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Genetic Algorithms for Improving Material Utilization in Manufacturing

  • Mira Yi
  • Jihyun Hong
  • Taeho Cho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

Abstract

In modern production industries, computer aided systems have been improving the efficiency and convenience of the various stages of work. However, as the complexity of computerized production systems increases, various techniques are still necessary. The problem we addressed occurs in computer systems that automatically make manufacturing process plans in the metal grating manufacturing industry. In the system, the merging of tasks as a work unit is important to reduce the material loss. However, there is no guarantee that merging always reduces the material loss. So, operators must compare the material loss rates of diverse merging cases to find a near-optimal solution that provides a low material loss rate. In this paper, we apply genetic algorithms to search the near-optimal solution of a planning problem focused on the reduction of material loss. In order to reflect the domain dependent characteristics, we apply genetic algorithms in two levels related each other.

Keywords

Genetic Algorithm Material Loss Sample Task Material Demand Schedule Flexible Manufacture System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mira Yi
    • 1
  • Jihyun Hong
    • 2
  • Taeho Cho
    • 2
  1. 1.Division of Marine Electronics & Communication Eng.Mokpo National Maritime UniversityMokpoS. Korea
  2. 2.School of Information & Communication Eng.Sungkyunkwan UniversitySuwonS. Korea

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