
                        <label>5</label>
                        <mixed-citation publication-type="journal">5.	OECD, Health at a Glance 2017: OECD Indicators, Ed: Marlène Mohier, Kate Lancaster and Andrew Esson, OECD Publishing, Paris, 2017.</mixed-citation>
                    </ref>
                                    <ref id="ref6">
                        <label>6</label>
                        <mixed-citation publication-type="journal">6.	Chen, Z., De Causmaecker, P., &amp; Dou, Y., A combined mixed integer programming and deep neural network–assisted heuristics algorithm for the nurse rostering problem, Appl. Soft Comput., 919-957, 2023.</mixed-citation>
                    </ref>
                                    <ref id="ref7">
                        <label>7</label>
                        <mixed-citation publication-type="journal">7.	Chawasemerwa, T., Taifa, I. W., &amp; Hartmann, D., Development of a doctor scheduling system: a constraint satisfaction and penalty minimisation scheduling model, International Journal of Research in Industrial Engineering, 7, 396-422, 2018.</mixed-citation>
                    </ref>
                                    <ref id="ref8">
                        <label>8</label>
                        <mixed-citation publication-type="journal">8.	M’Hallah, R., &amp; Alkhabbaz, A., Scheduling of nurses: A case study of a Kuwaiti health care unit, Oper. Res. Health Care, 2, 1-19, 2013.</mixed-citation>
                    </ref>
                                    <ref id="ref9">
                        <label>9</label>
                        <mixed-citation publication-type="journal">9.	Wirnitzer, J., Heckmann, I., Meyer, A., &amp; Nickel, S., Patient-based nurse rostering in home care, Oper. Res. Health Care, 8, 91-102, 2016.</mixed-citation>
                    </ref>
                                    <ref id="ref10">
                        <label>10</label>
                        <mixed-citation publication-type="journal">10.	Wright, P. D., &amp; Mahar, S., Centralized nurse scheduling to simultaneously improve schedule cost and nurse satisfaction, Omega, 41, 1042-1052, 2013.</mixed-citation>
                    </ref>
                                    <ref id="ref11">
                        <label>11</label>
                        <mixed-citation publication-type="journal">11.	Böðvarsdottir, E. B., Smet, P., &amp; Berghe, G. V., Behind-the-scenes weight tuning for applied nurse rostering, Oper. Res. Health Care, 26, 265-278, 2020.</mixed-citation>
                    </ref>
                                    <ref id="ref12">
                        <label>12</label>
                        <mixed-citation publication-type="journal">12.	Samah, A. A., Yusoff, S. N. M., Zainudin, Z., &amp; Abd Majid, H., A study on rostering on-call doctor using genetic algorithm with enhanced genetic operator, 2012 Third International Conference on Intelligent Systems Modelling and Simulation, Kota Kinabalu-Sabah Malaysia, 126-130, 8-10 February, 2012.</mixed-citation>
                    </ref>
                                    <ref id="ref13">
                        <label>13</label>
                        <mixed-citation publication-type="journal">13.	Majid, H. A., Yusuf, L. M., Samah, A. A., Othman, M. S., &amp; Ren, A. N. W. Application of genetic algorithm for doctor rostering at primary care clinics in Malaysia, 2017 6th ICT International Student Project Conference, Johor-Malaysia, 1-4, 2017, 23-24 May.</mixed-citation>
                    </ref>
                                    <ref id="ref14">
                        <label>14</label>
                        <mixed-citation publication-type="journal">14.	Alharbi, A., &amp; AlQahtani, K., An evolutionary ıntelligent algorithm approach for the doctor scheduling problem, International Journal on Advances in Software, 10, 180-190, 2017.</mixed-citation>
                    </ref>
                                    <ref id="ref15">
                        <label>15</label>
                        <mixed-citation publication-type="journal">15.	Samah, A. A., Zainudin, Z., Majid, H. A., &amp; Yusoff, S. N. M., A framework using an evolutionary algorithm for on-call doctor scheduling, Journal of Computer Science &amp; Computational Mathematics, 2 (3), 9-16, 2012.</mixed-citation>
                    </ref>
                                    <ref id="ref16">
                        <label>16</label>
                        <mixed-citation publication-type="journal">16.	Wu, T. H., Yeh, J. Y., &amp; Lee, Y. M., A particle swarm optimization approach with refinement procedure for nurse rostering problem, Comput. Oper. Res., 54, 52-63, 2015.</mixed-citation>
                    </ref>
                                    <ref id="ref17">
                        <label>17</label>
                        <mixed-citation publication-type="journal">17.	Hadwan, M., Ayob, M., Sabar, N. R., &amp; Qu, R., A harmony search algorithm for nurse rostering problems, Inf. Sci., 233, 126-140, 2013.</mixed-citation>
                    </ref>
                                    <ref id="ref18">
                        <label>18</label>
                        <mixed-citation publication-type="journal">18.	Awadallah, M. A., Khader, A. T., Al-Betar, M. A., &amp; Bolaji, A. L., Global best harmony search with a new pitch adjustment designed for nurse rostering, Computer and Information Sciences, 25, 142-162, 2013.</mixed-citation>
                    </ref>
                                    <ref id="ref19">
                        <label>19</label>
                        <mixed-citation publication-type="journal">19.	Tassopoulos, I. X., Solos, I. P., &amp; Beligiannis, G. N., Α two-phase adaptive variable neighborhood approach for nurse rostering, Comput. Oper. Res., 60, 150-169, 2015.</mixed-citation>
                    </ref>
                                    <ref id="ref20">
                        <label>20</label>
                        <mixed-citation publication-type="journal">20.	Zheng, Z., Liu, X., &amp; Gong, X., A simple randomized variable neighbourhood search for nurse rostering, Comput. Ind. Eng., 110, 165-174, 2017.</mixed-citation>
                    </ref>
                                    <ref id="ref21">
                        <label>21</label>
                        <mixed-citation publication-type="journal">21.	Cürebal A., Eren T., Competency-based security personnel scheduling during the covid-19 pandemic, Journal of the Faculty of Engineering and Architecture of Gazi University, 36 (3), 1483-1498, 2021.</mixed-citation>
                    </ref>
                                    <ref id="ref22">
                        <label>22</label>
                        <mixed-citation publication-type="journal">22.	Akkuş İ., Yıldız E.A., Karaoğlan İ., Altıparmak, F., Mobile healthcare service planning in rural areas: A hybrid record to record travel algorithm, Journal of the Faculty of Engineering and Architecture of Gazi University, 39 (1), 593-606, 2024.</mixed-citation>
                    </ref>
                                    <ref id="ref23">
                        <label>23</label>
                        <mixed-citation publication-type="journal">23.	Dengiz A.Ö., Atalay K., Altıparmak F., A goal programming approach for multi objective, multi-trips and time window routing problem in home health care service, Journal of the Faculty of Engineering and Architecture of Gazi University, 36 (4), 2167-2182, 2021.</mixed-citation>
                    </ref>
                                    <ref id="ref24">
                        <label>24</label>
                        <mixed-citation publication-type="journal">24.	Otay İ., Intuitionistic fuzzy multi-expert &amp; multi-criteria decision making methodology: An application in healthcare industry, Journal of the Faculty of Engineering and Architecture of Gazi University, 37 (2), 1047-1062, 2022.</mixed-citation>
                    </ref>
                                    <ref id="ref25">
                        <label>25</label>
                        <mixed-citation publication-type="journal">25.	Saad, G., Harb, H., Abouaissa, A., Idoumghar, L., &amp; Charara, N., A sensing-based patient classification framework for efficient patient-nurse scheduling. Sustainable Comput. Inf. Syst., 38, 100855, 2023.</mixed-citation>
                    </ref>
                                    <ref id="ref26">
                        <label>26</label>
                        <mixed-citation publication-type="journal">26.	Yin, P. Y., Chao, C. C., &amp; Chiang, Y. T., Multiobjective optimization for nurse scheduling, Advances in Swarm Intelligence: Second International Conference, International Conference on Swarm Intelligence, Chongqing-China, 66-73, 12-15 June, 2011.</mixed-citation>
                    </ref>
                                    <ref id="ref27">
                        <label>27</label>
                        <mixed-citation publication-type="journal">27.	Maenhout, B., &amp; Vanhoucke, M., An evolutionary approach for the nurse rerostering problem, Comput. Oper. Res., 38, 1400-1411, 2011.</mixed-citation>
                    </ref>
                                    <ref id="ref28">
                        <label>28</label>
                        <mixed-citation publication-type="journal">28.	He, F., &amp; Qu, R., A constraint programming based column generation approach to nurse rostering problems, Comput. Oper. Res., 39, 3331-3343, 2012.</mixed-citation>
                    </ref>
                                    <ref id="ref29">
                        <label>29</label>
                        <mixed-citation publication-type="journal">29.	Lü, Z., &amp; Hao, J. K., Adaptive neighborhood search for nurse rostering, Cent. Eur. Oper. Res. Central, 218, 865-876, 2012.</mixed-citation>
                    </ref>
                                    <ref id="ref30">
                        <label>30</label>
                        <mixed-citation publication-type="journal">30.	Valouxis, C., Gogos, C., Goulas, G., Alefragis, P., &amp; Housos, E., A systematic two phase approach for the nurse rostering problem, Eur. J. Oper. Res., 219, 425-433, 2012.</mixed-citation>
                    </ref>
                                    <ref id="ref31">
                        <label>31</label>
                        <mixed-citation publication-type="journal">31.	Martin, S., Quelhadj, D., Smet, P., Berghe, G. V., &amp; Özcan, E., Cooperative search for fair nurse rosters. Expert Syst. Appl., 40, 6674-6683, 2013.</mixed-citation>
                    </ref>
                                    <ref id="ref32">
                        <label>32</label>
                        <mixed-citation publication-type="journal">32.	Maenhout, B., &amp; Vanhoucke, M., An integrated nurse staffing and scheduling analysis for longer-term nursing staff allocation problems, Omega, 41, 485-499, 2013.</mixed-citation>
                    </ref>
                                    <ref id="ref33">
                        <label>33</label>
                        <mixed-citation publication-type="journal">33.	Maenhout, B., &amp; Vanhoucke, M., Reconstructing nurse schedules: Computational insights in the problem size parameters, Omega, 41, 903-918, 2013.</mixed-citation>
                    </ref>
                                    <ref id="ref34">
                        <label>34</label>
                        <mixed-citation publication-type="journal">34.	Burke, E. K., &amp; Curtois, T., New approaches to nurse rostering benchmark instances, Eur. J. Oper. Res., 237, 71-81, 2014.</mixed-citation>
                    </ref>
                                    <ref id="ref35">
                        <label>35</label>
                        <mixed-citation publication-type="journal">35.	Wong, T. C., Xu, M., &amp; Chin, K. S., A two-stage heuristic approach for nurse scheduling problem: A case study in an emergency department, Comput. Oper. Res., 51, 99-110, 2014.</mixed-citation>
                    </ref>
                                    <ref id="ref36">
                        <label>36</label>
                        <mixed-citation publication-type="journal">36.	Baeklund, J., Nurse rostering at a Danish ward, Ann Oper. Res., 222, 107-123, 2014.</mixed-citation>
                    </ref>
                                    <ref id="ref37">
                        <label>37</label>
                        <mixed-citation publication-type="journal">37.	Awadallah, M. A., Bolaji, A. L., &amp; Al-Betar, M. A., A hybrid artificial bee colony for a nurse rostering problem, Appl. Soft Comput., 35, 726-739, 2015.</mixed-citation>
                    </ref>
                                    <ref id="ref38">
                        <label>38</label>
                        <mixed-citation publication-type="journal">38.	Rahimian, E., Akartunalı, K., &amp; Levine, J., A hybrid integer programming and variable neighbourhood search algorithm to solve nurse rostering problems, Eur. J. Oper. Res., 258, 411-423, 2016.</mixed-citation>
                    </ref>
                                    <ref id="ref39">
                        <label>39</label>
                        <mixed-citation publication-type="journal">39.	Asta, S., Özcan, E., &amp; Curtois, T., A tensor based hyper-heuristic for nurse rostering, Knowledge-Based Syst., 98, 185-199, 2016.</mixed-citation>
                    </ref>
                                    <ref id="ref40">
                        <label>40</label>
                        <mixed-citation publication-type="journal">40.	Lin, W. D., &amp; Chia, L., Combined forecasting of patient arrivals and doctor rostering simulation modelling for hospital emergency department, 2017 IEEE International conference on industrial engineering and engineering management, Singapore, 2391-2395, December, 2017.</mixed-citation>
                    </ref>
                                    <ref id="ref41">
                        <label>41</label>
                        <mixed-citation publication-type="journal">41.	Lavygina, A., Welsh, K., &amp; Crispin, A., Doctor rostering in compliance with the new UK junior doctor contract, The 11th Annual International Conference on Combinatorial Optimization and Applications, Shanghai-China, 394-408, 16-18 December, 2017.</mixed-citation>
                    </ref>
                                    <ref id="ref42">
                        <label>42</label>
                        <mixed-citation publication-type="journal">42.	Rahimian, E., Akartunalı, K., &amp; Levine, J., A hybrid integer and constraint programming approach to solve nurse rostering problems, Computers and Operations Research, 82, 83-94, 2017.</mixed-citation>
                    </ref>
                                    <ref id="ref43">
                        <label>43</label>
                        <mixed-citation publication-type="journal">43.	Liu, Z., Liu, Z., Zhu, Z., Shen, Y., &amp; Dong, J., Simulated annealing for a multi-level nurse rostering problem in hemodialysis service, Appl. Soft Comput., 64, 148-160, 2017.</mixed-citation>
                    </ref>
                                    <ref id="ref44">
                        <label>44</label>
                        <mixed-citation publication-type="journal">44.	Gomes, R. A. M., Toffoloa, T. A. M., &amp; Santos, H. G., Variable neighborhood search accelerated column generation for the nurse rostering problem, Electron. Notes Discrete Math., 58, 31-38, 2017.</mixed-citation>
                    </ref>
                                    <ref id="ref45">
                        <label>45</label>
                        <mixed-citation publication-type="journal">45.	Landtsheer, R. D., Delannay, G., &amp; Ponsard, C., Dealing with perceived fairness when planning doctor shifts in hospitals, Proceedings of the 7th International Conference on Operations Research and Enterprise Systems, Madeira-Portugal, 320-326, 24-26 January, 2018.</mixed-citation>
                    </ref>
                                    <ref id="ref46">
                        <label>46</label>
                        <mixed-citation publication-type="journal">46.	Fügener, A., Pahr, A., &amp; Brunner, J. O., Mid-term nurse rostering considering cross-training effects, Int. J. Prod. Econ., 196, 176-187, 2018.</mixed-citation>
                    </ref>
                                    <ref id="ref47">
                        <label>47</label>
                        <mixed-citation publication-type="journal">47.	Aktunc, E. A., &amp; Tekin, E., Nurse scheduling with shift preferences in a surgical suite using goal programming, Global Joint Conference on Industrial Engineering and Its Application (GJCIE 2018) Areas, Nevsehir-Turkey, 23-36, 21-22 July, 2018.</mixed-citation>
                    </ref>
                                    <ref id="ref48">
                        <label>48</label>
                        <mixed-citation publication-type="journal">48.	Jaradat, G. M., Al-Badareen, A., Ayob, M., Al-Smadi, M., Al-Marashdeh, I., Ash-Shuqran, M., &amp; Al-Odat, E., Hybrid elitist-ant system for nurse-rostering problem, J. King Saud Univ. Comput. Inf. Sci., 31, 378-384, 2019.</mixed-citation>
                    </ref>
                                    <ref id="ref49">
                        <label>49</label>
                        <mixed-citation publication-type="journal">49.	Wickert, T. I., Smet, P., &amp; Berghe, G. V., The nurse rerostering problem: Strategies for reconstructing disrupted schedules, Computers and Operations Research, 104, 319-337, 2019.</mixed-citation>
                    </ref>
                                    <ref id="ref50">
                        <label>50</label>
                        <mixed-citation publication-type="journal">50.	Hadwan, M., Ayob, M., Al-Hagery, M., &amp; Al-Tamimi, B. N., Climbing harmony search algorithm for nurse rostering problems, Recent Trends in Data Science and Soft Computing: 3rd International Conference of Reliable Information and Communication Technology, Kuala Lumpur-Malaysia, 74-83, 23-24 July, 2019.</mixed-citation>
                    </ref>
                                    <ref id="ref51">
                        <label>51</label>
                        <mixed-citation publication-type="journal">51.	Turhan, A. M., &amp; Bilgen, B., A hybrid fix-and-optimize and simulated annealing approaches for nurse rostering problem, Comput. Ind. Eng., 145, 531-542, 2020.</mixed-citation>
                    </ref>
                                    <ref id="ref52">
                        <label>52</label>
                        <mixed-citation publication-type="journal">52.	Böðvarsdottir, E. B., Smet, P., Berghe, G. V., &amp; Stidsen, T. J. R., Achieving compromise solutions in nurse rostering by using automatically estimated acceptance thresholds, Eur. J. Oper. Res., 292, 980-995, 2020.</mixed-citation>
                    </ref>
                                    <ref id="ref53">
                        <label>53</label>
                        <mixed-citation publication-type="journal">53.	Chen, P. S., &amp; Zeng, Z. Y., Developing two heuristic algorithms with metaheuristic algorithms to improve solutions of optimization problems with soft and hard constraints: An application to nurse rostering problems, Appl. Soft Comput., 93, 336-358, 2020.</mixed-citation>
                    </ref>
                                    <ref id="ref54">
                        <label>54</label>
                        <mixed-citation publication-type="journal">54.	Strandmark, P., Qu, Y., &amp; Curtois, T., First-order linear programming in a column generation-based heuristic approach to the nurse rostering problem, Computers and Operations Research, 120, 945-959, 2020.</mixed-citation>
                    </ref>
                                    <ref id="ref55">
                        <label>55</label>
                        <mixed-citation publication-type="journal">55.	Kheiri, A., Gretsista, A., Keedwell, E., Lulli, G., Epitropakis, M. G., &amp; Burke, E. K., A hyper-heuristic approach based upon a hidden Markov model for the multi-stage nurse rostering problem, Computers and Operations Research, 130, 221-234, 2021.</mixed-citation>
                    </ref>
                                    <ref id="ref56">
                        <label>56</label>
                        <mixed-citation publication-type="journal">56.	Hassani, M. R., &amp; Behnamian, J., A scenario-based robust optimization with a pessimistic approach for nurse rostering problem, J. Comb. Optim., 41, 143-169, 2021.</mixed-citation>
                    </ref>
                                    <ref id="ref57">
                        <label>57</label>
                        <mixed-citation publication-type="journal">57.	Guo, J., &amp; Bard, J. F., A column generation-based algorithm for midterm nurse scheduling with specialized constraints, preference considerations, and overtime. Comput. Oper. Res., 138, 597-623, 2022.</mixed-citation>
                    </ref>
                                    <ref id="ref58">
                        <label>58</label>
                        <mixed-citation publication-type="journal">58.	Turhan, A. M., &amp; Bilgen, B., A mat-heuristic based solution approach for an extended nurse rostering problem with skills and units, Socio-Economic Planning Sciences, 82, 300-311, 2022.</mixed-citation>
                    </ref>
                                    <ref id="ref59">
                        <label>59</label>
                        <mixed-citation publication-type="journal">59.	Otero-Caicedo, R., Casas, C. E. M., Jaimes, C. B., Garzón, C. F. G., Vergel, E. A. Y., &amp; Valdés, J. C. Z. A preventive–reactive approach for nurse scheduling considering absenteeism and nurses’ preferences, Oper. Res. Health Care, 38, 100389, 2023.</mixed-citation>
                    </ref>
                                    <ref id="ref60">
                        <label>60</label>
                        <mixed-citation publication-type="journal">60.	Alharbi, A., &amp; AlQuahtani, K., A Genetic algorithm solution for the doctor scheduling problem, The Tenth International Conference on Advanced Engineering Computing and Applications in Sciences, Venice-Italy, 91-98, 9-13 October, 2016.</mixed-citation>
                    </ref>
                                    <ref id="ref61">
                        <label>61</label>
                        <mixed-citation publication-type="journal">61.	Zhang, Z., Hao, Z., &amp; Huang, H., Hybrid swarm-based optimization algorithm of ga &amp; vns for nurse scheduling problem, Information Computing and Applications: Second International Conference, Qinhuangdao-China, 375-382, 2011, 28-31 October.</mixed-citation>
                    </ref>
                                    <ref id="ref62">
                        <label>62</label>
                        <mixed-citation publication-type="journal">62.	Burke, E. K., Li, J., &amp; Qu, R., A Pareto-based search methodology for multi-objective nurse scheduling, Ann Oper. Res., 196, 91-109, 2012.</mixed-citation>
                    </ref>
                                    <ref id="ref63">
                        <label>63</label>
                        <mixed-citation publication-type="journal">63.	Fan, N., Mujahid, S., Zhang, J., Georgiev, P., Papajorgji, P., Steponavice, I., Neugard, B., &amp; Pardalos, P. M., Nurse scheduling problem: an integer programming model with a practical application, Systems Analysis Tools For Better Health Care Delivery, Pardalos, P., Georgiev, P., Papajorgji, P., Neugaard, B. (Eds), Springer. New York, NY, 74, 65-98, 2013.</mixed-citation>
                    </ref>
                                    <ref id="ref64">
                        <label>64</label>
                        <mixed-citation publication-type="journal">64.	Rasip, M. N., Basari, A. S. H., Ibrahim, N. K., &amp; Hussin, B., Enhancement of nurse scheduling steps using particle swarm optimization, Advanced Computer and Communication Engineering Technology: Proceedings of the 1st International Conference on Communication and Computer Engineering, Kanyakumari-India, 459-469, 2-3 November, 2015.</mixed-citation>
                    </ref>
                                    <ref id="ref65">
                        <label>65</label>
                        <mixed-citation publication-type="journal">65.	Legraina, A., Omer, J., &amp; Rosat, S., A rotation-based branch-and-price approach for the nurse scheduling problem, Math. Program. Comput., 12, 417-450, 2020.</mixed-citation>
                    </ref>
                                    <ref id="ref66">
                        <label>66</label>
                        <mixed-citation publication-type="journal">66.	Legraina, A., Omer, J., &amp; Rosat, S., An online stochastic algorithm for a dynamic nurse scheduling problem, Eur. J. Oper. Res., 285, 196-210, 2020.</mixed-citation>
                    </ref>
                                    <ref id="ref67">
                        <label>67</label>
                        <mixed-citation publication-type="journal">67.	Sarkar, P., Chaki, R., &amp; Cortesi, A., A patient-centric nurse scheduling algorithm. SN Comput. Sci., 3, 1-16, 2022.</mixed-citation>
                    </ref>
                                    <ref id="ref68">
                        <label>68</label>
                        <mixed-citation publication-type="journal">68.	Chen, Z., Dou, Y., &amp; De Causmaecker, P., Neural networked-assisted method for the nurse rostering problem, Comput. Ind. Eng., 171, 430-444, 2022.</mixed-citation>
                    </ref>
                                    <ref id="ref69">
                        <label>69</label>
                        <mixed-citation publication-type="journal">69.	Michael, C., Jeffery, C., &amp; David, C., Nurse preference rostering using agents and iterated local search, Annals of Operational Research, 226, 443-461, 2015.</mixed-citation>
                    </ref>
                                    <ref id="ref70">
                        <label>70</label>
                        <mixed-citation publication-type="journal">70.	Shukla, M., Li, X., &amp; Sun, Y., Time-interval based coverage constraint for nurse scheduling problems, 2015 Industrial and Systems Engineering Research Conference, Nashville-Tennessee, 1234-1242, 30 May – 2 June, 2015.</mixed-citation>
                    </ref>
                                    <ref id="ref71">
                        <label>71</label>
                        <mixed-citation publication-type="journal">71.	Kumar, M., Husian, M., Upreti, N., &amp; Gupta, D., Genetic algorithm: review and application, International Journal of Information Technology and Knowledge Management, 2, 451-454, 2010.</mixed-citation>
                    </ref>
                                    <ref id="ref72">
                        <label>72</label>
                        <mixed-citation publication-type="journal">72.	Min, L., &amp; Cheng, W., A genetic algorithm for minimizing the makespan in the case of scheduling identical paralel machines, Artificial Intelligence in Engineering, 13, 399-403, 1999.</mixed-citation>
                    </ref>
                                    <ref id="ref73">
                        <label>73</label>
                        <mixed-citation publication-type="journal">73.	Huanga, M., Ma, Y., Wan, J. &amp; Chen, X., A sensor-software based on a genetic algorithm-based neural fuzzy system for modeling and simulating a wastewater treatment process, Appl. Soft Comput., 27, 1-10, 2015.</mixed-citation>
                    </ref>
                                    <ref id="ref74">
                        <label>74</label>
                        <mixed-citation publication-type="journal">74.	Kechagias, J.D., Aslani, K. E., Fountas, N. A., Vaxevanidis, N. M., &amp; Manolakos, D. E., A comparative investigation of Taguchi and full factorial design for machinability prediction in turning of a titanium alloy, Measurement, 151, 1-11, 2020.</mixed-citation>
                    </ref>
                                    <ref id="ref75">
                        <label>75</label>
                        <mixed-citation publication-type="journal">75.	Basheer, P. A. M., Montgomery, F. R., &amp; Long, A. E., Factorial experimental design for concrete durability research, Proc. Inst. Civ. Eng. Struct. Build., 104, 449 – 462, 1994.</mixed-citation>
                    </ref>
                                    <ref id="ref76">
                        <label>76</label>
                        <mixed-citation publication-type="journal">76.	Antony, J., &quot;Some key things industrial engineers should know about experimental design&quot;, Logist. Inf. Manage., 11, 386 – 392, 1995.</mixed-citation>
                    </ref>
                                    <ref id="ref77">
                        <label>77</label>
                        <mixed-citation publication-type="journal">77.	Eşme, U., Application of Taguchi method for the optimization of resistance spot welding process, Arabian J. Sci. Eng., 34, 519-528, 2009.</mixed-citation>
                    </ref>
                                    <ref id="ref78">
                        <label>78</label>
                        <mixed-citation publication-type="journal">78.	Hosny, M., &amp; Al Turiki, N., A genetic-based nurse rostering tool: A Riyadh hospital case, International Conference on Genetic and Evolutionary Methods (GEM), Las Vegas-Nevada, 1-7, 22-25 July,2013.</mixed-citation>
                    </ref>
                                    <ref id="ref79">
                        <label>79</label>
                        <mixed-citation publication-type="journal">79.	Rae, C. S. W. E., A study of evolutionary perturbative hyper-heuristics for the nurse rostering problem, Doctoral Thesis, University of Kwazulu-Natal, Master of Science, Kwazulu-Natal, 2017.</mixed-citation>
                    </ref>
                                    <ref id="ref80">
                        <label>80</label>
                        <mixed-citation publication-type="journal">80.	Lin, C. C., Kang, J. R., Chiang, D. J., &amp; Chen, C. L., Nurse scheduling with joint normalized shift and day-off preference satisfaction using a genetic algorithm with immigrant scheme. Int. J. of Distrib. Sens. Netw., 11, 1-10, 2015.</mixed-citation>
                    </ref>
                                    <ref id="ref81">
                        <label>81</label>
                        <mixed-citation publication-type="journal">81.	Andriansyah, Alfadilla, N., Sentia, P. D., &amp; Asmadi, D., Optimization of nurse scheduling problem using genetic algorithm: a case study, IOP Conference Series: Materials Science and Engineering, 536, International Conference on Science and Innovated Engineering, Aceh-Indonesia, 131-137, 28 May – 2 June, 2019.</mixed-citation>
                    </ref>
                                    <ref id="ref82">
                        <label>82</label>
                        <mixed-citation publication-type="journal">82.	Abadi, M. Q. H., Rahmati, S., Sharifi, A., &amp; Ahmadi, M., HSSAGA: Designation and scheduling of nurses for taking care of COVID-19 patients using novel method of hybrid salp swarm algorithm and genetic algorithm, Appl, Soft Comput., 108, 449-459, 2021.</mixed-citation>
                    </ref>
                                    <ref id="ref83">
                        <label>83</label>
                        <mixed-citation publication-type="journal">83.	Rurifandho, A., Renaldi, F., &amp; Santikarama, I., Doctors dynamic scheduling for outpatient, inpatient, and surgery using genetic algorithm, International Conference on Science and Technology, Batam-Indonesia, 1-8, 3-4 February, 2022.</mixed-citation>
                    </ref>
                                    <ref id="ref84">
                        <label>84</label>
                        <mixed-citation publication-type="journal">84.	Kim, T. K., Understanding one-way ANOVA using conceptual figures, Korean Journal of Anesthesiology, 70 (1), 22-26, 2017.</mixed-citation>
                    </ref>
                                    <ref id="ref85">
                        <label>85</label>
                        <mixed-citation publication-type="journal">85.	Cramer, A. O. J., van Ravenzwaaij, D., Matzke, D., Steingroever, H., Wetzels, R., Grasman, R. P., Waldorp, L. J., &amp; Wagenmakers, E. J., Hidden multiplicity in exploratory multiway ANOVA: Prevalence and remedies, Psychonomic Bulletin &amp; Review, 23, 640-647, 2016.</mixed-citation>
                    </ref>
                                    <ref id="ref86">
                        <label>86</label>
                        <mixed-citation publication-type="journal">86.	Perazzi, A., Gomiero, C., Corain, L., Iacopetti, I., Grisan, E., Lombardo, M., Lombardo, G., Salvalaio, G., Contin, R., Patruno, M., Martinello, T., &amp; Peruffo, A., An assay system to evaluate riboflavin/UV-A corneal phototherapy efficacy in a porcine corneal organ culture model, Animals, 10 (4), 730-746, 2020.</mixed-citation>
                    </ref>
                                    <ref id="ref87">
                        <label>87</label>
                        <mixed-citation publication-type="journal">87.	Millman, J., &amp; Glass, J. V., Rules of thumb for writing the ANOVA table, Journal of Educational Measurement, 4 (2), 41-51, 1967.</mixed-citation>
                    </ref>
                                    <ref id="ref88">
                        <label>88</label>
                        <mixed-citation publication-type="journal">88.	Lee, J. Y., A genetic algorithm for a two-machine flowshop with a limited waiting time constraint and sequence-dependent setup times, Math. Probl. Eng., 2020, 1-13, 2020.</mixed-citation>
                    </ref>
                                    <ref id="ref89">
                        <label>89</label>
                        <mixed-citation publication-type="journal">89.	Gerostathopoulos, I., Prehofer, C., &amp; Bures, T., Adapting a system with noisy outputs with statistical guarantees, Proceedings of the 13th International Conference on Software Engineering for Adaptive and Self-Managing Systems, Gothenburg-Sweden, 58-68, 28-29 May, 2018.</mixed-citation>
                    </ref>
                                    <ref id="ref90">
                        <label>90</label>
                        <mixed-citation publication-type="journal">90.	Banerjee, S., Poria, S., Sutradhar, G., &amp; Sahoo, P., Wear performance of Mg-WC metal matrix nanocomposites using taguchi methodology, Mater. Today Proc., 19, 177-18, 2019.</mixed-citation>
                    </ref>
                                    <ref id="ref91">
                        <label>91</label>
                        <mixed-citation publication-type="journal">91.	Trucano, T. G., Swiler, L. P., Igusa, T., Oberkampf, W. L., &amp; Pilch, M., Calibration, validation, and sensitivity analysis: What&#039;s what, Reliab. Eng. Syst. Saf., 91, 1331-1357, 2006.</mixed-citation>
                    </ref>
                                    <ref id="ref92">
                        <label>92</label>
                        <mixed-citation publication-type="journal">92.	Chitnis, N., Hyman, J. M., &amp; Cushing, J. M., Determining important parameters in the spread of malaria through the sensitivity analysis of a mathematical model, Bull. Math. Biol., 70, 1272-1296, 2008.</mixed-citation>
                    </ref>
                                    <ref id="ref93">
                        <label>93</label>
                        <mixed-citation publication-type="journal">93.	Sutanto, E. M., Sampson, J. S., &amp; Mulyono, F., Organizational Justice work environment and motivation, International Journal of Business and Society, 19, 313-322, 2018.</mixed-citation>
                    </ref>
                                    <ref id="ref94">
                        <label>94</label>
                        <mixed-citation publication-type="journal">94.	Yalçın, A. Doktor nöbet çizelgeleme problemi için ağırlıklı hedef programlama tabanlı genetik algoritma, Yüksek Lisans Tezi, Kütahya Dumlupınar Üniversitesi, Fen Bilimleri Enstitüsü, Kütahya, 2023.</mixed-citation>
                    </ref>
                            </ref-list>
                    </back>
    </article>
