The Experts below are selected from a list of 26832 Experts worldwide ranked by ideXlab platform
Yankai Chen - One of the best experts on this subject based on the ideXlab platform.
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multi objective Simulation based evolutionary algorithm for an aircraft spare parts allocation problem
European Journal of Operational Research, 2008Co-Authors: Ek Peng Chew, Suyan Teng, Yankai ChenAbstract:Simulation optimization has received considerable attention from both Simulation researchers and practitioners. In this study, we develop a solution framework which integrates multi-objective evolutionary algorithm (MOEA) with multi-objective computing Budget allocation (MOCBA) method for the multi-objective Simulation optimization problem. We apply it on a multi-objective aircraft spare parts allocation problem to find a set of non-dominated solutions. The problem has three features: huge search space, multi-objective, and high variability. To address these difficulties, the solution framework employs Simulation to estimate the performance, MOEA to search for the more promising designs, and MOCBA algorithm to identify the non-dominated designs and efficiently allocate the Simulation Budget. Some computational experiments are carried out to test the effectiveness and performance of the proposed solution framework.
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multi objective Simulation based evolutionary algorithm for an aircraft spare parts allocation problem
European Journal of Operational Research, 2008Co-Authors: Loo Hay Lee, Suyan Teng, Ek Peng Chew, Yankai ChenAbstract:Simulation optimization has received considerable attention from both Simulation researchers and practitioners. In this study, we develop a solution framework which integrates multi-objective evolutionary algorithm (MOEA) with multi-objective computing Budget allocation (MOCBA) method for the multi-objective Simulation optimization problem. We apply it on a multi-objective aircraft spare parts allocation problem to find a set of non-dominated solutions. The problem has three features: huge search space, multi-objective, and high variability. To address these difficulties, the solution framework employs Simulation to estimate the performance, MOEA to search for the more promising designs, and MOCBA algorithm to identify the non-dominated designs and efficiently allocate the Simulation Budget. Some computational experiments are carried out to test the effectiveness and performance of the proposed solution framework.
Ek Peng Chew - One of the best experts on this subject based on the ideXlab platform.
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a Simulation Budget allocation procedure for enhancing the efficiency of optimal subset selection
IEEE Transactions on Automatic Control, 2016Co-Authors: Si Zhang, Ek Peng Chew, Loo Hay Lee, Chunhung ChenAbstract:Selecting the optimal subset is highly beneficial to numerous developments in Simulation optimization. This paper studies the problem of maximizing the probability of correctly selecting the top- $m$ designs out of $k$ designs under a computing Budget constraint. We develop a new procedure which is more efficient and robust than currently existing procedures in the literature. We also provide an analysis on its asymptotic convergence rate. Based on this analysis, we show that our new procedure achieves a higher convergence rate than other procedures under certain conditions. Numerical testing supports our analytical analysis and shows that the new procedure is significantly more efficient and robust.
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finding the non dominated pareto set for multi objective Simulation models
Iie Transactions, 2010Co-Authors: Ek Peng Chew, Suyan Teng, David GoldsmanAbstract:This article considers a multi-objective Ranking and Selection (R+S) problem, where the system designs are evaluated in terms of more than one performance measure. The concept of Pareto optimality is incorporated into the R+S scheme, and attempts are made to find all of the non-dominated designs rather than a single “best” one. In addition to a performance index to measure how non-dominated a design is, two types of errors are defined to measure the probabilities that designs in the true Pareto/non-Pareto sets are dominated/non-dominated based on observed performance. Asymptotic allocation rules are derived for Simulation replications based on a Lagrangian relaxation method, under the assumption that an arbitrarily large Simulation Budget is available. Finally, a simple sequential procedure is proposed to allocate the Simulation replications based on the asymptotic allocation rules. Computational results show that the proposed solution framework is efficient when compared to several other algorithms in te...
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multi objective Simulation based evolutionary algorithm for an aircraft spare parts allocation problem
European Journal of Operational Research, 2008Co-Authors: Ek Peng Chew, Suyan Teng, Yankai ChenAbstract:Simulation optimization has received considerable attention from both Simulation researchers and practitioners. In this study, we develop a solution framework which integrates multi-objective evolutionary algorithm (MOEA) with multi-objective computing Budget allocation (MOCBA) method for the multi-objective Simulation optimization problem. We apply it on a multi-objective aircraft spare parts allocation problem to find a set of non-dominated solutions. The problem has three features: huge search space, multi-objective, and high variability. To address these difficulties, the solution framework employs Simulation to estimate the performance, MOEA to search for the more promising designs, and MOCBA algorithm to identify the non-dominated designs and efficiently allocate the Simulation Budget. Some computational experiments are carried out to test the effectiveness and performance of the proposed solution framework.
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multi objective Simulation based evolutionary algorithm for an aircraft spare parts allocation problem
European Journal of Operational Research, 2008Co-Authors: Loo Hay Lee, Suyan Teng, Ek Peng Chew, Yankai ChenAbstract:Simulation optimization has received considerable attention from both Simulation researchers and practitioners. In this study, we develop a solution framework which integrates multi-objective evolutionary algorithm (MOEA) with multi-objective computing Budget allocation (MOCBA) method for the multi-objective Simulation optimization problem. We apply it on a multi-objective aircraft spare parts allocation problem to find a set of non-dominated solutions. The problem has three features: huge search space, multi-objective, and high variability. To address these difficulties, the solution framework employs Simulation to estimate the performance, MOEA to search for the more promising designs, and MOCBA algorithm to identify the non-dominated designs and efficiently allocate the Simulation Budget. Some computational experiments are carried out to test the effectiveness and performance of the proposed solution framework.
Loo Hay Lee - One of the best experts on this subject based on the ideXlab platform.
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a Simulation Budget allocation procedure for enhancing the efficiency of optimal subset selection
IEEE Transactions on Automatic Control, 2016Co-Authors: Si Zhang, Ek Peng Chew, Loo Hay Lee, Chunhung ChenAbstract:Selecting the optimal subset is highly beneficial to numerous developments in Simulation optimization. This paper studies the problem of maximizing the probability of correctly selecting the top- $m$ designs out of $k$ designs under a computing Budget constraint. We develop a new procedure which is more efficient and robust than currently existing procedures in the literature. We also provide an analysis on its asymptotic convergence rate. Based on this analysis, we show that our new procedure achieves a higher convergence rate than other procedures under certain conditions. Numerical testing supports our analytical analysis and shows that the new procedure is significantly more efficient and robust.
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minimizing opportunity cost in selecting the best feasible design
Winter Simulation Conference, 2013Co-Authors: Nugroho A Pujowidianto, Loo Hay Lee, Chunhung ChenAbstract:Constrained ranking and selection (R&S) refers to the problem of selecting the best feasible design where both main objective and constraint measures need to be estimated via stochastic Simulation. Despite the growing interests in constrained R&S, none has considered other selection qualities than a statistical measure called the probability of correct selection (PCS). In contrast, several new developments in other R&S literatures have considered financial significance as the selection quality. This paper aims to lay the foundation of using other selection qualities by attempting to minimize the opportunity cost in allocating the limited Simulation Budget. The opportunity cost is defined and two allocation rules which minimize its upper bound are presented together with a fully-sequential heuristic algorithm for implementation.
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approximate Simulation Budget allocation for selecting the best design in the presence of stochastic constraints
IEEE Transactions on Automatic Control, 2012Co-Authors: Loo Hay Lee, Chunhung Chen, Nugroho A Pujowidianto, Chee Meng YapAbstract:We develop a new Optimal Computing Budget Allocation (OCBA) approach for the ranking and selection problem with stochastic constraints. The goal is to maximize the probability of correctly selecting the best feasible design within a fixed Simulation Budget. Based on some approximations, we derive an asymptotic closed-form allocation rule which is easy to compute and implement and can help provide more insights about the allocation. The numerical testing shows that our approach can enhance the Simulation efficiency.
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multi objective Simulation based evolutionary algorithm for an aircraft spare parts allocation problem
European Journal of Operational Research, 2008Co-Authors: Loo Hay Lee, Suyan Teng, Ek Peng Chew, Yankai ChenAbstract:Simulation optimization has received considerable attention from both Simulation researchers and practitioners. In this study, we develop a solution framework which integrates multi-objective evolutionary algorithm (MOEA) with multi-objective computing Budget allocation (MOCBA) method for the multi-objective Simulation optimization problem. We apply it on a multi-objective aircraft spare parts allocation problem to find a set of non-dominated solutions. The problem has three features: huge search space, multi-objective, and high variability. To address these difficulties, the solution framework employs Simulation to estimate the performance, MOEA to search for the more promising designs, and MOCBA algorithm to identify the non-dominated designs and efficiently allocate the Simulation Budget. Some computational experiments are carried out to test the effectiveness and performance of the proposed solution framework.
Suyan Teng - One of the best experts on this subject based on the ideXlab platform.
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finding the non dominated pareto set for multi objective Simulation models
Iie Transactions, 2010Co-Authors: Ek Peng Chew, Suyan Teng, David GoldsmanAbstract:This article considers a multi-objective Ranking and Selection (R+S) problem, where the system designs are evaluated in terms of more than one performance measure. The concept of Pareto optimality is incorporated into the R+S scheme, and attempts are made to find all of the non-dominated designs rather than a single “best” one. In addition to a performance index to measure how non-dominated a design is, two types of errors are defined to measure the probabilities that designs in the true Pareto/non-Pareto sets are dominated/non-dominated based on observed performance. Asymptotic allocation rules are derived for Simulation replications based on a Lagrangian relaxation method, under the assumption that an arbitrarily large Simulation Budget is available. Finally, a simple sequential procedure is proposed to allocate the Simulation replications based on the asymptotic allocation rules. Computational results show that the proposed solution framework is efficient when compared to several other algorithms in te...
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multi objective Simulation based evolutionary algorithm for an aircraft spare parts allocation problem
European Journal of Operational Research, 2008Co-Authors: Ek Peng Chew, Suyan Teng, Yankai ChenAbstract:Simulation optimization has received considerable attention from both Simulation researchers and practitioners. In this study, we develop a solution framework which integrates multi-objective evolutionary algorithm (MOEA) with multi-objective computing Budget allocation (MOCBA) method for the multi-objective Simulation optimization problem. We apply it on a multi-objective aircraft spare parts allocation problem to find a set of non-dominated solutions. The problem has three features: huge search space, multi-objective, and high variability. To address these difficulties, the solution framework employs Simulation to estimate the performance, MOEA to search for the more promising designs, and MOCBA algorithm to identify the non-dominated designs and efficiently allocate the Simulation Budget. Some computational experiments are carried out to test the effectiveness and performance of the proposed solution framework.
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multi objective Simulation based evolutionary algorithm for an aircraft spare parts allocation problem
European Journal of Operational Research, 2008Co-Authors: Loo Hay Lee, Suyan Teng, Ek Peng Chew, Yankai ChenAbstract:Simulation optimization has received considerable attention from both Simulation researchers and practitioners. In this study, we develop a solution framework which integrates multi-objective evolutionary algorithm (MOEA) with multi-objective computing Budget allocation (MOCBA) method for the multi-objective Simulation optimization problem. We apply it on a multi-objective aircraft spare parts allocation problem to find a set of non-dominated solutions. The problem has three features: huge search space, multi-objective, and high variability. To address these difficulties, the solution framework employs Simulation to estimate the performance, MOEA to search for the more promising designs, and MOCBA algorithm to identify the non-dominated designs and efficiently allocate the Simulation Budget. Some computational experiments are carried out to test the effectiveness and performance of the proposed solution framework.
Chunhung Chen - One of the best experts on this subject based on the ideXlab platform.
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optimal computing Budget allocation for stochastic n k problem in the power grid system
IEEE Transactions on Reliability, 2019Co-Authors: Giulia Pedrielli, Haobin Li, Chunhung Chen, John F ShortleAbstract:The $N$ − $k$ problem is very well known in the power industry and it tries to answer the question whether there exists a set of $k$ lines in a power network with $N$ elements whose removal would cause the failure of the system. In practice, it is common to evaluate a system according to an $N$ −1 criterion, i.e., $k = 1$ . While this problem has traditionally been considered in a deterministic setting, stochastic behavior within the system is important especially in the context of extreme events. A number of stochastic Monte Carlo models have been proposed to estimate the probability of cascading failures. In this paper, we deal with Simulation Budget allocation of the stochastic $N$ –1 problem. More specifically, we assume that a Simulation model is able to provide us an estimate of the system failure rate when any line is tripped. It is not difficult to see how Simulation of all configurations to some certain accuracy can become computationally expensive with the growth of $N$ . Under such a setting, we transform the $N$ –1 problem into a stochastic selection process with optimal computing Budget allocation (OCBA): given $N$ configurations, we would like to sequentially allocate a certain number of Simulation replications in order to answer the question whether the system is reliable or not. We show through theoretical analysis and numerical experiments that the probability of correctly identifying the system reliability state can be increased by applying OCBA allocation rules in the Simulation Budget allocation process.
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a Simulation Budget allocation procedure for enhancing the efficiency of optimal subset selection
IEEE Transactions on Automatic Control, 2016Co-Authors: Si Zhang, Ek Peng Chew, Loo Hay Lee, Chunhung ChenAbstract:Selecting the optimal subset is highly beneficial to numerous developments in Simulation optimization. This paper studies the problem of maximizing the probability of correctly selecting the top- $m$ designs out of $k$ designs under a computing Budget constraint. We develop a new procedure which is more efficient and robust than currently existing procedures in the literature. We also provide an analysis on its asymptotic convergence rate. Based on this analysis, we show that our new procedure achieves a higher convergence rate than other procedures under certain conditions. Numerical testing supports our analytical analysis and shows that the new procedure is significantly more efficient and robust.
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minimizing opportunity cost in selecting the best feasible design
Winter Simulation Conference, 2013Co-Authors: Nugroho A Pujowidianto, Loo Hay Lee, Chunhung ChenAbstract:Constrained ranking and selection (R&S) refers to the problem of selecting the best feasible design where both main objective and constraint measures need to be estimated via stochastic Simulation. Despite the growing interests in constrained R&S, none has considered other selection qualities than a statistical measure called the probability of correct selection (PCS). In contrast, several new developments in other R&S literatures have considered financial significance as the selection quality. This paper aims to lay the foundation of using other selection qualities by attempting to minimize the opportunity cost in allocating the limited Simulation Budget. The opportunity cost is defined and two allocation rules which minimize its upper bound are presented together with a fully-sequential heuristic algorithm for implementation.
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approximate Simulation Budget allocation for selecting the best design in the presence of stochastic constraints
IEEE Transactions on Automatic Control, 2012Co-Authors: Loo Hay Lee, Chunhung Chen, Nugroho A Pujowidianto, Chee Meng YapAbstract:We develop a new Optimal Computing Budget Allocation (OCBA) approach for the ranking and selection problem with stochastic constraints. The goal is to maximize the probability of correctly selecting the best feasible design within a fixed Simulation Budget. Based on some approximations, we derive an asymptotic closed-form allocation rule which is easy to compute and implement and can help provide more insights about the allocation. The numerical testing shows that our approach can enhance the Simulation efficiency.