Title :
Solving complex task scheduling by a hybrid genetic algorithm
Author :
Jun-qing Li ; Quan-ke Pan ; Kun Maoa
Author_Institution :
State Key Lab. of Synthetic Autom. for Process Ind., Northeastern Univ., Shenyang, China
Abstract :
The Internet-of-Things (IoT) aims to connect everything on the Internet. One main advantage of IoT is the task assignment among entities. The task scheduling in IoT is very complex because there exist complex relationship between devices. In this study, we introduce the task scheduling problem with multiple processing sequence relation constraints in IoT system. Several benchmarks are given in this paper, the corresponding Gantt charts are displayed as well.
Keywords :
Internet; Internet of Things; bar charts; genetic algorithms; scheduling; task analysis; Gantt charts; Internet-of-Things; IoT; complex task scheduling; hybrid genetic algorithm; multiple processing sequence; Algorithm design and analysis; Benchmark testing; Genetic algorithms; Job shop scheduling; Optimization; Radiofrequency identification; Vectors; Internet-of-Things; benchmarks; processing sequence relation; task scheduling;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2014 11th World Congress on
DOI :
10.1109/WCICA.2014.7053287
