Markov network based multi-objective EDA and its application for resource constrained project scheduling

Jing Tian, Xinchang Hao, Tomohiro Murata

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This paper presents a Markov network based multi-objective estimation distribution of algorithm (MMEDA) to solve the resource constrained scheduling problem (RCSP), which hybrid a constraint handling by Markov network based EDA and multi-objective optimization by enforced EDA. Firstly, in order to increase the searching performance while keeping the diversity of Pareto solutions, two kinds of fitness assignment functions are integrated within a novel paradigm. Secondly, Markov network, as an undirected graph model, is adopted to model interrelation between variables with constraints. Thirdly, an enforced EDA with mutation operation is proposed to handle the scheduling. Fourthly, a problem-specific local search for RCSP is applied to improve searching performance. Experiments are conducted on multi-mode resource constrained scheduling problem (MRCPSP) which is an extended RCSP including multi-mode resource constraints. The results of the proposed method highly outperformed conventional meta-heuristic based scheduling methods.

Original languageEnglish
Pages (from-to)290-298
Number of pages9
JournalIEEJ Transactions on Electronics, Information and Systems
Volume136
Issue number3
DOIs
Publication statusPublished - 2016

Keywords

  • Estimation distribution of algorithm
  • Markov network
  • Multi-objective optimization
  • Project scheduling
  • Resource constrained scheduling problem

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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