Fast and effective evolutionary algorithm for multiobjective process planning and scheduling problem

Wenqiang Zhang, Xin Wei, Shigeru Fujimura

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

Abstract

Multiobjective process planning and scheduling (moPPS) is a most important, practical but very intractable problem in manufacturing systems. Many research works use multiobjective EA to solve such problems; however, they cannot achieve satisfactory results in both quality and speed. This paper proposes a fast and effective evolutionary algorithm (FEEA) to deal with moPPS problem. FEEA tactfully unites the advantages of vector evaluated genetic algorithm (VEGA) and Pareto dominating and dominated relationship-based (PDDR) fitness function. VEGA prefers the edge region of the Pareto front and PDDR has the tendency converging toward the central area of the Pareto front. These two mechanisms preserve both the convergence rate and the distribution performance. Numerical comparisons show that the convergence performance of FEEA is better than NSGA-II and SPEA2 while the distribution performance is slightly better or equivalent, and the efficiency is obviously better than they are.

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
CountryGermany
CityStuttgart
Period11/7/3111/8/4

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Keywords

  • Evolutionary algorithm
  • Multiobjective optimization
  • Process planning and scheduling

ASJC Scopus subject areas

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

Cite this

Zhang, W., Wei, X., & Fujimura, S. (2011). Fast and effective evolutionary algorithm for multiobjective process planning and scheduling problem. In T. Krause, D. Spath, & R. Ilg (Eds.), 21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Conference Proceedings (21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Conference Proceedings). Fraunhofer-Verlag.