An enhanced branch-and-bound algorithm for the talent scheduling problem

•The talent scheduling problem is formulated as an IP model.•An enhanced B&B algorithm is proposed for the problem.•We employ a novel lower bound and memoization technique in the B&B.•Two new dominance rules are employed to reduce the search space.•Our algorithm outperforms the existing best...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:European journal of operational research Ročník 250; číslo 2; s. 412 - 426
Hlavní autoři: Qin, Hu, Zhang, Zizhen, Lim, Andrew, Liang, Xiaocong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 16.04.2016
Elsevier Sequoia S.A
Témata:
Algorithms
Branch & bound algorithms
Branch-and-bound
Dominance rules
Dynamic programming
Integer programming
Linear programming
Mathematical models
Problem solving
Scheduling
Studies
Talent scheduling
scheduling
Branch-and-bound
Talent scheduling
Dynamic programming
Dominance rules
ISSN:0377-2217, 1872-6860
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:•The talent scheduling problem is formulated as an IP model.•An enhanced B&B algorithm is proposed for the problem.•We employ a novel lower bound and memoization technique in the B&B.•Two new dominance rules are employed to reduce the search space.•Our algorithm outperforms the existing best approach. The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved. In this article, we first formulate the problem as an integer linear programming model. Next, we devise a branch-and-bound algorithm to solve the problem. The branch-and-bound algorithm is enhanced by several accelerating techniques, including preprocessing, dominance rules and caching search states. Extensive experiments over two sets of benchmark instances suggest that our algorithm is superior to the current best exact algorithm. Finally, the impacts of different parameter settings, algorithm components and instance generation distributions are disclosed by some additional experiments.
Bibliografie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2015.10.002