Publication
A genetic algorithm for the job shop scheduling with a new local search using Monte Carlo method
| dc.contributor.author | Magalhães-Mendes, J. | |
| dc.date.accessioned | 2014-03-28T12:45:12Z | |
| dc.date.available | 2014-03-28T12:45:12Z | |
| dc.date.issued | 2011 | |
| dc.description.abstract | This paper presents a methodology for applying scheduling algorithms using Monte Carlo simulation. The methodology is based on a decision support system (DSS). The proposed methodology combines a genetic algorithm with a new local search using Monte Carlo Method. The methodology is applied to the job shop scheduling problem (JSSP). The JSSP is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. The methodology is tested on a set of standard instances taken from the literature and compared with others. The computation results validate the effectiveness of the proposed methodology. The DSS developed can be utilized in a common industrial or construction environment. | por |
| dc.identifier.isbn | 9789604742738 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/4287 | |
| dc.language.iso | eng | por |
| dc.peerreviewed | yes | por |
| dc.publisher | ACM | por |
| dc.relation.ispartofseries | AIKED'11; | |
| dc.relation.publisherversion | http://dl.acm.org/citation.cfm?id=1959491 | por |
| dc.title | A genetic algorithm for the job shop scheduling with a new local search using Monte Carlo method | por |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.conferencePlace | Cambridge, UK | por |
| oaire.citation.endPage | 31 | por |
| oaire.citation.startPage | 26 | por |
| oaire.citation.title | 10th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases | por |
| rcaap.rights | closedAccess | por |
| rcaap.type | article | por |