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

• Type: Article in International Scientific Journal
• Date: 2014

Authors

Jose Fernando Goncalves (Author) FEP; Mauricio G C Resende (Author) Other

Journal

Title: International Transactions in Operational Research

Vol. 21

Pages: 215-246

ISSN: 0969-6016

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 29 Citations

Scopus - 42 Citations

Scientific classification

FOS: Engineering and technology > Industrial biotechnology

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 32