Guidelines for reporting results of computational experiments. Report of the ad hoc committee
Mathematical Programming: Series A and B
On Reporting Computational Experiments with Mathematical Software
ACM Transactions on Mathematical Software (TOMS)
Integrating optimization and simulation: research and practice
Proceedings of the 32nd conference on Winter simulation
Additional Perspectives on Simulation for Optimization
INFORMS Journal on Computing
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
Monte carlo computation of conditional expectation quantiles
Monte carlo computation of conditional expectation quantiles
Journal of Global Optimization
Monte Carlo bounding techniques for determining solution quality in stochastic programs
Operations Research Letters
Allocation of simulation runs for simulation optimization
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Extension of the direct optimization algorithm for noisy functions
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Retrospective-approximation algorithms for the multidimensional stochastic root-finding problem
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Some topics for simulation optimization
Proceedings of the 40th Conference on Winter Simulation
Proceedings of the Winter Simulation Conference
SimOpt: a library of simulation optimization problems
Proceedings of the Winter Simulation Conference
Adaptive probabilistic branch and bound for level set approximation
Proceedings of the Winter Simulation Conference
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets
INFORMS Journal on Computing
Multidimensional stochastic approximation: Adaptive algorithms and applications
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on simulation in complex service systems
| Hi-index | 0.00 |
We propose a testbed of simulation-optimization problems. The purpose of the testbed is to encourage development and constructive comparison of simulation-optimization techniques and algorithms. We are particularly interested in increasing attention to the finite-time performance of algorithms, rather than the asymptotic results that one often finds in the literature.