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Algorithms to solve the dynamic weighted tardiness problem

Lasso, Marta Graciela et al · SEDICI UNLP · 2002

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In static scheduling problems it is assumed that jobs are ready at zero time or before processing begins. In dynamic scheduling problems a job arrival can be given at any instant in the time interval between zero and a limit established by its processing time, ensuring to accomplish it before the due date deadline. In the cases where the arrivals are near to zero the problem comes closer to the static problem, otherwise the problem becomes more restrictive. This paper proposes two approaches for resolution of the dynamic problem of Total Weighted Tardiness for a single machine environment. The first approach uses, as a list of dispatching priorities a schedule, which an evolutionary algorithm found as the best for a similar static problem: same job features, processing time, due dates and weights. The second approach uses as a dispatching priority a schedule created by a robust non-evolutionary heuristic. The details of implementation of the proposed algorithms and results for a group of selected instances are discussed in this work. Eje: Sistemas inteligentes Red de Universidades con Carreras en Informática (RedUNCI)

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APA 7

Lasso, M. G. E. A. (2002). Algorithms to solve the dynamic weighted tardiness problem. SEDICI UNLP. http://sedici.unlp.edu.ar/handle/10915/23133

MLA

Lasso, Marta Graciela et al. Algorithms to solve the dynamic weighted tardiness problem. SEDICI UNLP, 2002. http://sedici.unlp.edu.ar/handle/10915/23133.

Chicago

Lasso, Marta Graciela et al. 2002. Algorithms to solve the dynamic weighted tardiness problem. SEDICI UNLP. http://sedici.unlp.edu.ar/handle/10915/23133.

Harvard

Lasso, M. G. E. A. 2002, Algorithms to solve the dynamic weighted tardiness problem, SEDICI UNLP, available at: http://sedici.unlp.edu.ar/handle/10915/23133 [Accessed 5 Aug. 2026].

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Title
Algorithms to solve the dynamic weighted tardiness problem
Author / contributors
Lasso, Marta Graciela et al
Publisher
SEDICI UNLP
Publication year
2002
Language
English

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