Cooperative Bayesian Optimization Algorithm: A novel approach to multiple resources scheduling problem

Xinchang Hao*, Hao Wen Lin, Xili Chen, Tomohiro Murata

*この研究の対応する著者

研究成果: Article査読

6 被引用数 (Scopus)

抄録

During the past several years, there has been a significant number of researches conducted in the field of Multiple Resources Scheduling Problem (MRSP). Intelligent manufacturing planning and scheduling based on meta-heuristic methods, such as Genetic Algorithms (GAs), Simulated Annealing (SA), and Particle Swarm Optimization (PSO), have become some of the common tools for finding acceptable solutions within reasonable computational time in real settings. However, limited researches were conducted at analysing the effects of interdependent relationships between each activity of group decision-making processes. Moreover for a complex and large problem, local constraints and objectives from each managerial entity, and their effects on global objectives of the problem cannot be effectively represented using a single model. In this paper, we propose a novel Cooperative Bayesian Optimization Algorithm (COBOA) to overcome the challenges mentioned afore. The COBOA approach employs the concepts of divide-and-conquer strategy and it is incorporated with an innovative co-evolutionary framework. Considerable experiments were performed, and the results confirmed that COBOA outperforms recent research results for scheduling problems in FMS.

本文言語English
ページ(範囲)2007-2018
ページ数12
ジャーナルIEEJ Transactions on Electronics, Information and Systems
132
12
DOI
出版ステータスPublished - 2012

ASJC Scopus subject areas

  • 電子工学および電気工学

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