An improved NSGA-II algorithm for multi-objective lot-streaming flow shop scheduling problem

An improved NSGA-II algorithm for multi-objective lot-streaming flow shop scheduling problem. Han, Y., Gong, D., Sun, X., & Pan, Q. International Journal of Production Research, 52(8):2211--2231, 2014.

Paper doi abstract bibtex

Crossover and mutation operators in NSGA-II are random and aimless, and encounter difficulties in generating offspring with high quality. Aiming to overcoming these drawbacks, we proposed an improved NSGA-II algorithm (INSGA-II) and applied it to solve the lot-streaming flow shop scheduling problem with four criteria. We first presented four variants of NEH heuristic to generate the initial population, and then incorporated the estimation of distribution algorithm and a mutation operator based on insertion and swap into NSGA-II to replace traditional crossover and mutation operators. Last but not least, we performed a simple and efficient restarting strategy on the population when the diversity of the population is smaller than a given threshold. We conducted a serial of experiments, and the experimental results demonstrate that the proposed algorithm outperforms the comparative algorithms.

@article{ han_improved_2014,
  title = {An improved {NSGA}-{II} algorithm for multi-objective lot-streaming flow shop scheduling problem},
  volume = {52},
  issn = {0020-7543},
  url = {http://www.tandfonline.com/doi/abs/10.1080/00207543.2013.848492},
  doi = {10.1080/00207543.2013.848492},
  abstract = {Crossover and mutation operators in NSGA-II are random and aimless, and encounter difficulties in generating offspring with high quality. Aiming to overcoming these drawbacks, we proposed an improved NSGA-II algorithm (INSGA-II) and applied it to solve the lot-streaming flow shop scheduling problem with four criteria. We first presented four variants of NEH heuristic to generate the initial population, and then incorporated the estimation of distribution algorithm and a mutation operator based on insertion and swap into NSGA-II to replace traditional crossover and mutation operators. Last but not least, we performed a simple and efficient restarting strategy on the population when the diversity of the population is smaller than a given threshold. We conducted a serial of experiments, and the experimental results demonstrate that the proposed algorithm outperforms the comparative algorithms.},
  number = {8},
  urldate = {2014-03-19TZ},
  journal = {International Journal of Production Research},
  author = {Han, Yu-Yan and Gong, Dun-wei and Sun, Xiao-Yan and Pan, Quan-Ke},
  year = {2014},
  pages = {2211--2231}
}

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