Genetic-algorithm-based simulation optimization considering a single stochastic constraint

Shing-Chih Tsai, Sheng Yang Fu

研究成果: Article同行評審

17 引文 斯高帕斯(Scopus)


In this paper, we consider the discrete optimization via simulation problem with a single stochastic constraint. We present two genetic-algorithm-based algorithms that adopt different sampling rules and searching mechanisms, and thus deliver different statistical guarantees. The first algorithm offers global convergence as the simulation effort goes to infinity. However, the algorithm's finite-time efficiency may be sacrificed to maintain this theoretically appealing property. We therefore propose the second heuristic algorithm that can take advantage of the desirable mechanics of genetic algorithm, and might be better able to find near-optimal solutions in a reasonable amount of time. Empirical studies are performed to compare the efficiency of the proposed algorithms with other existing ones.

頁(從 - 到)113-125
期刊European Journal of Operational Research
出版狀態Published - 2014 七月 1

All Science Journal Classification (ASJC) codes

  • 電腦科學(全部)
  • 建模與模擬
  • 管理科學與經營研究
  • 資訊系統與管理


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