Robust optimization method based on hybridization of GA and MMEDA for resource constraint project scheduling with uncertainty

Jing Tian, Xinchang Hao, Tomohiro Murata

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Inspired by the cooperative co-evolutionary paradigm, this paper presents a two-stage algorithm hybrid genetic algorithm (GA) and multi-objective Markov network based EDA (MMEDA), to solve the robust scheduling problem for resource constrained scheduling problem (RCSP) with uncertainty. Within the two-stage architecture based on sequential co-evolutionary paradigm, GA is used to find feasible solution for sequencing sub-problem in the first stage, and in the second stage, MMEDA is adopted to model the interrelation for resource allocation and calculate the Pareto set with the scenario based approach. Moreover, one problem-specific local search with considering both makespan and robustness is designed to increase the solution quality. Experiment results based on a benchmark (PSPLIB) and comparisons demonstrate that our approach is highly effective and tolerant of uncertainty.

Original languageEnglish
Pages (from-to)957-966
Number of pages10
JournalIEEJ Transactions on Electronics, Information and Systems
Volume137
Issue number7
DOIs
Publication statusPublished - 2017

Keywords

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

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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