The Experts below are selected from a list of 212391 Experts worldwide ranked by ideXlab platform
Michel Gendreau - One of the best experts on this subject based on the ideXlab platform.
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closed loop supply chain network design under uncertain quality status case of durable products
International Journal of Production Economics, 2017Co-Authors: M Jeihoonian, Masoumeh Kazemi Zanjani, Michel GendreauAbstract:Abstract This paper proposes a two-stage Stochastic mixed-integer programming model for a closed-loop supply chain network design Problem in the context of modular structured products in which the reverse network entails several types of recovery options. It accounts for uncertainty in the quality status of the return stream, modeled as binary scenarios for each component in the reverse bill of material corresponding to such products. To deal with the intractable number of scenarios in the proposed model, a scenario reduction scheme is adapted to the Problem of interest to preserve the most pertinent scenarios based on a modified Euclidean distance measure. The reduced Stochastic large-scale optimization Problem is then solved via a L-shaped algorithm enhanced with surrogate constraints and Pareto-optimal cuts. Numerical results indicate that the scenario reduction algorithm provides good quality solutions to the Stochastic Problem in a reasonable amount of time through applying the enhanced L-shaped method.
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progressive hedging based metaheuristics for Stochastic network design
Networks, 2011Co-Authors: Teodor Gabriel Crainic, Michel Gendreau, Xiaorui Fu, Stein W WallaceAbstract:We consider the Stochastic fixed-charge capacitated multicommodity network design (S-CMND) Problem with uncertain demand. We propose a two-stage Stochastic programming formulation, where design decisions make up the first stage, while recourse decisions are made in the second stage to distribute the commodities according to observed demands. The overall objective is to optimize the cost of the first-stage design decisions plus the total expected distribution cost incurred in the second stage. To solve this formulation, we propose a metaheuristic framework inspired by the progressive hedging algorithm of Rockafellar and Wets. Following this strategy, scenario decomposition is used to separate the Stochastic Problem following the possible outcomes, scenarios, of the random event. Each scenario subProblem then becomes a deterministic CMND Problem to be solved, which may be addressed by efficient specialized methods. We also propose and compare different strategies to gradually guide scenario subProblems to agree on the status of design arcs and aim for a good global design. These strategies are embedded into a parallel solution method, which is numerically shown to be computationally efficient and to yield high-quality solutions under various Problem characteristics and demand correlations. © 2011 Wiley Periodicals, Inc. NETWORKS, 2011. © 2011 Wiley Periodicals, Inc.
M Jeihoonian - One of the best experts on this subject based on the ideXlab platform.
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closed loop supply chain network design under uncertain quality status case of durable products
International Journal of Production Economics, 2017Co-Authors: M Jeihoonian, Masoumeh Kazemi Zanjani, Michel GendreauAbstract:Abstract This paper proposes a two-stage Stochastic mixed-integer programming model for a closed-loop supply chain network design Problem in the context of modular structured products in which the reverse network entails several types of recovery options. It accounts for uncertainty in the quality status of the return stream, modeled as binary scenarios for each component in the reverse bill of material corresponding to such products. To deal with the intractable number of scenarios in the proposed model, a scenario reduction scheme is adapted to the Problem of interest to preserve the most pertinent scenarios based on a modified Euclidean distance measure. The reduced Stochastic large-scale optimization Problem is then solved via a L-shaped algorithm enhanced with surrogate constraints and Pareto-optimal cuts. Numerical results indicate that the scenario reduction algorithm provides good quality solutions to the Stochastic Problem in a reasonable amount of time through applying the enhanced L-shaped method.
Michael J Neely - One of the best experts on this subject based on the ideXlab platform.
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delay reduction via lagrange multipliers in Stochastic network optimization
IEEE Transactions on Automatic Control, 2011Co-Authors: Longbo Huang, Michael J NeelyAbstract:In this paper, we consider the Problem of reducing network delay in Stochastic network utility optimization Problems. We start by studying the recently proposed quadratic Lyapunov function based algorithms (QLA, also known as the MaxWeight algorithm). We show that for every Stochastic Problem, there is a corresponding deterministic Problem, whose dual optimal solution “exponentially attracts” the network backlog process under QLA. In particular, the probability that the backlog vector under QLA deviates from the attractor is exponentially decreasing in their Euclidean distance. This is the first such result for the class of algorithms built upon quadratic Lyapunov functions. The result quantifies the “network gravity” role of Lagrange Multipliers in network scheduling. It not only helps to explain how QLA achieves the desired performance but also suggests that one can roughly “subtract out” a Lagrange multiplier from the system induced by QLA.
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delay reduction via lagrange multipliers in Stochastic network optimization
Modeling and Optimization in Mobile Ad-Hoc and Wireless Networks, 2009Co-Authors: Longbo Huang, Michael J NeelyAbstract:In this paper, we consider the Problem of reducing network delay in Stochastic network utility optimization Problems. We start by studying the recently proposed quadratic Lyapunov function based algorithms (QLA). We show that for every Stochastic Problem, there is a corresponding deterministic Problem, whose dual optimal solution “exponentially attracts” the network backlog process under QLA. In particular, the probability that the backlog vector under QLA deviates from the attractor is exponentially decreasing in their Euclidean distance. This suggests that one can roughly “subtract out” a Lagrange multiplier from the system induced by QLA. We thus develop a family of Fast Quadratic Lyapunov based Algorithms (FQLA) that achieve an [O(1/V ),O(log2(V ))] performance-delay tradeoff. These results highlight the “network gravity” role of Lagrange Multipliers in network scheduling. This role can be viewed as the counterpart of the “shadow price” role of Lagrange Multipliers in flow regulation for classic flow-based network Problems.
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delay reduction via lagrange multipliers in Stochastic network optimization
arXiv: Optimization and Control, 2009Co-Authors: Longbo Huang, Michael J NeelyAbstract:In this paper, we consider the Problem of reducing network delay in Stochastic network utility optimization Problems. We start by studying the recently proposed quadratic Lyapunov function based algorithms (QLA). We show that for every Stochastic Problem, there is a corresponding \emph{deterministic} Problem, whose dual optimal solution "exponentially attracts" the network backlog process under QLA. In particular, the probability that the backlog vector under QLA deviates from the attractor is exponentially decreasing in their Euclidean distance. This not only helps to explain how QLA achieves the desired performance but also suggests that one can roughly "subtract out" a Lagrange multiplier from the system induced by QLA. We thus develop a family of \emph{Fast Quadratic Lyapunov based Algorithms} (FQLA) that achieve an $[O(1/V), O(\log^2(V))]$ performance-delay tradeoff for Problems with a discrete set of action options, and achieve a square-root tradeoff for continuous Problems. This is similar to the optimal performance-delay tradeoffs achieved in prior work by Neely (2007) via drift-steering methods, and shows that QLA algorithms can also be used to approach such performance. These results highlight the "network gravity" role of Lagrange Multipliers in network scheduling. This role can be viewed as the counterpart of the "shadow price" role of Lagrange Multipliers in flow regulation for classic flow-based network Problems.
Nathaniel K Newlands - One of the best experts on this subject based on the ideXlab platform.
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a quantile based scenario analysis approach to biomass supply chain optimization under uncertainty
Computers & Chemical Engineering, 2017Co-Authors: David S Zamar, Bhushan R Gopaluni, Shahab Sokhansanj, Nathaniel K NewlandsAbstract:Abstract Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. Biomass supply chain design and operational planning models are often formulated and studied using deterministic mathematical models. While these models are beneficial for making decisions, their applicability to real world Problems may be limited because they do not capture all the complexities in the supply chain, including uncertainties in the parameters. This paper develops a statistically robust quantile-based approach for Stochastic optimization under uncertainty, which builds upon scenario analysis. We apply and evaluate the performance of our approach to address the Problem of analyzing competing biomass supply chains subject to Stochastic demand and supply. The proposed approach was found to outperform alternative methods in terms of computational efficiency and ability to meet the Stochastic Problem requirements.
Ian R Petersen - One of the best experts on this subject based on the ideXlab platform.
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finite horizon h infty control for a class of linear quantum measurement delayed systems a dynamic game approach
Siam Journal on Control and Optimization, 2014Co-Authors: Aline I Maalouf, Ian R PetersenAbstract:In this paper, a finite horizon $H^\infty$ control Problem is solved for a class of linear quantum systems using a dynamic game approach for the case of delayed measurements. The methodology adopted involves an equivalence between the quantum Problem and an auxiliary classical Stochastic Problem. Then, the finite horizon $H^\infty$ control Problem for the class of linear quantum systems under consideration is solved for the case of delayed measurements by solving the finite horizon $H^\infty$ control Problem for an equivalent Stochastic $H^\infty$ control Problem following a dynamic game approach.
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finite horizon h control for a class of linear quantum measurement delayed systems a dynamic game approach
American Control Conference, 2011Co-Authors: Aline I Maalouf, Ian R PetersenAbstract:In this paper, a finite horizon H∞ control Problem is solved for a class of linear quantum systems using a dynamic game approach for the case of delayed measurements. The methodology adopted involves an equivalence between the quantum Problem and an auxiliary classical Stochastic Problem. Then, the finite horizon H∞ control Problem for the class of linear quantum systems under consideration is solved for the case of delayed measurements by solving the finite horizon H∞ control Problem for an equivalent Stochastic H∞ control Problem using some results from a corresponding deterministic Problem following a dynamic game approach.
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finite horizon h control for a class of linear quantum sampled data measurement systems a dynamic game approach
IFAC Proceedings Volumes, 2011Co-Authors: Aline I Maalouf, Ian R PetersenAbstract:Abstract In this paper, the finite horizon H∞ control Problem is solved for a class of linear quantum systems using a dynamic game approach for the case of sampled-data measurements. The methodology adopted involves a certain equivalence between the quantum Problem and an auxiliary classical Stochastic Problem. Then, by solving the finite horizon H∞ control Problem for the equivalent Stochastic Problem using some results from a corresponding deterministic Problem following a dynamic game approach, the finite horizon H∞ control Problem for the class of linear quantum systems under consideration is solved for the case of sampled-data measurements.
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finite horizon h control for a class of linear quantum systems a dynamic game approach
Advances in Computing and Communications, 2010Co-Authors: Aline I Maalouf, Ian R PetersenAbstract:In this paper, the finite horizon H∞ control Problem is solved for a class of linear quantum systems using a dynamic game approach. The methodology adopted involves an equivalence between the quantum Problem and an auxiliary classical Stochastic Problem. Then, by solving the finite horizon H∞ control Problem for the equivalent Stochastic Problem using results from a corresponding deterministic Problem following a dynamic game approach, the finite horizon H∞ control Problem for the class of linear quantum systems under consideration is solved.
