Multiobjective parallel machine scheduling in the sawmill industry using memetic algorithms
Date
2014
Type:
Artículo
item.page.extent
item.page.accessRights
Authors
item.contributor.advisor
ORCID:
Journal Title
Journal ISSN
Volume Title
Publisher
item.page.isbn
item.page.issn
item.page.issne
item.page.doiurl
item.page.other
item.page.references
Abstract
This study presents a multiobjective optimization algorithm that is termed memetic algorithm with compromise search (MACS). This algorithm, proposed by the authors, combines genetic evolution with local search, in the same way as traditional memetic algorithms, but with the use of independent populations for each objective, as well as a mechanism for finding compromise solutions (tradeoffs) via a local search operator. The algorithm was applied to a parallel machine scheduling problem involving a molding production process in the wood industry. The algorithm was compared against four multiobjective techniques available in the literature: the multiobjective genetic algorithm (MOGA), the strength Pareto evolutionary algorithm (SPEA), the non-sorting genetic algorithm II (NSGA II), and multiobjective genetic local search (MOGLS). The proposed approach outperformed the benchmark techniques in most of the test problems based on two objectives of industrial interest: minimization of the maximum completion time (Cmax) and minimization of total tardiness. These objectives are directly related to the productivity of the product and the ability to deliver goods on time.
Description
item.page.coverage.spatial
item.page.sponsorship
Citation
The International Journal of Advanced Manufacturing Technology , 2014, 74 ( 5 ): 757–768
Keywords
Multiobjective, Memetic, Tradeoff, Scheduling, Parallel machine, Wood industry