Heuristics for online three-dimensional packing problems and algorithm selection framework for semi-online with full look-ahead

• Tipo: Artigo em Revista Científica Internacional
• Data: 2024

Autores

Ali, S (Autor) Outra; Ramos, AG (Autor) Outra; Maria Antónia Carravilla (Autor) FEUP; José Fernando Oliveira (Autor) FEUP

Revista

Título: Applied Soft Computing Journal

Vol. 151

ISSN: 1568-4946

Editora: Elsevier

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00Z-KQP

DOI: 10.1016/j.asoc.2023.111168

Abstract (EN): In online three-dimensional packing problems (3D-PPs), unlike offline problems, items arrive sequentially and require immediate packing decisions without any information about the quantities and sizes of the items to come. Heuristic methods are of great importance in solving online problems to find good solutions in a reasonable amount of time. However, the literature on heuristics for online problems is sparse. As our first contribution, we developed a pool of heuristics applicable to online 3D-PPs with complementary performance on different sets of instances. Computational results showed that in terms of the number of used bins, in all problem instances, at least one of our heuristics had a better or equal performance compared to existing heuristics in the literature. The developed heuristics are also fully applicable to an intermediate class between offline and online problems, referred to in this paper as a specific type of semi-online with full look-ahead, which has several practical applications. In this class, as in offline problems, complete information about all items is known in advance (i.e., full look-ahead); however, due to time or space constraints, as in online problems, items should be packed immediately in the order of their arrival. As our second contribution, we presented an algorithm selection framework, building on developed heuristics and utilizing prior information about items in this specific class of problems. We used supervised machine learning techniques to find the relationship between the features of problem instances and the performance of heuristics and to build a prediction model. The results indicate an 88% accuracy in predicting (identifying) the most promising heuristic(s) for solving any new instance from this class of problems.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14