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A Grouping Genetic Algorithm for the Temporal Vector Bin Packing Problem Научная публикация

Конференция Optimization Problems of Complex Systems : International Asian School-Seminar
14-22 авг. 2023 , Новосибирск
Сборник 2023 19th International Asian School-Seminar on Optimization Problems of Complex Systems (OPCS)
Сборник, IEEE. 2023. 6 c. ISBN 9798350331134.
Вых. Данные Год: 2023, Страницы: 94-99 Страниц : 6 DOI: 10.1109/opcs59592.2023.10275770
Ключевые слова temporal bin packing problem, genetic algorithm, greedy algorithm
Авторы Sakhno Maxim A. 1
Организации
1 Omsk Department, Sobolev Institute of Mathematics SB RAS, Omsk, Russia

Информация о финансировании (1)

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

Реферат: We consider the temporal vector bin packing problem that originated from cloud computing (Ratushnyi, Kochetov, 2021). Suppose we are given a finite set of items and for each item, an arriving time, a processing time, and two weights (dimensions) are known. A certain subset of all items is called a set of large items. Each bin has two capacities (dimensions) and is divided into two identical parts. A large item should be divided into two identical parts and placed in both parts of a bin. Other items can be placed in one part of a bin, if there is enough capacity there in both dimensions. The goal is to pack all items into the minimum number of bins of identical dimensions. We propose a grouping genetic algorithm for solving this problem, compare it with the Column Generation heuristic and describe the results of a computational experiment. The comparison of the algorithms was carried out on an open data set.
Библиографическая ссылка: Sakhno M.A.
A Grouping Genetic Algorithm for the Temporal Vector Bin Packing Problem
В сборнике 2023 19th International Asian School-Seminar on Optimization Problems of Complex Systems (OPCS). – IEEE., 2023. – C.94-99. – ISBN 9798350331134. DOI: 10.1109/opcs59592.2023.10275770 Scopus OpenAlex
Даты:
Опубликована в печати: 13 окт. 2023 г.
Опубликована online: 13 окт. 2023 г.
Идентификаторы БД:
Scopus: 2-s2.0-85175467509
OpenAlex: W4387620807
Цитирование в БД:
БД Цитирований
OpenAlex 2
Scopus 2
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