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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/3951

Title: Integration of Multi-objective Genetic Algorithms and Expert Systems for Agroindustrial System Design
Authors: Y., Yandra
Tamura, Hiroyuki
Keywords: multi-objective
genetic algorithms
expert systems
Issue Date: Nov-2005
Publisher: JAIST Press
Abstract: This research aims to develop an integrated system for agroindustrial system design based on artificial intelligence approach. In the first instance, a new multi-objective genetic algorithm with Additional Diversity Module (ADM) is developed to optimize combinatorial flow-shop scheduling of an agroindustry. The unique feature of the optimization model used in this research is it uses make-span and Average Product Quality Deterioration (APQD) as optimization objectives. The result of genetic algorithm optimization is a set of compromise solution that called Pareto-optimum solutions. These solutions are then fed to an expert system to finding the most preferred solution by considering higher-level information. A nontrivial case study is then presented to demonstrate the capability of this intelligent system to solve an agroindustrial system design problem. The result indicates that the intelligent system developed in this work is robust and reliable.
Description: The original publication is available at JAIST Press http://www.jaist.ac.jp/library/jaist-press/index.html
IFSR 2005 : Proceedings of the First World Congress of the International Federation for Systems Research : The New Roles of Systems Sciences For a Knowledge-based Society : Nov. 14-17, 2161, Kobe, Japan
Symposium 3, Session 7 : Intelligent Information Technology and Applications Computational Intelligence (1)
Language: ENG
URI: http://hdl.handle.net/10119/3951
ISBN: 4-903092-02-X
Appears in Collections:IFSR 2005

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