The Experts below are selected from a list of 13671 Experts worldwide ranked by ideXlab platform
Xiang Li - One of the best experts on this subject based on the ideXlab platform.
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Natural gas Production Network infrastructure development under uncertainty
Optimization and Engineering, 2017Co-Authors: Xiang Li, Asgeir Tomasgard, Paul I. BartonAbstract:Mathematical programming has been widely applied for the planning of natural gas Production infrastructure development. As the Production infrastructure involves large investments and is expected to remain in operation over several decades, the factors that will impact the gas Production but cannot be foreseen before the development of the infrastructure need to be taken into account in the planning. Therefore, two scenario-based two-stage stochastic programming models are developed to facilitate natural gas Production infrastructure development under uncertainty. One is called the stochastic pooling model, which tracks the qualities of gas streams throughout the Production Network via a generalized pooling model. The other is an enhancement of the stochastic pooling model with the consideration of pressure. Either model results in a large-scale nonconvex mixed-integer nonlinear programming (MINLP) problem, for which a global optimal solution, although very important for a problem that involves large investments, is very difficult to obtain. A novel optimization method, called nonconvex generalized Benders decomposition (NGBD), is developed for efficient global optimization of the large-scale nonconvex MINLP. Case studies of a real industrial natural gas Production system show the advantages of the proposed stochastic programming models over deterministic optimization models, as well as the dramatic computational advantages of NGBD over a state-of-the-art global optimization solver.
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A new optimization model and a customized solution method for natural gas Production Network design and operation
AIChE Journal, 2017Co-Authors: Dan Li, Xiang LiAbstract:This article proposes to tackle integrated design and operation of natural gas Production Networks under uncertainty, using a new two-stage stochastic programming model, a novel reformulation strategy, and a customized global optimization method. The new model addresses material balances for multiple key gas components, pressure flow relationships in gas wells and pipelines, and compressor performance. This model is a large-scale nonconvex mixed-integer nonlinear programming problem that cannot be practically solved by existing global optimization solvers or decomposition-based optimization methods. With the new reformulation strategy, the reformulated model has a better decomposable structure, and then a new decomposition-based global optimization method is developed for efficient global optimization. In the case study of an industrial naturals Production system, it is shown that the proposed modeling and optimization methods enable efficient solution, and the proposed optimization method is faster than a state-of-the-art decomposition method by at least an order of magnitude.
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stochastic pooling problem for natural gas Production Network design and operation under uncertainty
Aiche Journal, 2011Co-Authors: Xiang Li, Asgeir Tomasgard, Emre Armagan, Paul I. BartonAbstract:Product quality and uncertainty are two important issues in the design and operation of natural gas Production Networks. This paper presents a stochastic pooling problem optimization formulation to address these two issues, where the qualities of the flows in the system are described with a pooling model and the uncertainty in the system is handled with a multiscenario, two-stage stochastic recourse approach. In addition, multi-objective problems are handled via a hierarchical optimization approach. The advantages of the proposed formulation are demonstrated with case studies involving an example system based on Haverly’s pooling problem and a real industrial system. The stochastic pooling problem is a potentially large-scale nonconvex Mixed-Integer Nonlinear Program (MINLP), and a rigorous decomposition method developed recently is used to solve this problem. A computational study demonstrates the advantage of the decomposition method over a state-of-the-art branchand-reduce global optimizer, BARON. VC 2010 American Institute of Chemical Engineers AIChE J, 00: 000–000, 2010
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Decomposition strategy for natural gas Production Network design under uncertainty
49th IEEE Conference on Decision and Control (CDC), 2010Co-Authors: Xiang Li, Asgeir Tomasgard, Paul I. BartonAbstract:The use of natural gas for power generation has been rising rapidly in the past two decades. To ensure the security of supply of gas to the market and meet strict specifications on gas quality (e.g., sulfur content), natural gas Production Network design must address uncertainty explicitly as well as tracking the quality of each gas flow in the entire system. This leads to the stochastic pooling problem, which is a (potentially large-scale) nonconvex mixed-integer nonlinear program (MINLP). This paper presents a rigorous, duality-based decomposition strategy to solve the stochastic pooling problem, which guarantees finding an ε-optimal solution of the problem with a finite number of iterations. A case study involving a gas Production Network demonstrates the dramatic computational advantages of the decomposition method over a state-of-the-art global optimization method. The proposed method can be extended to tackle more general nonconvex MINLP problems, which may occur in the design of integrated energy systems involving fuel Production, power generation and electricity transmission.
Paul I. Barton - One of the best experts on this subject based on the ideXlab platform.
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Natural gas Production Network infrastructure development under uncertainty
Optimization and Engineering, 2017Co-Authors: Xiang Li, Asgeir Tomasgard, Paul I. BartonAbstract:Mathematical programming has been widely applied for the planning of natural gas Production infrastructure development. As the Production infrastructure involves large investments and is expected to remain in operation over several decades, the factors that will impact the gas Production but cannot be foreseen before the development of the infrastructure need to be taken into account in the planning. Therefore, two scenario-based two-stage stochastic programming models are developed to facilitate natural gas Production infrastructure development under uncertainty. One is called the stochastic pooling model, which tracks the qualities of gas streams throughout the Production Network via a generalized pooling model. The other is an enhancement of the stochastic pooling model with the consideration of pressure. Either model results in a large-scale nonconvex mixed-integer nonlinear programming (MINLP) problem, for which a global optimal solution, although very important for a problem that involves large investments, is very difficult to obtain. A novel optimization method, called nonconvex generalized Benders decomposition (NGBD), is developed for efficient global optimization of the large-scale nonconvex MINLP. Case studies of a real industrial natural gas Production system show the advantages of the proposed stochastic programming models over deterministic optimization models, as well as the dramatic computational advantages of NGBD over a state-of-the-art global optimization solver.
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stochastic pooling problem for natural gas Production Network design and operation under uncertainty
Aiche Journal, 2011Co-Authors: Xiang Li, Asgeir Tomasgard, Emre Armagan, Paul I. BartonAbstract:Product quality and uncertainty are two important issues in the design and operation of natural gas Production Networks. This paper presents a stochastic pooling problem optimization formulation to address these two issues, where the qualities of the flows in the system are described with a pooling model and the uncertainty in the system is handled with a multiscenario, two-stage stochastic recourse approach. In addition, multi-objective problems are handled via a hierarchical optimization approach. The advantages of the proposed formulation are demonstrated with case studies involving an example system based on Haverly’s pooling problem and a real industrial system. The stochastic pooling problem is a potentially large-scale nonconvex Mixed-Integer Nonlinear Program (MINLP), and a rigorous decomposition method developed recently is used to solve this problem. A computational study demonstrates the advantage of the decomposition method over a state-of-the-art branchand-reduce global optimizer, BARON. VC 2010 American Institute of Chemical Engineers AIChE J, 00: 000–000, 2010
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Decomposition strategy for natural gas Production Network design under uncertainty
49th IEEE Conference on Decision and Control (CDC), 2010Co-Authors: Xiang Li, Asgeir Tomasgard, Paul I. BartonAbstract:The use of natural gas for power generation has been rising rapidly in the past two decades. To ensure the security of supply of gas to the market and meet strict specifications on gas quality (e.g., sulfur content), natural gas Production Network design must address uncertainty explicitly as well as tracking the quality of each gas flow in the entire system. This leads to the stochastic pooling problem, which is a (potentially large-scale) nonconvex mixed-integer nonlinear program (MINLP). This paper presents a rigorous, duality-based decomposition strategy to solve the stochastic pooling problem, which guarantees finding an ε-optimal solution of the problem with a finite number of iterations. A case study involving a gas Production Network demonstrates the dramatic computational advantages of the decomposition method over a state-of-the-art global optimization method. The proposed method can be extended to tackle more general nonconvex MINLP problems, which may occur in the design of integrated energy systems involving fuel Production, power generation and electricity transmission.
Hideaki Aoyama - One of the best experts on this subject based on the ideXlab platform.
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hierarchical communities in the walnut structure of the japanese Production Network
PLOS ONE, 2018Co-Authors: Abhijit Chakraborty, Yoshi Fujiwara, Yuichi Kichikawa, Takashi Iino, Hiroshi Iyetomi, Hiroyasu Inoue, Hideaki AoyamaAbstract:This paper studies the structure of the Japanese Production Network, which includes one million firms and five million supplier-customer links. This study finds that this Network forms a tightly-knit structure with a core giant strongly connected component (GSCC) surrounded by IN and OUT components constituting two half-shells of the GSCC, which we call awalnut structure because of its shape. The hierarchical structure of the communities is studied by the Infomap method, and most of the irreducible communities are found to be at the second level. The composition of some of the major communities, including overexpressions regarding their industrial or regional nature, and the connections that exist between the communities are studied in detail. The findings obtained here cause us to question the validity and accuracy of using the conventional input-output analysis, which is expected to be useful when firms in the same sectors are highly connected to each other.
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SITIS - Community Dynamics and Controllability of G7 Global Production Network
2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2015Co-Authors: Yuichi Ikeda, Hideaki Aoyama, Yohei SakamotoAbstract:We study G7 Global Production Network constructedusing Production index time series from January 1998to January 2015 for G7 countries. Collective motion of G7Global Production Network is analyzed using complex Hilbertprincipal component analysis, community analysis for single layerNetwork and multiplex Networks, and structural controllability. Throughtout this analysis we charactrize features of collectivemotion for G7 Global Production Network during economic crisisin 2008.
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Community Dynamics and Controllability of G7 Global Production Network
2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2015Co-Authors: Yuichi Ikeda, Hideaki Aoyama, Yohei SakamotoAbstract:We study G7 Global Production Network constructed using Production index time series from January 1998to January 2015 for G7 countries. Collective motion of G7Global Production Network is analyzed using complex Hilbert principal component analysis, community analysis for single layer Network and multiplex Networks, and structural controllability. Through tout this analysis we characterize features of collective motion for G7 Global Production Network during economic crisis in 2008.
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large scale structure of a nation wide Production Network
European Physical Journal B, 2010Co-Authors: Yoshi Fujiwara, Hideaki AoyamaAbstract:Production in an economy is a set of firms’ activities as suppliers and customers; a firm buys goods from other firms, puts value added and sells products to others in a giant Network of Production. Empirical study is lacking despite the fact that the structure of the Production Network is important to understand and make models for many aspects of dynamics in economy. We study a nation-wide Production Network comprising a million firms and millions of supplier-customer links by using recent statistical methods developed in physics. We show in the empirical analysis scale-free degree distribution, disassortativity, correlation of degree to firm-size, and community structure having sectoral and regional modules. Since suppliers usually provide credit to their customers, who supply it to theirs in turn, each link is actually a creditor-debtor relationship. We also study chains of failures or bankruptcies that take place along those links in the Network, and corresponding avalanche-size distribution. Copyright EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2010
Asgeir Tomasgard - One of the best experts on this subject based on the ideXlab platform.
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Natural gas Production Network infrastructure development under uncertainty
Optimization and Engineering, 2017Co-Authors: Xiang Li, Asgeir Tomasgard, Paul I. BartonAbstract:Mathematical programming has been widely applied for the planning of natural gas Production infrastructure development. As the Production infrastructure involves large investments and is expected to remain in operation over several decades, the factors that will impact the gas Production but cannot be foreseen before the development of the infrastructure need to be taken into account in the planning. Therefore, two scenario-based two-stage stochastic programming models are developed to facilitate natural gas Production infrastructure development under uncertainty. One is called the stochastic pooling model, which tracks the qualities of gas streams throughout the Production Network via a generalized pooling model. The other is an enhancement of the stochastic pooling model with the consideration of pressure. Either model results in a large-scale nonconvex mixed-integer nonlinear programming (MINLP) problem, for which a global optimal solution, although very important for a problem that involves large investments, is very difficult to obtain. A novel optimization method, called nonconvex generalized Benders decomposition (NGBD), is developed for efficient global optimization of the large-scale nonconvex MINLP. Case studies of a real industrial natural gas Production system show the advantages of the proposed stochastic programming models over deterministic optimization models, as well as the dramatic computational advantages of NGBD over a state-of-the-art global optimization solver.
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stochastic pooling problem for natural gas Production Network design and operation under uncertainty
Aiche Journal, 2011Co-Authors: Xiang Li, Asgeir Tomasgard, Emre Armagan, Paul I. BartonAbstract:Product quality and uncertainty are two important issues in the design and operation of natural gas Production Networks. This paper presents a stochastic pooling problem optimization formulation to address these two issues, where the qualities of the flows in the system are described with a pooling model and the uncertainty in the system is handled with a multiscenario, two-stage stochastic recourse approach. In addition, multi-objective problems are handled via a hierarchical optimization approach. The advantages of the proposed formulation are demonstrated with case studies involving an example system based on Haverly’s pooling problem and a real industrial system. The stochastic pooling problem is a potentially large-scale nonconvex Mixed-Integer Nonlinear Program (MINLP), and a rigorous decomposition method developed recently is used to solve this problem. A computational study demonstrates the advantage of the decomposition method over a state-of-the-art branchand-reduce global optimizer, BARON. VC 2010 American Institute of Chemical Engineers AIChE J, 00: 000–000, 2010
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Decomposition strategy for natural gas Production Network design under uncertainty
49th IEEE Conference on Decision and Control (CDC), 2010Co-Authors: Xiang Li, Asgeir Tomasgard, Paul I. BartonAbstract:The use of natural gas for power generation has been rising rapidly in the past two decades. To ensure the security of supply of gas to the market and meet strict specifications on gas quality (e.g., sulfur content), natural gas Production Network design must address uncertainty explicitly as well as tracking the quality of each gas flow in the entire system. This leads to the stochastic pooling problem, which is a (potentially large-scale) nonconvex mixed-integer nonlinear program (MINLP). This paper presents a rigorous, duality-based decomposition strategy to solve the stochastic pooling problem, which guarantees finding an ε-optimal solution of the problem with a finite number of iterations. A case study involving a gas Production Network demonstrates the dramatic computational advantages of the decomposition method over a state-of-the-art global optimization method. The proposed method can be extended to tackle more general nonconvex MINLP problems, which may occur in the design of integrated energy systems involving fuel Production, power generation and electricity transmission.
Yohei Sakamoto - One of the best experts on this subject based on the ideXlab platform.
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SITIS - Community Dynamics and Controllability of G7 Global Production Network
2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2015Co-Authors: Yuichi Ikeda, Hideaki Aoyama, Yohei SakamotoAbstract:We study G7 Global Production Network constructedusing Production index time series from January 1998to January 2015 for G7 countries. Collective motion of G7Global Production Network is analyzed using complex Hilbertprincipal component analysis, community analysis for single layerNetwork and multiplex Networks, and structural controllability. Throughtout this analysis we charactrize features of collectivemotion for G7 Global Production Network during economic crisisin 2008.
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Community Dynamics and Controllability of G7 Global Production Network
2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2015Co-Authors: Yuichi Ikeda, Hideaki Aoyama, Yohei SakamotoAbstract:We study G7 Global Production Network constructed using Production index time series from January 1998to January 2015 for G7 countries. Collective motion of G7Global Production Network is analyzed using complex Hilbert principal component analysis, community analysis for single layer Network and multiplex Networks, and structural controllability. Through tout this analysis we characterize features of collective motion for G7 Global Production Network during economic crisis in 2008.
