Adaptive genetic algorithm for advanced planning in manufacturing supply chain

Chiung Moon, Yoon Ho Seo, Youngsu Yun, Mitsuo Gen

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

A main function for supporting global objectives in a manufacturing supply chain is planning and scheduling. This is considered such an important function because it is involved in the assignment of factory resources to production tasks. In this paper, an advanced planning model that simultaneously decides process plans and schedules was proposed for the manufacturing supply chain (MSC). The model was formulated with mixed integer programming, which considered alternative resources and sequences, a sequence-dependent setup and transportation times.The objective of the model was to analyze alternative resources and sequences to determine the schedules and operation sequences that minimize makespan. A new adaptive genetic algorithm approach was developed to solve the model. Numerical experiments were carried out to demonstrate the efficiency of the developed approach.

Original languageEnglish
Pages (from-to)509-522
Number of pages14
JournalJournal of Intelligent Manufacturing
Volume17
Issue number4
DOIs
Publication statusPublished - 2006 Aug 1

Fingerprint

Adaptive algorithms
Supply chains
Genetic algorithms
Planning
Integer programming
Industrial plants
Scheduling
Experiments

Keywords

  • Adaptive genetic algorithm
  • Advanced planning
  • Manufacturing supply chain
  • Scheduling

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

Cite this

Adaptive genetic algorithm for advanced planning in manufacturing supply chain. / Moon, Chiung; Seo, Yoon Ho; Yun, Youngsu; Gen, Mitsuo.

In: Journal of Intelligent Manufacturing, Vol. 17, No. 4, 01.08.2006, p. 509-522.

Research output: Contribution to journalArticle

Moon, Chiung ; Seo, Yoon Ho ; Yun, Youngsu ; Gen, Mitsuo. / Adaptive genetic algorithm for advanced planning in manufacturing supply chain. In: Journal of Intelligent Manufacturing. 2006 ; Vol. 17, No. 4. pp. 509-522.
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