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Simulation optimization refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation-discrete o...
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Simulation optimization refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation-discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise-various algorithms have been proposed in the literature. As one can imagine, there exist several competing algorithms for each of these classes of problems. This document emphasizes the difficulties in simulation optimization as compared to algebraic model-based mathematical programming makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the field.
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Simulation optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation-discr...
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Simulation optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation-discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise-various algorithms have been proposed in the literature. As one can imagine, there exist several competing algorithms for each of these classes of problems. This document emphasizes the difficulties in SO as compared to algebraic model-based mathematical programming, makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the field.
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In this paper, we present a simulated annealing algorithm for solving multi-objective simulation optimization problems. The algorithm is based on the idea of simulated annealing with constant temperature, and uses a rule for accep...
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In this paper, we present a simulated annealing algorithm for solving multi-objective simulation optimization problems. The algorithm is based on the idea of simulated annealing with constant temperature, and uses a rule for accepting a candidate solution that depends on the individual estimated objective function values. The algorithm is shown to converge almost surely to an optimal solution. It is applied to a multi-objective inventory problem; the numerical results show that the algorithm converges rapidly.
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Simulation optimization is increasingly popular for solving complicated and mathematically intractable business problems. Focusing on academic articles published between 1998 and 2013, the present survey aims to unveil the extent ...
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Simulation optimization is increasingly popular for solving complicated and mathematically intractable business problems. Focusing on academic articles published between 1998 and 2013, the present survey aims to unveil the extent to which simulation optimization has been used to solve practical inventory problems (as opposed to small, theoretical "toy problem"), and to detect any trends that might have arisen (e.g., popular topics, effective simulation optimization methods, frequently studied inventory system structures). We find that metaheuristics (especially genetic algorithms) and methods that combine several simulation optimization techniques are the most popular. The resulting categorizations provide a useful overview for researchers studying complex inventory management problems, by providing detailed information on the inventory system characteristics and the employed simulation optimization techniques, highlighting articles that involve stochastic constraints (e.g., expected fill rate constraints) or that employ a robust simulation optimization approach. Finally, in highlighting both trends and gaps in the research field, this review suggests avenues for further research.
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We present a modification of the simulated annealing algorithm designed for solving discrete stochastic optimization problems. Like the original simulated annealing algorithm, our method has the hill climbing feature, so it can fi...
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We present a modification of the simulated annealing algorithm designed for solving discrete stochastic optimization problems. Like the original simulated annealing algorithm, our method has the hill climbing feature, so it can find global optimal solutions to discrete stochastic optimization problems with many local solutions. However, our method differs from the original simulated annealing algorithm in that it uses a constant (rather than decreasing) temperature. We consider two approaches for estimating the optimal solution. The first approach uses the number of visits the algorithm makes to the different states (divided by a normalizer) to estimate the optimal solution. The second approach uses the state that has the best average estimated objective function value as estimate of the optimal solution. We show that both variants of our method are guaranteed to converge almost surely to the set of global optimal solutions, and discuss how our work applies in the discrete deterministic optimization setting. We also show how both variants can be applied for solving discrete optimization problems when the objective function values are estimated using either transient or steady-state simulation. Finally, we include some encouraging numerical results documenting the behavior of the two variants of our algorithm when applied for solving two versions of a particular discrete stochastic optimization problem, and compare their performance with that of other variants of the simulated annealing algorithm designed for solving discrete stochastic optimization problems.
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This paper presents generalized process simulation and optimization strategies to predict and improve the performance of three-axis milling operations. Cutter-part engagement conditions are extracted from a solid modeling system, ...
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This paper presents generalized process simulation and optimization strategies to predict and improve the performance of three-axis milling operations. Cutter-part engagement conditions are extracted from a solid modeling system, which can handle free form part surfaces found in dies and molds. The cutting force distribution along the engaged cutting edge-part surface is evaluated based on the laws of mechanics of milling. By integrating the distributed force along the cutting edge, total forces, torque and power are either predicted analytically using closed-form solutions, or numerically if the cutting tool shape is discontinuous. Simulation results are then used in a constraint-based optimization scheme to maximize the material removal rate (MRR) by calculating acceptable feedrate levels. The proposed virtual milling system is demonstrated experimentally in milling a stamping die with free form surfaces.
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Simulation can be a very powerful tool to help decision making in many applications; however, exploring multiple courses of actions can be time consuming. Numerous Ranking & Selection (R&S) procedures have been developed to enhance the simulation efficiency of finding the best design. This article explores the potential of further enhancing R&S efficiency by incorporating simulation information from across the domain into a regression metamodel. This article assumes that the underlying function to be optimized is one-dimensional as well as approximately quadratic or piecewise quadratic. Under some common conditions in most regression-based approaches, the proposed method provides approximations of the optimal rules that determine the design locations to conduct simulation runs and the number of samples allocated to each design location. Numerical experiments demonstrate that the proposed approach can dramatically enhance efficiency over existing efficient R&S methods and can obtain significant savings over regression-based methods. In addition to utilizing concepts from the Design Of Experiments (DOE) literature, it introduces the probability of correct selection optimality criterion that underpins our new R&S method to the DOE literature....
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Simulation can be a very powerful tool to help decision making in many applications; however, exploring multiple courses of actions can be time consuming. Numerous Ranking & Selection (R&S) procedures have been developed to enhance the simulation efficiency of finding the best design. This article explores the potential of further enhancing R&S efficiency by incorporating simulation information from across the domain into a regression metamodel. This article assumes that the underlying function to be optimized is one-dimensional as well as approximately quadratic or piecewise quadratic. Under some common conditions in most regression-based approaches, the proposed method provides approximations of the optimal rules that determine the design locations to conduct simulation runs and the number of samples allocated to each design location. Numerical experiments demonstrate that the proposed approach can dramatically enhance efficiency over existing efficient R&S methods and can obtain significant savings over regression-based methods. In addition to utilizing concepts from the Design Of Experiments (DOE) literature, it introduces the probability of correct selection optimality criterion that underpins our new R&S method to the DOE literature.
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Purpose Continuous process improvement is a hard problem, especially in high variety/low volume environments due to the complex interrelationships between processes. The purpose of this paper is to address the process improvement ...
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Purpose Continuous process improvement is a hard problem, especially in high variety/low volume environments due to the complex interrelationships between processes. The purpose of this paper is to address the process improvement issues by simultaneously investigating the job sequencing and buffer size optimization problems. Design/methodology/approach This paper proposes a continuous process improvement implementation framework using a modified genetic algorithm (GA) and discrete event simulation to achieve multi-objective optimization. The proposed combinatorial optimization module combines the problem of job sequencing and buffer size optimization under a generic process improvement framework, where lead time and total inventory holding cost are used as two combinatorial optimization objectives. The proposed approach uses the discrete event simulation to mimic the manufacturing environment, the constraints imposed by the real environment and the different levels of variability associated with the resources. Findings Compared to existing evolutionary algorithm-based methods, the proposed framework considers the interrelationship between succeeding and preceding processes and the variability induced by both job sequence and buffer size problems on each other. A computational analysis shows significant improvement by applying the proposed framework. Originality/value Significant body of work exists in the area of continuous process improvement, discrete event simulation and GAs, a little work has been found where GAs and discrete event simulation are used together to implement continuous process improvement as an iterative approach. Also, a modified GA simultaneously addresses the job sequencing and buffer size optimization problems by considering the interrelationships and the effect of variability due to both on each other.
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This review discusses some issues related to the use of simulation in transportation analysis. Potential pitfalls are identified and discussed. An overview of some methods relevant to the use of an advanced simulation tool in an o...
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This review discusses some issues related to the use of simulation in transportation analysis. Potential pitfalls are identified and discussed. An overview of some methods relevant to the use of an advanced simulation tool in an optimization context is also provided. (C) 2015 Elsevier Ltd. All rights reserved.
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We propose a framework for targeting and selection (T&S), a new problem class in simulation optimization where the objective is to select a simulation alternative whose mean performance matches a prespecified target as closely as possible. T&S resembles the more well-known problem of ranking and selection but presents unexpected challenges: for example, a one-step look-ahead method may produce statistically inconsistent estimates of the values, even under very standard normality assumptions. We create a new and fundamentally different approach, based on a Brownian local time model, that exhibits characteristics of two widely studied methodologies, namely expected value of information and optimal computing budget allocation. We characterize the asymptotic sampling rates of this method and relate them to the convergence rates of metrics of interest. The local time method outperforms benchmarks in experiments, including problems where the modeling assumptions of T&S are violated....
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We propose a framework for targeting and selection (T&S), a new problem class in simulation optimization where the objective is to select a simulation alternative whose mean performance matches a prespecified target as closely as possible. T&S resembles the more well-known problem of ranking and selection but presents unexpected challenges: for example, a one-step look-ahead method may produce statistically inconsistent estimates of the values, even under very standard normality assumptions. We create a new and fundamentally different approach, based on a Brownian local time model, that exhibits characteristics of two widely studied methodologies, namely expected value of information and optimal computing budget allocation. We characterize the asymptotic sampling rates of this method and relate them to the convergence rates of metrics of interest. The local time method outperforms benchmarks in experiments, including problems where the modeling assumptions of T&S are violated.
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