Multi Agent Hyper-Heuristics based framework for production scheduling problem
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- @InProceedings{Nugraheni:2016:ICIC,
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author = "Cecilia E. Nugraheni and Luciana Abednego",
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booktitle = "2016 International Conference on Informatics and
Computing (ICIC)",
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title = "Multi Agent Hyper-Heuristics based framework for
production scheduling problem",
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year = "2016",
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pages = "309--313",
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abstract = "This paper investigates the potential use of
hyper-heuristics and multi agent approach for solution
of the real single machine production scheduling
problem. A framework consisting of six agents is
proposed. The agents are Problem Agent, Trainer Agent,
Training Dataset Agent, Heuristic Pool Agent, Algorithm
Agent, Advisor Agent, and Solver Agent. Three Algorithm
Agents are proposed to solve the problem, i.e. Genetic
Programming Hyper-Heuristics (GPHH) agent, Genetic
Algorithm Hyper-Heuristic (GAHH) agent, and Simulated
Annealing Hyper-Heuristics (SAHH) agent. Experimental
results show that the performance of GAHH is comparable
with SAHH. While GPHH agent outperforms GAHH algorithm
agent and SAHH algorithm agent, and also six other
benchmark heuristics including MRT, SPT, LPT, EDD, LDD,
and MON rules with respect to minimum tardiness and
minimum flow time objectives.",
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keywords = "genetic algorithms, genetic programming,
hyper-heuristic, multi agent, single machine production
scheduling",
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DOI = "doi:10.1109/IAC.2016.7905735",
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month = oct,
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notes = "Also known as \cite{7905735}",
- }
Genetic Programming entries for
Cecilia Esti Nugraheni
Luciana Abednego
Citations