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

Shing-Chih Tsai, Sheng Yang Fu

Research output: Contribution to journalArticlepeer-review

16 Citations (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.

Original languageEnglish
Pages (from-to)113-125
Number of pages13
JournalEuropean Journal of Operational Research
Issue number1
Publication statusPublished - 2014 Jul 1

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modelling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management


Dive into the research topics of 'Genetic-algorithm-based simulation optimization considering a single stochastic constraint'. Together they form a unique fingerprint.

Cite this