Multiobjective process planning and scheduling using improved vector evaluated genetic algorithm with archive

Wenqiang Zhang, Shigeru Fujimura

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Multiobjective process planning and scheduling (PPS) is a most important practical but very intractable combinatorial optimization problem in manufacturing systems. Many researchers have used multiobjective evolutionary algorithms (moEAs) to solve such problems; however, these approaches could not achieve satisfactory results in both efficacy (quality, i.e., convergence and distribution) and efficiency (speed). As classical moEAs, nondominated sorting genetic algorithm II (NSGA-II) and SPEA2 can get good efficacy but need much CPU time. Vector evaluated genetic algorithm (VEGA) also cannot be applied owing to its poor efficacy. This paper proposes an improved VEGA with archive (iVEGA-A) to deal with multiobjective PPS problems, with consideration being given to the minimization of both makespan and machine workload variation. The proposed method tactfully combines the mechanism of VEGA with a preference for the edge region of the Pareto front and the characteristics of generalized Pareto-based scale-independent fitness function (gp-siff) with the tendency to converge toward the central area of the Pareto front. These two mechanisms not only preserve the convergence rate but also guarantee better distribution performance. Moreover, some problem-dependent crossover, mutation, and local search methods are used to improve the performance of the algorithm. Complete numerical comparisons show that the iVEGA-A is obviously better than VEGA in efficacy, and the convergence performance is also better than NSGA-II and SPEA2, while the distribution performance is comparable to and the efficiency is obviously better than theirs.

Original languageEnglish
Pages (from-to)258-267
Number of pages10
JournalIEEJ Transactions on Electrical and Electronic Engineering
Volume7
Issue number3
DOIs
Publication statusPublished - 2012 May

Fingerprint

Process planning
Genetic algorithms
Scheduling
Sorting
Evolutionary algorithms
Combinatorial optimization
Program processors

Keywords

  • Archive
  • Genetic algorithm
  • Multiobjective optimization
  • Process planning and scheduling

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

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title = "Multiobjective process planning and scheduling using improved vector evaluated genetic algorithm with archive",
abstract = "Multiobjective process planning and scheduling (PPS) is a most important practical but very intractable combinatorial optimization problem in manufacturing systems. Many researchers have used multiobjective evolutionary algorithms (moEAs) to solve such problems; however, these approaches could not achieve satisfactory results in both efficacy (quality, i.e., convergence and distribution) and efficiency (speed). As classical moEAs, nondominated sorting genetic algorithm II (NSGA-II) and SPEA2 can get good efficacy but need much CPU time. Vector evaluated genetic algorithm (VEGA) also cannot be applied owing to its poor efficacy. This paper proposes an improved VEGA with archive (iVEGA-A) to deal with multiobjective PPS problems, with consideration being given to the minimization of both makespan and machine workload variation. The proposed method tactfully combines the mechanism of VEGA with a preference for the edge region of the Pareto front and the characteristics of generalized Pareto-based scale-independent fitness function (gp-siff) with the tendency to converge toward the central area of the Pareto front. These two mechanisms not only preserve the convergence rate but also guarantee better distribution performance. Moreover, some problem-dependent crossover, mutation, and local search methods are used to improve the performance of the algorithm. Complete numerical comparisons show that the iVEGA-A is obviously better than VEGA in efficacy, and the convergence performance is also better than NSGA-II and SPEA2, while the distribution performance is comparable to and the efficiency is obviously better than theirs.",
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