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

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

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2007-2018
Number of pages12
JournalIEEJ Transactions on Electronics, Information and Systems
Volume132
Issue number12
DOIs
Publication statusPublished - 2012

Keywords

  • Bayesian network
  • Co-evolutionary algorithm
  • Estimation of distribution algorithm
  • Multiple resources scheduling

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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