2008
DOI: 10.1016/j.amc.2007.07.022
|View full text |Cite
|
Sign up to set email alerts
|

Solving the resource availability cost problem in project scheduling by path relinking and genetic algorithm

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
31
0
2

Year Published

2010
2010
2015
2015

Publication Types

Select...
5
4
1

Relationship

0
10

Authors

Journals

citations
Cited by 56 publications
(33 citation statements)
references
References 8 publications
0
31
0
2
Order By: Relevance
“…The selected individuals are marked in order to not be selected again during the application of path-relinking. Path-relinking was also hybridized with a genetic algorithm as a postoptimization procedure (Ranjbar et al 2008). In that paper, the solutions in the final population produced by the genetic algorithm are progressively combined and refined.…”
Section: Progressive Crossover In Genetic Algorithmsmentioning
confidence: 99%
“…The selected individuals are marked in order to not be selected again during the application of path-relinking. Path-relinking was also hybridized with a genetic algorithm as a postoptimization procedure (Ranjbar et al 2008). In that paper, the solutions in the final population produced by the genetic algorithm are progressively combined and refined.…”
Section: Progressive Crossover In Genetic Algorithmsmentioning
confidence: 99%
“…В частном и наиболее распространённом случае данная стоимостная функция является линейной, то есть ( ) = , где -стоимость поддержания одной единицы ресурса в доступном состоянии. Задача ресурсных инвестиция были успешно решена в ряде работ [ [97], [98], [129], [130], [131]]. Стоит отметить работу [ [132]] в которой представлена расширенная задача ресурсных инвестиций: вместо крайнего срока завершения проекта (deadline) в ней используется директивный срок, что допускает появления задержки завершения всего проекта.…”
Section: минимизация затрат на возобновляемые ресурсыunclassified
“…Other meta-heuristics containing methods such as genetic algorithm [23][24][25][26], ant colony optimization [27], and particle swarm optimization [28][29][30] maintain a set of solutions at each cycle of the algorithm. These approaches solve the RCPSP by employing an initial population of individuals each of which representing a candidate schedule for the project.…”
Section: Related Workmentioning
confidence: 99%