Comparative study of meta-heuristics for solving flow shop scheduling problem under fuzziness

被引：0
|
作者：
Gonzalez, Noelia [1 ]
Vela, Camino R. [1 ]
Gonzalez-Rodriguez, Ines [1 ]
机构：
[1] Univ Oviedo, Ctr Inteligencia Artificial, Campus Viesques, E-33271 Gijon, Spain
来源：
BIO-INSPIRED MODELING OF COGNITIVE TASKS, PT 1, PROCEEDINGS | 2007年 / 4527卷
关键词：
fuzzy sets; flow shop scheduling; meta-heuristics; evolutive algorithms;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we propose a hybrid method, combining heuristics and local search, to solve flow shop scheduling problems under uncertainty. This method is compared with a genetic algorithm from the literature, enhanced with three new multi-objective functions. Both single objective and multi-objective approaches are taken for two optimisation goals: minimisation of completion time and fulfilment of due date constraints. We present results for newly generated examples that illustrate the effectiveness of each method.
引用
收藏
页码：548 / +
页数：3
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