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Parallel evolutionary algorithms for the reconfigurable transfer line balancing problem Научная публикация

Журнал Yugoslav Journal of Operations Research
ISSN: 0354-0243 , E-ISSN: 2334-6043
Вых. Данные Год: 2024, Том: 34, Номер: 1, Страницы: 93-107 Страниц : 15 DOI: 10.2298/yjor230415018b
Ключевые слова CNC machines, partial order, setup times, split decoder, parallel computing, scalability
Авторы Borisovsky Pavel A. 1
Организации
1 Sobolev Institute of Mathematics SB RAS, Novosibirsk, Russia

Информация о финансировании (1)

1 Омский филиал ФГБУН «Институт математики им. С.Л. Соболева СО РАН». FWNF-2022-0020

Реферат: This paper deals with an industrial problem of machining line design, which consists in partitioning a given set of operations into several subsets corresponding toworkstations and sequencing the operations to satisfy the technical requirements and achieve the best performance of the line. The problem has a complex set of constraints that include partial order on operations, part positioning, inclusion, exclusion, cycletime, and installation of parallel machines on a workstation. The problem is NP-hard and even finding a feasible solution can be a difficult task from the practical point of view. A parallel evolutionary algorithm (EA) is proposed and implemented for execution on a Graphics Processing Unit (GPU). The parallelization in the EA is done by working on several parents in one iteration and in multiple application of mutation operator to the same parent to produce the best offspring. The proposed approach is evaluated on large scale instances and demonstrated superior performance compared to the algorithms from the literature in terms of running time and ability to obtain feasible solutions. It is shown that in comparison to the traditional populational EA scheme the newly proposed algorithm is more suitable for advanced GPUs with a large number of cores.
Библиографическая ссылка: Borisovsky P.A.
Parallel evolutionary algorithms for the reconfigurable transfer line balancing problem
Yugoslav Journal of Operations Research. 2024. V.34. N1. P.93-107. DOI: 10.2298/yjor230415018b Scopus РИНЦ OpenAlex
Даты:
Поступила в редакцию: 17 июл. 2023 г.
Опубликована online: 29 авг. 2023 г.
Опубликована в печати: 2 февр. 2024 г.
Идентификаторы БД:
Scopus: 2-s2.0-85187291601
РИНЦ: 62755792
OpenAlex: W4386411932
Цитирование в БД:
БД Цитирований
OpenAlex 1
Scopus 1
Альметрики: