제어로봇시스템학회:학술대회논문집

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Development of Real Coded Genetic Algorithm for Multiperiod Optimization

  • Chang, Young-Jung (School of Chemical Engineering, Seoul National University) ;
  • Song, Sang-Ok (School of Chemical Engineering, Seoul National University) ;
  • Song, Ji-Ho (School of Chemical Engineering, Seoul National University) ;
  • Dongil Shin (School of Chemical Engineering, Seoul National University) ;
  • S. Ando (School of Chemical Engineering, Seoul National University)
  • Published : 2000.10.01
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Abstract

Multiperiod optimization is the key step to tackle the supply chain optimization problems. Taking supply and demand uncertainty or prediction into consideration during the process synthesis phase leads to the maximization of the profit for the long range time horizon. In this study, new algorithm based on the Genetic Algorithms is proposed for multiperiod optimization formulated in MINLP, GDP and hybrid MINLP/GDP. In this study, the focus is given especially on the design of the Genetic Algorithm suitable to handle disjunctive programming with the same level of MINLP handling capability. Hybridization with the Simulated Annealing is tried. and many heuristics are adopted for this purpose.

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