A novel approach to production planning of flexible manufacturing systems using an efficient multi-objective genetic algorithm
| dc.citation.epage | 957 | en_US |
| dc.citation.issue | 7-8 | en_US |
| dc.citation.spage | 949 | en_US |
| dc.citation.volume | 45 | en_US |
| dc.citation.woscount | 29 | |
| dc.contributor.author | Chen, JH | en_US |
| dc.contributor.author | Ho, SY | en_US |
| dc.contributor.department | 生物資訊及系統生物研究所 | zh_TW |
| dc.contributor.department | Institude of Bioinformatics and Systems Biology | en_US |
| dc.date.accessioned | 2014-12-08T15:18:57Z | |
| dc.date.available | 2014-12-08T15:18:57Z | |
| dc.date.issued | 2005-06-01 | en_US |
| dc.description.abstract | In this paper, a novel approach using an efficient multi-objective genetic algorithm EMOGA is proposed to solve the problems of production planning of flexible manufacturing systems (FMSs) having four objectives: minimizing total flow time, machine workload unbalance. greatest machine workload and total tool cost. EMOGA makes use of Pareto dominance relationship to solve the problems without using relative preferences of multiple objectives. High efficiency of EMOGA arises from that multiple objectives can be optimized simultaneously Without using heuristics and a set of good non-dominated Solutions can be obtained providing additional degrees of freedom for the exploitation of resources of FMSs. Experimental results demonstrate effectiveness of the proposed approach using EMOGA to]production planning of FMSs. (c) 2004 Elsevier Ltd. All rights reserved. | en_US |
| dc.identifier.doi | 10.1016/j.ijmachtools.2004.10.010 | en_US |
| dc.identifier.issn | 0890-6955 | en_US |
| dc.identifier.journal | INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE | en_US |
| dc.identifier.uri | http://dx.doi.org/10.1016/j.ijmachtools.2004.10.010 | en_US |
| dc.identifier.uri | https://ir.lib.nycu.edu.tw/handle/11536/13615 | |
| dc.identifier.wosnumber | WOS:000228207300023 | |
| dc.language.iso | en_US | en_US |
| dc.subject | flexible manufacturing system | en_US |
| dc.subject | multi-objective optimization | en_US |
| dc.subject | genetic algorithm | en_US |
| dc.subject | production planning | en_US |
| dc.title | A novel approach to production planning of flexible manufacturing systems using an efficient multi-objective genetic algorithm | en_US |
| dc.type | Article | en_US |