Genetic algorithm with critical path improvement strategy for solving project scheduling problem with multiple modes

I. Okada, X. F. Zhang, H. Y. Yang, W. Q. Zhang, S. Fujimura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this study, the resource-constrained project scheduling problem with multiple modes (rc-PSP/mM) minimizing the makespan as objective is treated, where an activity has different execution modes characterized by different duration and resource requirement combinations. To solve this NP-hard optimization problem, a novel activity list-based genetic algorithm is proposed. It includes the local search procedure using critical path for reduction of makespan of the schedule in rc-PSP/mM. In order to evaluate the performance, the proposed approach is implemented on some standard instances as the computational experiment and the results are compared with the several competitive heuristics in the literature.

Original languageEnglish
Title of host publication21st International Conference on Production Research
Subtitle of host publicationInnovation in Product and Production, ICPR 2011 - Conference Proceedings
EditorsTobias Krause, Dieter Spath, Rolf Ilg
PublisherFraunhofer-Verlag
ISBN (Electronic)9783839602935
Publication statusPublished - 2011 Jan 1
Event21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Stuttgart, Germany
Duration: 2011 Jul 312011 Aug 4

Publication series

Name21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Conference Proceedings

Other

Other21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011
Country/TerritoryGermany
CityStuttgart
Period11/7/3111/8/4

Keywords

  • Activity list-based genetic algorithm
  • Critical path improvement procedure
  • Resource-constrained project scheduling problem with multiple modes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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