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- L. Jeff Hong, Barry L. Nelson
- Operations Research
- 2006

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… (More)

- L. Jeff Hong, Guangwu Liu
- Management Science
- 2009

C value at risk (CVaR) is both a coherent risk measure and a natural risk statistic. It is often used to measure the risk associated with large losses. In this paper, we study how to estimate the sensitivities of CVaR using Monte Carlo simulation. We first prove that the CVaR sensitivity can be written as a conditional expectation for general loss… (More)

- Zhaolin Hu, L. Jeff Hong
- 2012

In this paper we study distributionally robust optimization (DRO) problems where the ambiguity set of the probability distribution is defined by the Kullback-Leibler (KL) divergence. We consider DRO problems where the ambiguity is in the objective function, which takes a form of an expectation, and show that the resulted minimax DRO problems can be… (More)

- L. Jeff Hong, Yi Yang, Liwei Zhang
- Operations Research
- 2011

When there is parameter uncertainty in the constraints of a convex optimization problem, it is natural to formulate the problem as a joint chance constrained program (JCCP) which requires that all constraints be satisfied simultaneously with a given large probability. In this paper, we propose to solve the JCCP by a sequence of convex approximations. We… (More)

- Jie Xu, Barry L. Nelson, L. Jeff Hong
- ACM Trans. Model. Comput. Simul.
- 2010

Industrial Strength COMPASS (ISC) is a particular implementation of a general framework for optimizing the expected value of a performance measure of a stochastic simulation with respect to integer-ordered decision variables in a finite (but typically large) feasible region defined by linear-integer constraints. The framework consists of a global-search… (More)

- L. Jeff Hong, Barry L. Nelson
- ACM Trans. Model. Comput. Simul.
- 2007

The goal of this article is to provide a general framework for locally convergent random-search algorithms for stochastic optimization problems when the objective function is embedded in a stochastic simulation and the decision variables are integer ordered. The framework guarantees desirable asymptotic properties, including almost-sure convergence and… (More)

- L. Jeff Hong
- Operations Research
- 2009

Quantiles of a random performance serve as important alternatives to the usual expected value. They are used in the financial industry as measures of risk and in the service industry as measures of service quality. To manage the quantile of a performance, we need to know how changes in the input parameters affect the output quantiles, which are called… (More)

Statistical Ranking and Selection (R&S) is a collection of experiment design and analysis techniques for selecting the “population” with the largest or smallest mean performance from among a finite set of alternatives. R&S procedures have received considerable research attention in the stochastic simulation community, and they have been incorporated in… (More)

- L. Jeff Hong, Barry L. Nelson
- Proceedings of the 2009 Winter Simulation…
- 2009

Optimization via simulation (OvS) is an exciting and fast developing area for both research and practice. In this article, we introduce three types of OvS problems: the R&S problems, the continuous OvS problems and the discrete OvS problems, and discuss the issues and current research development for these problems. We also give some suggestions on how… (More)

- Kuo-Hao Chang, L. Jeff Hong, Hong Wan
- INFORMS Journal on Computing
- 2013

R surface methodology (RSM) is a widely used method for simulation optimization. Its strategy is to explore small subregions of the decision space in succession instead of attempting to explore the entire decision space in a single attempt. This method is especially suitable for complex stochastic systems where little knowledge is available. Although RSM is… (More)