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Simulation Optimization: A Review, New Developments, and Applications
 In Proceedings of the 37th Winter Simulation Conference
, 2005
"... ABSTRACT We provide a descriptive review of the main approaches for carrying out simulation optimization, and sample some recent algorithmic and theoretical developments in simulation optimization research. Then we survey some of the software available for simulation languages and spreadsheets, and ..."
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Cited by 54 (5 self)
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ABSTRACT We provide a descriptive review of the main approaches for carrying out simulation optimization, and sample some recent algorithmic and theoretical developments in simulation optimization research. Then we survey some of the software available for simulation languages and spreadsheets, and present several illustrative applications.
Stochastic Gradient Estimation
, 2006
"... We consider the problem of efficiently estimating gradients from stochastic simulation. Although the primary motivation is their use in simulation optimization, the resulting estimators can also be useful in other ways, e.g., sensitivity analysis. The main approaches described are finite differences ..."
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Cited by 39 (6 self)
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We consider the problem of efficiently estimating gradients from stochastic simulation. Although the primary motivation is their use in simulation optimization, the resulting estimators can also be useful in other ways, e.g., sensitivity analysis. The main approaches described are finite differences (including simultaneous perturbations), perturbation analysis, the likelihood ratio/score function method, and the use of weak derivatives.
Provably nearoptimal samplingbased policies for stochastic inventory control models
 Proceedings, 38th Annual ACM Symposium on Theory of Computing
, 2006
"... In this paper, we consider two fundamental inventory models, the singleperiod newsvendor problem and its multiperiod extension, but under the assumption that the explicit demand distributions are not known and that the only information available is a set of independent samples drawn from the true ..."
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Cited by 16 (2 self)
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In this paper, we consider two fundamental inventory models, the singleperiod newsvendor problem and its multiperiod extension, but under the assumption that the explicit demand distributions are not known and that the only information available is a set of independent samples drawn from the true distributions. Under the assumption that the demand distributions are given explicitly, these models are wellstudied and relatively straightforward to solve. However, in most reallife scenarios, the true demand distributions are not available or they are too complex to work with. Thus, a samplingdriven algorithmic framework is very attractive, both in practice and in theory. We shall describe how to compute samplingbased policies, that is, policies that are computed based only on observed samples of the demands without any access to, or assumptions on, the true demand distributions. Moreover, we establish bounds on the number of samples required to guarantee that with high probability, the expected cost of the samplingbased policies is arbitrarily close (i.e., with arbitrarily small relative error) compared to the expected cost of the optimal policies which have full access to the demand distributions. The bounds that we develop are general, easy to compute and do not depend at all on the specific demand distributions.
Probabilistic service level guarantees in maketostock manufacturing systems
 Operations Research
, 2001
"... We consider a model of a multiclass maketostock manufacturing system. External demand for each product class is met from the available finished goods inventory; unsatisfied demand is backlogged. The objective is to devise a production policy that minimizes inventory costs subject to guaranteeing s ..."
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Cited by 13 (1 self)
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We consider a model of a multiclass maketostock manufacturing system. External demand for each product class is met from the available finished goods inventory; unsatisfied demand is backlogged. The objective is to devise a production policy that minimizes inventory costs subject to guaranteeing stockout probabilities to stay bounded above by given constants � j, for each product class j (service level guarantees). Such a policy determines whether the facility should be producing (idling decisions), and if it should, which product class (sequencing decisions). Approximating the original system, we analyze a corresponding fluid model to make sequencing decisions and employ large deviations techniques to make idling ones. We consider both linear and quadratic inventory cost structures to obtain a prioritybased and a generalized longest queue firstbased production policy, respectively. An important feature of our model is that it accommodates autocorrelated demand and service processes, both critical features of modern failureprone manufacturing systems. 1.
Feature article: Optimization for simulation: Theory vs. practice
 INFORMS Journal on Computing
"... Probably one of the most successful interfaces between operations research and computer science has been the development of discreteevent simulation software. The recent integration of optimization techniques into simulation practice, specifically into commercial software, has become nearly ubiquit ..."
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Cited by 11 (2 self)
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Probably one of the most successful interfaces between operations research and computer science has been the development of discreteevent simulation software. The recent integration of optimization techniques into simulation practice, specifically into commercial software, has become nearly ubiquitous, as most discreteevent simulation packages now include some form of “optimization ” routine. The main thesis of this article, however, is that there is a disconnect between research in simulation optimization—which has addressed the stochastic nature of discreteevent simulation by concentratingon theoretical results of convergence and specialized algorithms that are mathematically elegant—and the recent software developments, which implement very general algorithms adopted from techniques in the deterministic optimization metaheuristic literature (e.g., genetic algorithms, tabu search, artificial neural networks). A tutorial exposition that summarizes the approaches found in the research literature is included, as well as a discussion contrastingthese approaches with the algorithms implemented in commercial software. The article concludes with the author’s speculations on promisingresearch areas and possible future directions in practice.
What you should know about simulation and derivatives
 Naval Res. Logist
, 2008
"... Abstract: Derivatives (or gradients) are important for both sensitivity analysis and optimization, and in simulation models, these can often be estimated efficiently using various methods other than bruteforce finite differences. This article briefly summarizes the main approaches and discusses are ..."
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Cited by 10 (4 self)
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Abstract: Derivatives (or gradients) are important for both sensitivity analysis and optimization, and in simulation models, these can often be estimated efficiently using various methods other than bruteforce finite differences. This article briefly summarizes the main approaches and discusses areas in which the approaches can most fruitfully be applied: queueing, inventory, and finance.
Infinitesimal Perturbation Analysis and Optimization for MaketoStock Manufacturing Systems Based on Stochastic Fluid Models. Discrete Event Dynamic System
, 2006
"... In this paper we study MakeToStock manufacturing systems and seek online algorithms for determining optimal or near optimal buffer capacities (hedging points) that balance inventory against stockout costs. Using a Stochastic Fluid Model (SFM), we derive sample derivatives (sensitivities) which, u ..."
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Cited by 3 (0 self)
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In this paper we study MakeToStock manufacturing systems and seek online algorithms for determining optimal or near optimal buffer capacities (hedging points) that balance inventory against stockout costs. Using a Stochastic Fluid Model (SFM), we derive sample derivatives (sensitivities) which, under very weak structural assumptions on the defining demand and service processes, are shown to be unbiased estimators of the sensitivities of a cost function with respect to these capacities. When applied to discretepart systems, we show that these estimators are greatly simplified and become nonparametric. Thus, they can be easily implemented and evaluated on line. Though the implementation on discretepart systems does not necessarily preserve the unbiasedness property, simulation results show that stochastic approximation algorithms that use such estimates do converge to optimal or near optimal hedging points. 1
SIMULATIONBASED METHODS FOR STOCHASTIC CONTROL AND GLOBAL OPTIMIZATION
, 2011
"... Ideas of stochastic control have found applications in a variety of areas. A subclass of the problems with parameterized policies (including some stochastic impulse control problems) has received significant attention recently because of emerging applications in the areas of engineering, management, ..."
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Cited by 2 (0 self)
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Ideas of stochastic control have found applications in a variety of areas. A subclass of the problems with parameterized policies (including some stochastic impulse control problems) has received significant attention recently because of emerging applications in the areas of engineering, management, and mathematical finance. However, explicit solutions for this type of stochastic control problems only exist for some special cases, and effective numerical methods are relatively rare. Deriving efficient stochastic derivative estimators for payoff functions with discontinuities arising in many problems of practical interest is very challenging. Global optimization problems are extremely hard to solve due to the typical multimodal properties of objective functions. With the increasing availability of computing power and memory, there is a rapid development in the merging of simulation and optimization techniques. Developing new and efficient simulationbased optimization algorithms for solving stochastic control and global optimization problems is the primary goal of this thesis. First we develop a new simulationbased optimization algorithm to solve a stochastic control problem with a parameterized policy that arises in the setting of dynamic pricing
Inventory Control for Supply Chains with Service Level Constraints: A Synergy between Large Deviations and Perturbation Analysis
, 2002
"... We consider a model of a supply chain consisting of n production facilities in tandem and producing a single product class. External demand is met from the finished goods inventory maintained in front of the most downstream facility (stage 1); unsatisfied demand is backlogged. We adopt a basestock ..."
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We consider a model of a supply chain consisting of n production facilities in tandem and producing a single product class. External demand is met from the finished goods inventory maintained in front of the most downstream facility (stage 1); unsatisfied demand is backlogged. We adopt a basestock production policy at each stage of the supply chain, according to which the facility at stage i produces if inventory falls below a certain level wi and idles otherwise. We seek to optimize the hedging vector w = (w1,..., wn) to minimize expected inventory costs at all stages subject to maintaining the stockout probability at stage 1 below a prescribed level (service level constraint). We make rather general modeling assumptions on demand and production processes that include autocorrelated stochastic processes. We solve this stochastic optimization problem by combining analytical (large deviations) and sample pathbased (perturbation analysis) techniques. We demonstrate that there is a natural synergy between these two approaches.