Fitness Landscapes of Buffer Allocation Problem in Production Lines and Genetic Algorithms Performance | Sciact - Система учета научной деятельности ИМ СО РАН

Fitness Landscapes of Buffer Allocation Problem in Production Lines and Genetic Algorithms Performance Научная публикация

Конференция

The Genetic and Evolutionary Computation Conference, GECCO 2025
14-18 июл. 2025 , Malaga Hotel Malaga Spain

Сборник

GECCO '25 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion
Сборник, Publisher: Association for Computing Machinery, NY, United States. New York.2025. ISBN 979-8-4007-1464-1.

Вых. Данные

Год: 2025, Страницы: 27-28 Страниц : 2 DOI: 10.1145/3712255.3734243

Ключевые слова

Genetic algorithm, local optima, production line, unreliable machines, buffer allocation

Авторы

Dolgui Alexandre ¹ , Eremeev Anton ^2,3 , Sigaev Vyatcheslav ⁴

Организации

1	IMT Atlantique, LS2N-CNRS, Nantes, France
2	Novosibirsk State University, Novosibirsk, Russian Federation
3	Sobolev Institute of Mathematics, Novosibirsk, Russian Federation
4	Avtomatika-Servis LLC, Omsk, Russian Federation

Информация о финансировании (2)

1	Российский научный фонд	21-41-09017
2	Омский филиал ФГБУН «Институт математики им. С.Л. Соболева СО РАН».	FWNF-2022-0020

We study structural properties of the buffer allocation problem from the fitness landscape perspective. We consider manufacturing flow lines with series-parallel network structure. The machines are supposed to be unreliable, their time to failure and repair time are exponentially distributed. We carry out computational experiments with local search and genetic algorithms in order to evaluate the fitness landscape properties of previously published instances and their modifications. We show that in many problem instances, several clusters of local optima can be identified. Besides that, the so-called 'massif central' or 'big valley' structure of the fitness landscape is present only partially. The performance of genetic algorithms is discussed with respect to population clustering. The crossover operator is shown to be useful on those problem instances, where the population clustering was observed and the permanent usage of crossover is recommended.

Dolgui A. , Eremeev A. , Sigaev V.
Fitness Landscapes of Buffer Allocation Problem in Production Lines and Genetic Algorithms Performance
В сборнике GECCO '25 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion. – Publisher: Association for Computing Machinery, NY, United States., 2025. – C.27-28. – ISBN 979-8-4007-1464-1. DOI: 10.1145/3712255.3734243 Scopus OpenAlex

Опубликована в печати:	11 авг. 2025 г.
Опубликована online:	11 авг. 2025 г.

Scopus:	2-s2.0-105014587400
OpenAlex:	W4413214842

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