Integrated production system optimization using global optimization techniques
- Mason, T. L., Emelle, C., Van Berkel, J., Bagirov, Adil, Kampas, F., Pinter, J. D.
- Authors: Mason, T. L. , Emelle, C. , Van Berkel, J. , Bagirov, Adil , Kampas, F. , Pinter, J. D.
- Date: 2007
- Type: Text , Journal article
- Relation: Journal of Industrial and Management Optimization Vol. 3, no. 2 (May 2007), p. 257-277
- Full Text:
- Reviewed:
- Description: Many optimization problems related to integrated oil and gas production systems are nonconvex and multimodal. Additionally, apart from the innate nonsmoothness of many optimization problems, nonsmooth functions such as minimum and maximum functions may be used to model flow/pressure controllers and cascade mass in the gas gathering and blending networks. In this paper we study the application of different versions of the derivative free Discrete Gradient Method (DGM) as well as the Lipschitz Global Optimizer (LGO) suite to production optimization in integrated oil and gas production systems and their comparison with various local and global solvers used with the General Algebraic Modeling System (GAMS). Four nonconvex and nonsmooth test cases were constructed from a small but realistic integrated gas production system optimization problem. The derivation of the system of equations for the various test cases is also presented. Results demonstrate that DGM is especially effective for solving nonsmooth optimization problems and its two versions are capable global optimization algorithms. We also demonstrate that LGO solves successfully the presented test (as well as other related real-world) problems.
- Description: C1
- Description: 2003004725
- Authors: Mason, T. L. , Emelle, C. , Van Berkel, J. , Bagirov, Adil , Kampas, F. , Pinter, J. D.
- Date: 2007
- Type: Text , Journal article
- Relation: Journal of Industrial and Management Optimization Vol. 3, no. 2 (May 2007), p. 257-277
- Full Text:
- Reviewed:
- Description: Many optimization problems related to integrated oil and gas production systems are nonconvex and multimodal. Additionally, apart from the innate nonsmoothness of many optimization problems, nonsmooth functions such as minimum and maximum functions may be used to model flow/pressure controllers and cascade mass in the gas gathering and blending networks. In this paper we study the application of different versions of the derivative free Discrete Gradient Method (DGM) as well as the Lipschitz Global Optimizer (LGO) suite to production optimization in integrated oil and gas production systems and their comparison with various local and global solvers used with the General Algebraic Modeling System (GAMS). Four nonconvex and nonsmooth test cases were constructed from a small but realistic integrated gas production system optimization problem. The derivation of the system of equations for the various test cases is also presented. Results demonstrate that DGM is especially effective for solving nonsmooth optimization problems and its two versions are capable global optimization algorithms. We also demonstrate that LGO solves successfully the presented test (as well as other related real-world) problems.
- Description: C1
- Description: 2003004725
Improving risk grouping rules for prostate cancer patients with optimization
- Churilov, Leonid, Bagirov, Adil, Schwartz, Daniel, Smith, Kate, Dally, Michael
- Authors: Churilov, Leonid , Bagirov, Adil , Schwartz, Daniel , Smith, Kate , Dally, Michael
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at the 37th Annual Hawaii International Conference on System Sciences, Big Island, Hawaii : 5th January, 2004
- Full Text:
- Reviewed:
- Description: Data mining techniques provide a popular and powerful toolset to address both clinical and management issues in the area of health care. This paper describes the study of assigning prostate cancer patients into homogenous groups with the aim to support future clinical treatment decisions. The cluster analysis based model is suggested and an application of non-smooth non-convex optimization techniques to solve this model is discussed. It is demonstrated that using the optimization based approach to data mining of a prostate cancer patients database can lead to generation of a significant amount of new knowledge that can be effectively utilized to enhance clinical decision making.
- Description: E1
- Description: 2003000846
- Authors: Churilov, Leonid , Bagirov, Adil , Schwartz, Daniel , Smith, Kate , Dally, Michael
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at the 37th Annual Hawaii International Conference on System Sciences, Big Island, Hawaii : 5th January, 2004
- Full Text:
- Reviewed:
- Description: Data mining techniques provide a popular and powerful toolset to address both clinical and management issues in the area of health care. This paper describes the study of assigning prostate cancer patients into homogenous groups with the aim to support future clinical treatment decisions. The cluster analysis based model is suggested and an application of non-smooth non-convex optimization techniques to solve this model is discussed. It is demonstrated that using the optimization based approach to data mining of a prostate cancer patients database can lead to generation of a significant amount of new knowledge that can be effectively utilized to enhance clinical decision making.
- Description: E1
- Description: 2003000846
Optimization based clustering algorithms in multicast group hierarchies
- Jia, Long, Ouveysi, Iradj, Rubinov, Alex, Bagirov, Adil
- Authors: Jia, Long , Ouveysi, Iradj , Rubinov, Alex , Bagirov, Adil
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at the 2003 Australian Telecommunications Networks and Applications Conference, Melbourne : 8th - 10th December, 2003
- Full Text:
- Reviewed:
- Description: In this paper we propose the use of optimization based clustering algorithms to determine hierarchical multicast trees. This problem is formulated as an optimization problem with a non-smooth, non-convex objective function. Different algorithms are examined for solving this problem. Results of numerical experiments using some artificial and real-world databases are reported. We compare several optimization based clustering methods and their combinations with the k- means method. The results demonstrate the effectiveness of these algorithms.
- Description: E1
- Description: 2003000382
- Authors: Jia, Long , Ouveysi, Iradj , Rubinov, Alex , Bagirov, Adil
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at the 2003 Australian Telecommunications Networks and Applications Conference, Melbourne : 8th - 10th December, 2003
- Full Text:
- Reviewed:
- Description: In this paper we propose the use of optimization based clustering algorithms to determine hierarchical multicast trees. This problem is formulated as an optimization problem with a non-smooth, non-convex objective function. Different algorithms are examined for solving this problem. Results of numerical experiments using some artificial and real-world databases are reported. We compare several optimization based clustering methods and their combinations with the k- means method. The results demonstrate the effectiveness of these algorithms.
- Description: E1
- Description: 2003000382
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