Publication:
Genetic-Memetic Relational Approach for Scheduling Problems

Дата
2022
Авторы
Nelyubin, A. P.
Misyurin, S. Y.
Journal Title
Journal ISSN
Volume Title
Издатель
Научные группы
Организационные подразделения
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Организационная единица
Институт интеллектуальных кибернетических систем
Цель ИИКС и стратегия развития - это подготовка кадров, способных противостоять современным угрозам и вызовам, обладающих знаниями и компетенциями в области кибернетики, информационной и финансовой безопасности для решения задач разработки базового программного обеспечения, повышения защищенности критически важных информационных систем и противодействия отмыванию денег, полученных преступным путем, и финансированию терроризма.
Выпуск журнала
Аннотация
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.A general approach to the construction and optimization of schedules is proposed, based on the representation of schedules in the form of a set of binary relations. The description of the schedule in the language of relations is natural and reflects its essential characteristics. It allows us to formalize a lot of flexible constraints involving different priorities, requests, wishes of the schedule participants. It also brings us closer to solving the problem of schedule recognition arising in the process of regular rescheduling. To optimize the schedules, we use a hybrid algorithm scheme that includes genetic, memetic, greedy algorithms, and heuristic rules. The proposed relations are used to encode key scheduling features within the genetic and memetic routine. The relational approach allows us to derive new information maintaining the consistency. As an example of the application of the approach, the formulation of the problem of distribution of objects among a group of autonomous mobile robots during emergency rescue or exploration work is proposed.
Описание
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Цитирование
Nelyubin, A. P. Genetic-Memetic Relational Approach for Scheduling Problems / Nelyubin, A.P., Misyurin, S.Y. // Studies in Computational Intelligence. - 2022. - 1032 SCI. - P. 281-287. - 10.1007/978-3-030-96993-6_30
URI
https://www.doi.org/10.1007/978-3-030-96993-6_30
 https://www.scopus.com/record/display.uri?eid=2-s2.0-85127659929&origin=resultslist
 http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000833484200030
 https://openrepository.mephi.ru/handle/123456789/28950
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