An Artificial Intelligence Software Application for Solving Job Shop Scheduling Problem

被引：0
|
作者：
Toader, Florentina Alina [1 ]
机构：
[1] Petr Gas Univ Ploiesti, Dept Informat Informat Technol Math & Phys, Ploiesti, Romania
来源：
PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON VIRTUAL LEARNING | 2014年
关键词：
Job Shop Scheduling Problem; Ant Colony Optimization; artificial intelligence; production scheduling; ALGORITHM;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Job Shop Scheduling is an NP-hard problem and due to his complexity the researcher's attention was focused in this area in order to find the most suitable approaches. The current paper presents an implementation in this field of an artificial intelligence technique: Ant Colony Optimization. This probabilistic technique is inspired by the behavior of ants in their search of a source of food. The algorithm consists in a system of artificial ants that are able to communicate thru the pheromone trail and identify the best solution for the proposed problem. The algorithm is tested for different production scenarios and in order to identify the algorithm performance, a comparison with two classical scheduling algorithms is presented.
引用
收藏
页码：439 / 443
页数：5
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