Paper Info
Title
Job-Shop scheduling based on multiagent evolutionary algorithm
Abstract
With the intrinsic properties of job-shop scheduling problems (JSPs) in mind, we integrate the multiagent systems and evolutionary algorithms to form a new algorithm, Multiagent Evolutionary Algorithm for JSPs (MAEA-JSPs). In MAEA-JSPs, all agents live in a latticelike environment. Making use of the designed behaviors, MAEA-JSPs realizes the ability of agents to sense and act on the environment in which they live. During the process of interacting with the environment and the other agents, each agent increases energy as much as possible, so that MAEA-JSPs can find the optima. In the experiments, 59 benchmark JSPs are used, and good performance is obtained.
Year
DOI
Venue
2005
10.1007/11539902_114
ICNC (3)
Keywords
Field
DocType
multiagent evolutionary algorithm,agent increases energy,job-shop scheduling,latticelike environment,good performance,intrinsic property,new algorithm,multiagent system,evolutionary algorithm,job-shop scheduling problem,benchmark jsps,job shop scheduling
Job shop scheduling,Evolutionary algorithm,Scheduling (computing),Computer science,Job shop,Multi-agent system,Artificial intelligence,Local search (optimization),Genetic algorithm,Distributed computing
Conference
Volume
ISSN
ISBN
3612
0302-9743
3-540-28320-X
Citations 
PageRank 
References 
2
0.39
6
Authors
3
Name
Order
Citations
PageRank
Weicai Zhong138126.14
Jing Liu21043115.54
Licheng Jiao35698475.84