An extended Akers graphical method with a biased random-key genetic algorithm for job-shop scheduling

• Tipo: Artigo em Revista Científica Internacional
• Data: 2014

Autores

Jose Fernando Goncalves (Autor) FEP; Mauricio G C Resende (Autor) Outra

Revista

Título: International Transactions in Operational Research

Vol. 21

Páginas: 215-246

ISSN: 0969-6016

Editora: Wiley-Blackwell

Indexação

ISI Web of Knowledge - 29 Citações

Scopus - 42 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Biotecnologia industrial

Outras Informações

ID Authenticus: P-008-ND3

DOI: 10.1111/itor.12044

Abstract (EN): This paper presents a local search, based on a new neighborhood for the job-shop scheduling problem, and its application within a biased random-key genetic algorithm. Schedules are constructed by decoding the chromosome supplied by the genetic algorithm with a procedure that generates active schedules. After an initial schedule is obtained, a local search heuristic, based on an extension of the 1956 graphical method of Akers, is applied to improve the solution. The new heuristic is tested on a set of 205 standard instances taken from the job-shop scheduling literature and compared with results obtained by other approaches. The new algorithm improved the best-known solution values for 57 instances.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 32