Discrete optimization via simulation using COMPASS. We propose an optimization-via-simulation algorithm, called COMPASS, for use when the performance measure is estimated via a stochastic, discrete-event simulation, and the decision variables are integer ordered. We prove that COMPASS converges to the set of local optimal solutions with probability 1 for both terminating and steady-state simulation, and for both fully constrained problems and partially constrained or unconstrained problems under mild conditions

References in zbMATH (referenced in 39 articles , 1 standard article )

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  1. Bliek, Laurens; Verwer, Sicco; de Weerdt, Mathijs: Black-box combinatorial optimization using models with integer-valued minima (2021)
  2. Cooper, Kyle; Hunter, Susan R.; Nagaraj, Kalyani: Biobjective simulation optimization on integer lattices using the epsilon-constraint method in a retrospective approximation framework (2020)
  3. He, Xinyu; Reyes, Kristofer G.; Powell, Warren B.: Optimal learning with local nonlinear parametric models over continuous designs (2020)
  4. Hu, Liujia; Andradóttir, Sigrún: An asymptotically optimal set approach for simulation optimization (2019)
  5. Powell, Warren B.: A unified framework for stochastic optimization (2019)
  6. Zhang, Qi; Hu, Jiaqiao: Simulation optimization using multi-time-scale adaptive random search (2019)
  7. Binois, Mickaël; Gramacy, Robert B.; Ludkovski, Mike: Practical heteroscedastic Gaussian process modeling for large simulation experiments (2018)
  8. Fan, Qi; Hu, Jiaqiao: Surrogate-based promising area search for Lipschitz continuous simulation optimization (2018)
  9. Mao, Jianfeng; Cassandras, Christos G.: Solving a class of simulation-based optimization problems using “optimality in probability” (2018)
  10. Fleck, Julia L.; Cassandras, Christos G.: Optimal design of personalized prostate cancer therapy using infinitesimal perturbation analysis (2017)
  11. Horng, Shih-Cheng; Lin, Shieh-Shing: Ordinal optimization based metaheuristic algorithm for optimal inventory policy of assemble-to-order systems (2017)
  12. Wang, Honggang: Multi-objective retrospective optimization using stochastic zigzag search (2017)
  13. Amaran, Satyajith; Sahinidis, Nikolaos V.; Sharda, Bikram; Bury, Scott J.: Simulation optimization: a review of algorithms and applications (2016)
  14. Xie, Jing; Frazier, Peter I.; Chick, Stephen E.: Bayesian optimization via simulation with pairwise sampling and correlated prior beliefs (2016)
  15. Alfieri, Arianna; Matta, Andrea; Pedrielli, Giulia: Mathematical programming models for joint simulation-optimization applied to closed queueing networks (2015)
  16. Berkhout, Joost: An accelerated stopping rule for the nested partition hybrid algorithm for discrete stochastic optimization (2015)
  17. Tsai, Shing Chih; Liu, Chung Hung: A simulation-based decision support system for a multi-echelon inventory problem with service level constraints (2015)
  18. Xia, Li; Jia, Qing-Shan: Parameterized Markov decision process and its application to service rate control (2015)
  19. Amaran, Satyajith; Sahinidis, Nikolaos V.; Sharda, Bikram; Bury, Scott J.: Simulation optimization: a review of algorithms and applications (2014)
  20. Tsai, Shing Chih; Fu, Sheng Yang: Genetic-algorithm-based simulation optimization considering a single stochastic constraint (2014)

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