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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">moitvivt</journal-id>
      <journal-title-group>
        <journal-title xml:lang="ru">Моделирование, оптимизация и информационные технологии</journal-title>
        <trans-title-group xml:lang="en">
          <trans-title>Modeling, Optimization and Information Technology</trans-title>
        </trans-title-group>
      </journal-title-group>
      <issn pub-type="epub">2310-6018</issn>
      <publisher>
        <publisher-name>Издательство</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.26102/2310-6018/2026.59.8.001</article-id>
      <article-id pub-id-type="custom" custom-type="elpub">2423</article-id>
      <title-group>
        <article-title xml:lang="ru">Метод перехода к постоянной длине вектора параметров целевой функции в задачах оптимизации работы мостовых кранов на складе слябов</article-title>
        <trans-title-group xml:lang="en">
          <trans-title>A method for transitioning to a fixed-length parameter vector of the objective function in optimization problems of overhead crane operations in a slab yard</trans-title>
        </trans-title-group>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0008-2239-983X</contrib-id>
          <name-alternatives>
            <name name-style="eastern" xml:lang="ru">
              <surname>Щеголихин</surname>
              <given-names>Иван Сергеевич</given-names>
            </name>
            <name name-style="western" xml:lang="en">
              <surname>Shchegolikhin</surname>
              <given-names>Ivan Sergeevich</given-names>
            </name>
          </name-alternatives>
          <email>shchegolikhin.i@yandex.ru</email>
          <xref ref-type="aff">aff-1</xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0003-0735-6723</contrib-id>
          <name-alternatives>
            <name name-style="eastern" xml:lang="ru">
              <surname>Андреев</surname>
              <given-names>Сергей Михайлович</given-names>
            </name>
            <name name-style="western" xml:lang="en">
              <surname>Andreev</surname>
              <given-names>Sergey Mikhailovich</given-names>
            </name>
          </name-alternatives>
          <email>andreev.asc@gmail.com</email>
          <xref ref-type="aff">aff-2</xref>
        </contrib>
      </contrib-group>
      <aff-alternatives id="aff-1">
        <aff xml:lang="ru">Магнитогорский государственный технический университет им. Г.И. Носова</aff>
        <aff xml:lang="en">G.I. Nosov Magnitogorsk State Technical University</aff>
      </aff-alternatives>
      <aff-alternatives id="aff-2">
        <aff xml:lang="ru">Магнитогорский государственный технический университет им. Г.И. Носова</aff>
        <aff xml:lang="en">G.I. Nosov Magnitogorsk State Technical University</aff>
      </aff-alternatives>
      <pub-date pub-type="epub">
        <day>01</day>
        <month>01</month>
        <year>2026</year>
      </pub-date>
      <volume>1</volume>
      <issue>1</issue>
      <elocation-id>10.26102/2310-6018/2026.59.8.001</elocation-id>
      <permissions>
        <copyright-statement>Copyright © Авторы, 2026</copyright-statement>
        <copyright-year>2026</copyright-year>
        <license license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/">
          <license-p>This work is licensed under a Creative Commons Attribution 4.0 International License</license-p>
        </license>
      </permissions>
      <self-uri xlink:href="https://moitvivt.ru/ru/journal/article?id=2423"/>
      <abstract xml:lang="ru">
        <p>Ряд оптимизационных задач, возникающих при рационализации логистики склада непрерывнолитых заготовок металлургического предприятия, оперирует вектором параметров целевой функции переменной длины. Это накладывает ограничения на выбор методов, применимых для решения таких задач. Ряд существующих методов приведения вектора параметров целевой функции к постоянному размеру, которые применяются для комбинаторной оптимизации, обладают выразительной ограниченностью, т. е. применимы лишь к узкоспециализированному классу задач. Целью настоящего исследования является приведение вектора параметров целевой функции, возникающей в задачах оптимизации работы мостовых кранов на складе слябов, к постоянному размеру, что позволит для решаемой оптимизационной задачи использовать все известные методы оптимизации из подходящего класса, а также понизит ресурсоемкость алгоритма оптимизации за счет сокращения количества параметров целевой функции. В данной работе авторы предлагают метод, являющийся обобщением L-систем и клеточного кодирования, позволяющий для заданной целевой функции перейти от вектора параметров переменного размера к вектору параметров постоянного размера. Новизной работы является обобщение идей, специфичных для узкоспециализированных областей научного знания, на множество классов оптимизационных задач, в частности на задачи составления расписания для мостовых кранов. Авторами рассмотрена целевая функция, возникающая при совместном решения задач составления расписания мостовых кранов и выбора слябов для графика горячей прокатки. Для этой целевой функции был применен предложенный метод: описаны функции смены поискового пространства в виде простых алгоритмов. Результатом явилось сокращение размерности векторов целевой функции минимум в 10 раз. Предложенный метод может быть использован при проектировании цифрового двойника склада непрерывнолитых заготовок, а также для управления иерархией команд: от самых простых (для управления отдельными компонентами кранов) до самых сложных (задающих логистические операции над слябами).</p>
      </abstract>
      <trans-abstract xml:lang="en">
        <p>A number of optimization problems that arise when rationalizing the warehouse logistics of a continuously cast slab yard of a metallurgical enterprise operate with a variable-length vector of parameters of the objective function. This circumstance, consequently, restricts the choice of methods applicable to such problems. Moreover, an increase in the parameter vector of the objective function in many cases raises the resource intensity of the optimization algorithm. Several existing methods that reduce the parameter vector of the objective function to a constant size and that find use in combinatorial optimization suffer from a pronounced limitation: they apply only to a highly specialized class of problems. The present study aims to reduce the parameter vector of the objective function that appears in problems of optimizing the work of bridge cranes in a slab yard to a constant size. Such a reduction will allow one to use, for the optimization problem under consideration, all known optimization methods from the appropriate class and, in addition, will lower the resource intensity of the optimization algorithm by decreasing the number of parameters of the objective function. In this work, the authors propose a method that generalizes L‑systems and cellular coding and makes it possible, for a given objective function, to move from a variable‑size parameter vector to a constant‑size parameter vector. The novelty of the work consists in extending ideas that are specific to highly specialized fields of scientific knowledge to a set of classes of optimization problems, in particular to scheduling problems for bridge cranes. The authors examine an objective function that arises when one jointly solves bridge crane scheduling problems and the problem of selecting slabs for a hot rolling schedule. They apply the proposed method to this objective function and describe the functions that change the search space in the form of simple algorithms. As a result, the dimensionality of the objective function vectors decreases at least by a factor of 10. The proposed method can be used in the design of a digital twin for a continuous casting billet storage yard, as well as for managing a hierarchy of commands: from the simplest (for controlling individual crane components) to the most complex (defining logistics operations involving slabs).</p>
      </trans-abstract>
      <kwd-group xml:lang="ru">
        <kwd>вектор параметров</kwd>
        <kwd>целевая функция</kwd>
        <kwd>слябы</kwd>
        <kwd>клеточное кодирование</kwd>
        <kwd>непрямое кодирование</kwd>
        <kwd>прямое кодирование</kwd>
      </kwd-group>
      <kwd-group xml:lang="en">
        <kwd>parameter vector</kwd>
        <kwd>objective function</kwd>
        <kwd>slabs</kwd>
        <kwd>cellular encoding</kwd>
        <kwd>indirect encoding</kwd>
        <kwd>direct encoding</kwd>
      </kwd-group>
      <funding-group>
        <funding-statement xml:lang="ru">Исследование выполнено без спонсорской поддержки.</funding-statement>
        <funding-statement xml:lang="en">The study was performed without external funding.</funding-statement>
      </funding-group>
    </article-meta>
  </front>
  <back>
    <ref-list>
      <title>References</title>
      <ref id="cit1">
        <label>1</label>
        <mixed-citation xml:lang="ru">Щеголихин И.С., Андреев С.М. Математическая модель расписания кранов для графика горячей прокатки непрерывнолитых заготовок на складе слябов. Автоматизированные технологии и производства. 2024;(1):13–16.</mixed-citation>
      </ref>
      <ref id="cit2">
        <label>2</label>
        <mixed-citation xml:lang="ru">Shchegolikhin I.S., Andreev S.M. Mathematical Model of Crane Scheduling for a Defined Slab Storage Locations Assignment in a Continuous Cast Billet Warehouse. In: 2024 International Russian Automation Conference (RusAutoCon), 08–14 September 2024, Sochi, Russia. IEEE; 2024. P. 1193–1199. https://doi.org/10.1109/RusAutoCon61949.2024.10694388</mixed-citation>
      </ref>
      <ref id="cit3">
        <label>3</label>
        <mixed-citation xml:lang="ru">Kuyama S., Tomiyama S. A Two-phase Heuristic for Crane Scheduling in Steel Slab Yards. IFAC Proceedings Volumes. 2013;46(24):388–393. https://doi.org/10.3182/20130911-3-BR-3021.00039</mixed-citation>
      </ref>
      <ref id="cit4">
        <label>4</label>
        <mixed-citation xml:lang="ru">Bruno G., Cavola M., Diglio A., et al. A unifying framework and a mathematical model for the Slab Stack Shuffling Problem. International Journal of Industrial Engineering Computations. 2023;14(1):17–32. https://doi.org/10.5267/j.ijiec.2022.10.005</mixed-citation>
      </ref>
      <ref id="cit5">
        <label>5</label>
        <mixed-citation xml:lang="ru">Peng G., Wu Y., Zhang Ch., et al. Integrated optimization of storage location assignment and crane scheduling in an unmanned slab yard. Computers &amp; Industrial Engineering. 2021;161(3):107623. https://doi.org/10.1016/j.cie.2021.107623</mixed-citation>
      </ref>
      <ref id="cit6">
        <label>6</label>
        <mixed-citation xml:lang="ru">Tang L., Liu J., Rong A., et al. An effective heuristic algorithm to minimise stack shuffles in selecting steel slabs from the slab yard for heating and rolling. Journal of the Operational Research Society. 2001;52(10):1091–1097. https://doi.org/10.1057/palgrave.jors.2601143</mixed-citation>
      </ref>
      <ref id="cit7">
        <label>7</label>
        <mixed-citation xml:lang="ru">Lu Ch., Zhang R., Liu Sh. A 0-1 integer programming model and solving strategies for the slab storage problem. International Journal of Production Research. 2016;54(8):2366–2376. https://doi.org/10.1080/00207543.2015.1076949</mixed-citation>
      </ref>
      <ref id="cit8">
        <label>8</label>
        <mixed-citation xml:lang="ru">Shchegolikhin I.S., Andreev S.M. Development and Analysis of Genetic Algorithm for Optimization of Continuous Cast Billets Warehousing Process. In: 2024 International Russian Smart Industry Conference (SmartIndustryCon), 25–29 March 2024, Sochi, Russia. IEEE; 2024. P. 962–967. https://doi.org/10.1109/SmartIndustryCon61328.2024.10515628</mixed-citation>
      </ref>
      <ref id="cit9">
        <label>9</label>
        <mixed-citation xml:lang="ru">Erwin K., Engelbrecht A. Meta-heuristics for portfolio optimization. Soft Computing. 2023;27(24):19045–19073. https://doi.org/10.1007/s00500-023-08177-x</mixed-citation>
      </ref>
      <ref id="cit10">
        <label>10</label>
        <mixed-citation xml:lang="ru">Hwang Sh.-F., He R.-S. A hybrid real-parameter genetic algorithm for function optimization. Advanced Engineering Informatics. 2006;20(1):7–21. https://doi.org/10.1016/j.aei.2005.09.001</mixed-citation>
      </ref>
      <ref id="cit11">
        <label>11</label>
        <mixed-citation xml:lang="ru">Ono I., Kita H., Kobayashi Sh. A Real-coded Genetic Algorithm using the Unimodal Normal Distribution Crossover. In: Advances in Evolutionary Computing: Theory and Applications. Berlin, Heidelberg: Springer; 2003. P. 213–237. https://doi.org/10.1007/978-3-642-18965-4_8</mixed-citation>
      </ref>
      <ref id="cit12">
        <label>12</label>
        <mixed-citation xml:lang="ru">Herrera F., Lozano M. Gradual distributed real-coded genetic algorithms. IEEE Transactions on Evolutionary Computation. 2000;4(1):43–63. https://doi.org/10.1109/4235.843494</mixed-citation>
      </ref>
      <ref id="cit13">
        <label>13</label>
        <mixed-citation xml:lang="ru">Fontes D.B.M.M., Homayouni S.M., Gonçalves J.F. A hybrid particle swarm optimization and simulated annealing algorithm for the job shop scheduling problem with transport resources. European Journal of Operational Research. 2022;306(3):1140–1157. https://doi.org/10.1016/j.ejor.2022.09.006</mixed-citation>
      </ref>
      <ref id="cit14">
        <label>14</label>
        <mixed-citation xml:lang="ru">Wang J., Liu Ch., Li K. A hybrid simulated annealing for scheduling in dual-resource cellular manufacturing system considering worker movement. Automatika. 2019;60(2):172–180. https://doi.org/10.1080/00051144.2019.1603264</mixed-citation>
      </ref>
      <ref id="cit15">
        <label>15</label>
        <mixed-citation xml:lang="ru">Riazi A. Genetic algorithm and a double-chromosome implementation to the traveling salesman problem. SN Applied Sciences. 2019;1(11):1397. https://doi.org/10.1007/s42452-019-1469-1</mixed-citation>
      </ref>
      <ref id="cit16">
        <label>16</label>
        <mixed-citation xml:lang="ru">Yang J., Wu Ch., Lee H.P., et al. Solving traveling salesman problems using generalized chromosome genetic algorithm. Progress in Natural Science. 2008;18(7):887–892. https://doi.org/10.1016/j.pnsc.2008.01.030</mixed-citation>
      </ref>
      <ref id="cit17">
        <label>17</label>
        <mixed-citation xml:lang="ru">Zhang Q., Ding L. A new crossover mechanism for genetic algorithms with variable-length chromosomes for path optimization problems. Expert Systems with Applications. 2016;60:183–189. https://doi.org/10.1016/j.eswa.2016.04.005</mixed-citation>
      </ref>
      <ref id="cit18">
        <label>18</label>
        <mixed-citation xml:lang="ru">Stanley K.O., Miikkulainen R. A Taxonomy for Artificial Embryogeny. Artificial Life. 2003;9(2):93–130. https://doi.org/10.1162/106454603322221487</mixed-citation>
      </ref>
      <ref id="cit19">
        <label>19</label>
        <mixed-citation xml:lang="ru">Ariffin M.K.b., Hadi Sh., Phon-Amnuaisuk S. Evolving 3D Models Using Interactive Genetic Algorithms and L-Systems. In: Multi-disciplinary Trends in Artificial Intelligence: 11th International Workshop, 20–22 November 2017, Gadong, Brunei. Cham: Springer; 2017. P. 485–493. https://doi.org/10.1007/978-3-319-69456-6_40</mixed-citation>
      </ref>
      <ref id="cit20">
        <label>20</label>
        <mixed-citation xml:lang="ru">Broni-Bediako C., Murata Y., Mormille L.H.B., et al. Evolutionary NAS with Gene Expression Programming of Cellular Encoding. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 01–04 December 2020, Canberra, Australia. IEEE; 2020. P. 2670–2676. https://doi.org/10.1109/SSCI47803.2020.9308346</mixed-citation>
      </ref>
      <ref id="cit21">
        <label>21</label>
        <mixed-citation xml:lang="ru">Tang L., Liu J., Rong A., et al. Modelling and a genetic algorithm solution for the slab stack shuffling problem when implementing steel rolling schedules. International Journal of Production Research. 2002;40(7):1583–1595. https://doi.org/10.1080/00207540110110118424</mixed-citation>
      </ref>
    </ref-list>
    <fn-group>
      <fn fn-type="conflict">
        <p>The authors declare that there are no conflicts of interest present.</p>
      </fn>
    </fn-group>
  </back>
</article>