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Sequential Process Optimisation Using Genetic Algorithms

  • Victor Oduguwa
  • Ashutosh Tiwari
  • Rajkumar Roy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3242)

Abstract

Locating good design solutions within a sequential process environment is necessary to improve the quality and overall productivity of the processes. Multi-objective, multi-stage sequential process design is a complex problem involving large number of design variables and sequential relationship between any two stages. The aim of this paper is to propose a novel framework to handle real-life sequential process optimisation problems using a Genetic Algorithm (GA) based technique. The research validates the proposed GA based framework using a real-life case study of optimising the multi-pass rolling system design. The framework identifies a number of near optimal designs of the rolling system.

Keywords

Genetic Algorithm Design Variable Pareto Front Optimal Pareto Front Roll Force 
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 2004

Authors and Affiliations

  • Victor Oduguwa
    • 1
  • Ashutosh Tiwari
    • 1
  • Rajkumar Roy
    • 1
  1. 1.Enterprise Integration, School of Industrial and Manufacturing ScienceCranfield UniversityCranfield, BedfordUK

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