Simulation optimization: a tutorial overview and recent developments in gradient-based methods
We provide a tutorial overview of simulation optimization methods, including statistical
ranking & selection (R&S) methods such as indifference-zone procedures, optimal …
ranking & selection (R&S) methods such as indifference-zone procedures, optimal …
Data-driven sample average approximation with covariate information
We study optimization for data-driven decision making when we have observations of the
uncertain parameters within an optimization model together with concurrent observations of …
uncertain parameters within an optimization model together with concurrent observations of …
Time-coupled day-ahead wind power scenario generation: A combined regular vine copula and variance reduction method
Advanced stochastic programming-based power system operations planning requires wind
power forecast in the form of scenarios. Generating wind power scenarios reflecting the …
power forecast in the form of scenarios. Generating wind power scenarios reflecting the …
An introduction to multiobjective simulation optimization
The multiobjective simulation optimization (MOSO) problem is a nonlinear multiobjective
optimization problem in which multiple simultaneous and conflicting objective functions can …
optimization problem in which multiple simultaneous and conflicting objective functions can …
Outpatient clinic scheduling with limited waiting area capacity
This paper proposes an iterative simulation optimisation approach to maximise the number
of in-person consultations in the blueprint schedule of a clinic facing same-day multi …
of in-person consultations in the blueprint schedule of a clinic facing same-day multi …
SCORE allocations for bi-objective ranking and selection
G Feldman, SR Hunter - ACM Transactions on Modeling and Computer …, 2018 - dl.acm.org
The bi-objective ranking and selection (R8S) problem is a special case of the multi-objective
simulation optimization problem in which two conflicting objectives are known only through …
simulation optimization problem in which two conflicting objectives are known only through …
Sample average approximation with heavier tails i: non-asymptotic bounds with weak assumptions and stochastic constraints
We derive new and improved non-asymptotic deviation inequalities for the sample average
approximation (SAA) of an optimization problem. Our results give strong error probability …
approximation (SAA) of an optimization problem. Our results give strong error probability …
An Efficient Data-Driven Conditional Joint Wind Power Scenario Generation for Day-Ahead Power System Operations Planning
Advanced stochastic programming-based operations planning (OP) requires wind power
forecasts in the form of scenarios. The quality of the decisions made under uncertainty is …
forecasts in the form of scenarios. The quality of the decisions made under uncertainty is …
Acceleration on adaptive importance sampling with sample average approximation
R Kawai - SIAM Journal on Scientific Computing, 2017 - SIAM
We construct and analyze acceleration techniques for adaptive Monte Carlo simulations for
general multivariate probability laws when the sample average approximation is employed …
general multivariate probability laws when the sample average approximation is employed …
A Reliability Theory of Compromise Decisions for Large-Scale Stochastic Programs
Stochastic programming models can lead to very large-scale optimization problems for
which it may be impossible to enumerate all possible scenarios. In such cases, one adopts a …
which it may be impossible to enumerate all possible scenarios. In such cases, one adopts a …