A Genetics-Based Machine Learning Approach for Realtime Scheduling

Autor:	Toshiharu Iwatani, Hisashi Tamaki, Shinzo Kitamura, Kazutoshi Sakakibara, Kouichi Matsuda, Hajime Murao
Rok vydání:	2003
Předmět:	Job shop scheduling Total cost business.industry Computer science Artificial intelligence Electrical and Electronic Engineering Machine learning computer.software_genre business computer Material handling Scheduling (computing)
Zdroj:	IEEJ Transactions on Electronics, Information and Systems. 123:823-831
ISSN:	1348-8155 0385-4221
DOI:	10.1541/ieejeiss.123.823
Popis:	In this paper, we adopt a genetic-based machine learning (GBML) approach to a realtime scheduling problem in which several products are to be assigned to one of the buffers, and propose a method of generating and selecting rules for assigning each product to a desirable buffer. In applying the GBML, we use the Pitt approach, where the set of rules (rule-set) is represented symbolically as an individual of genetic algorithms, and the fitness of an individual is calculated based on the total cost required for transporting, operating and keeping of whole products. Through some computational experiments, the effectiveness and the possibility of our approach is investigated.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c39daabca2ddaef396342bb841303ff2 https://doi.org/10.1541/ieejeiss.123.823 Zobrazit plný text záznamu