Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/80328
Título: | A statistical comparison of metaheuristics for unrelated parallel machine scheduling problems with setup times |
Autor(es): | Antunes, Ana Rita A. Matos, Marina Rocha, Ana Maria A. C. Costa, Lino Varela, M.L.R. |
Palavras-chave: | scheduling unrelated parallel machines sequence-dependent tasks makespan metaheuristics genetic algorithm statistical analysis |
Data: | 12-Jul-2022 |
Editora: | Multidisciplinary Digital Publishing Institute |
Revista: | Mathematics |
Citação: | Antunes, A.R.; Matos, M.A.; Rocha, A.M.A.C.; Costa, L.A.; Varela, L.R. A Statistical Comparison of Metaheuristics for Unrelated Parallel Machine Scheduling Problems with Setup Times. Mathematics 2022, 10, 2431. https://doi.org/10.3390/math10142431 |
Resumo(s): | Manufacturing scheduling aims to optimize one or more performance measures by allocating a set of resources to a set of jobs or tasks over a given period of time. It is an area that considers a very important decision-making process for manufacturing and production systems. In this paper, the unrelated parallel machine scheduling problem with machine-dependent and job-sequence-dependent setup times is addressed. This problem involves the scheduling of tasks on unrelated machines with setup times in order to minimize the makespan. The genetic algorithm is used to solve small and large instances of this problem when processing and setup times are balanced (Balanced problems), when processing times are dominant (Dominant P problems), and when setup times are dominant (Dominant S problems). For small instances, most of the values achieved the optimal makespan value, and, when compared to the metaheuristic ant colony optimization (ACOII) algorithm referred to in the literature, it was found that there were no significant differences between the two methods. However, in terms of large instances, there were significant differences between the optimal makespan obtained by the two methods, revealing overall better performance by the genetic algorithm for Dominant S and Dominant P problems. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/80328 |
DOI: | 10.3390/math10142431 |
e-ISSN: | 2227-7390 |
Versão da editora: | https://www.mdpi.com/2227-7390/10/14/2431 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
mathematics-10-02431-v2.pdf | 1,91 MB | Adobe PDF | Ver/Abrir |