Evolutionary Algorithms for Job Shop Scheduling

被引：0
|
作者：
Toader, Florentine Alina [1 ]
机构：
[1] Petr Gas Univ Ploiesti, Dept Informat Informat Technol Math & Phys, Ploiesti, Romania
来源：
2016 8TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTERS AND ARTIFICIAL INTELLIGENCE (ECAI) | 2016年
关键词：
Job Shop Scheduling; Evolutionary Algorithms; Scheduling Optimization;
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：
Job Shop Scheduling Problem (JSSP) represents a real challenge for the researchers' community due to its complexity consisting in the plurality of resources that needs to be optimally used and the variety of goals that needs to be accomplished. This paper presents the implementation of three Evolutionary Algorithms (Genetic Algorithms, Particle Swarm Optimization and Ant Colony Optimization) for the JSSP. The tests are made considered a set of classical benchmarks for the proposed problem and the obtained results are subject to comparison.
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页数：6
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