Multi Agent Hyper-Heuristics based framework for production scheduling problem

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Nugraheni:2016:ICIC,

• author = "Cecilia E. Nugraheni and Luciana Abednego",
• booktitle = "2016 International Conference on Informatics and Computing (ICIC)",
• title = "Multi Agent Hyper-Heuristics based framework for production scheduling problem",
• year = "2016",
• pages = "309--313",
• 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.",
• keywords = "genetic algorithms, genetic programming, hyper-heuristic, multi agent, single machine production scheduling",
• DOI = "doi:10.1109/IAC.2016.7905735",
• month = oct,
• notes = "Also known as \cite{7905735}",

}

Genetic Programming entries for Cecilia Esti Nugraheni Luciana Abednego

Citations