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A Computational Comparison of Branch and Bound and Outer Approximation Algorithms for 01 Mixed Integer Nonlinear Programs
 Computers and Operations Research
, 1996
"... this paper we compare the performance of two codes for convex 01 mixed integer nonlinear programs on a number of test problems. The first code uses a branch and bound algorithm. The second code is a commercially available implementation of an outer approximation algorithm. The comparison demons ..."
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Cited by 10 (2 self)
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this paper we compare the performance of two codes for convex 01 mixed integer nonlinear programs on a number of test problems. The first code uses a branch and bound algorithm. The second code is a commercially available implementation of an outer approximation algorithm. The comparison demonstrates that both approaches are generally capable of solving the test problems. However, there are significant differences in the robustness of the two codes and their performance on different classes of problems
MixedInteger Nonlinear Optimization in Process Synthesis
, 1998
"... The use of networks allows the representation of a variety of important engineering problems. The treatment of a particular class of network applications, the process synthesis problem, is exposed in this paper. Process Synthesis seeks to develop systematically process flowsheets that convert raw ma ..."
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Cited by 7 (0 self)
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The use of networks allows the representation of a variety of important engineering problems. The treatment of a particular class of network applications, the process synthesis problem, is exposed in this paper. Process Synthesis seeks to develop systematically process flowsheets that convert raw materials into desired products. In recent years, the optimization approach to process synthesis has shown promise in tackling this challenge. It requires the development of a network of interconnected units, the process superstructure, that represents the alternative process flowsheets. The mathematical modeling of the superstructure has a mixed set of binary and continuous variables and results in a mixedinteger optimization model. Due to the nonlinearity of chemical models, these problems are generally classified as MixedInteger Nonlinear Programming (MINLP) problems. A number of local optimization algorithms, developed for the solution of this class of problems, are presented in this pap...
Advances in Mathematical Programming for Automated Design Integration
 KOREAN J. CHEM. ENG
, 1999
"... This paper presents a review of advances that have taken place in the mathematical programming approach to process design and synthesis. A review is first presented on the algorithms that are available for solving MINLP problems, and its most recent variant, Generalized Disjunctive Programming model ..."
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Cited by 5 (3 self)
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This paper presents a review of advances that have taken place in the mathematical programming approach to process design and synthesis. A review is first presented on the algorithms that are available for solving MINLP problems, and its most recent variant, Generalized Disjunctive Programming models. The formulation of superstructures, models and solution strategies is also discussed for the effective solution of the corresponding optimization problems. The rest of the paper is devoted to reviewing recent mathematical programming models for the synthesis of reactor networks, distillation sequences, heat exchanger networks, mass exchanger networks, utility plants, and total flowsheets. As will be seen from this review, the progress that has been achieved in this area over the last decade is very significant.
ADVANCES IN MATHEMATICAL PROGRAMMING FOR THE SYNTHESIS OF PROCESS SYSTEMS
"... This paper presents a review of advances that have taken place in the mathematical programming approach to process design and synthesis. A review is first presented on the algorithms that are available for solving MINLP problems, and its most recent variant, Generalized Disjunctive Programming model ..."
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Cited by 2 (0 self)
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This paper presents a review of advances that have taken place in the mathematical programming approach to process design and synthesis. A review is first presented on the algorithms that are available for solving MINLP problems, and its most recent variant, Generalized Disjunctive Programming models. The formulation of superstructures, models and solution strategies is also discussed for the effective solution of the corresponding optimization problems. The rest of the paper is devoted to reviewing recent mathematical programming models for the synthesis of reactor networks, distillation sequences, heat exchanger networks, mass exchanger networks, utility plants, and total flowsheets. As will be seen from this review, the progress that has been achieved in this area over the last decade is very significant. 1.
SOME MINLP SOLUTION ALGORITHMS
"... Abstract. This document describes several methods to solve MixedInteger Nonlinear Programming (MINLP) problems within a GAMS environment. We demonstrate Generalized Benders Decomposition (GDB), Outer Approximation (OA) and Branchandbound (BB) using algorithms compactly implemented in the GAMS lan ..."
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Cited by 1 (0 self)
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Abstract. This document describes several methods to solve MixedInteger Nonlinear Programming (MINLP) problems within a GAMS environment. We demonstrate Generalized Benders Decomposition (GDB), Outer Approximation (OA) and Branchandbound (BB) using algorithms compactly implemented in the GAMS language.
Nonlinear and MixedInteger Optimization in Chemical Process Network Systems
"... . The use of networks allows the representation of a variety of important engineering problems. The treatment of a particular class of network applications, the Process Synthesis problem, is exposed in this paper. Process Synthesis seeks to develop systematically process flowsheets that convert raw ..."
Abstract
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. The use of networks allows the representation of a variety of important engineering problems. The treatment of a particular class of network applications, the Process Synthesis problem, is exposed in this paper. Process Synthesis seeks to develop systematically process flowsheets that convert raw materials into desired products. In recent years, the optimization approach to process synthesis has shown promise in tackling this challenge. It requires the development of a network of interconnected units, the process superstructure, that represents the alternative process flowsheets. The mathematical modeling of the superstructure has a mixed set of binary and continuous variables and results in a mixedinteger optimization model. Due to the nonlinearity of chemical models, these problems are generally classified as MixedInteger Nonlinear Programming (MINLP) problems. A number of local optimization algorithms for MINLP problems are outlined in this paper: Generalized Benders Decompositi...