A multicut generalized benders decomposition approach for the integration of process operations and dynamic optimization for continuous systems

Ilias Mitrai; Prodromos Daoutidis

doi:10.1016/j.compchemeng.2022.107859

A multicut generalized benders decomposition approach for the integration of process operations and dynamic optimization for continuous systems

Ilias Mitrai, Prodromos Daoutidis

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

The integration of process operations and dynamic optimization leads to large scale optimization problems whose monolithic solution is challenging. In this paper we propose a new formulation of the integrated planning, scheduling, and dynamic optimization problem for continuous single stage systems. We analyze the structure of the problem using Stochastic Blockmodeling and we show that the estimated structure can be used as the basis for a multicut Generalized Benders decomposition (GBD) algorithm, which can solve the problem in reduced computational time. Furthermore, we propose an accelerated hybrid multicut algorithm which can lead to further reduction in computational time. Through case studies, we analyze the computational performance of the proposed formulation and decomposition based solution algorithms.

Original language	English (US)
Article number	107859
Journal	Computers and Chemical Engineering
Volume	164
DOIs	https://doi.org/10.1016/j.compchemeng.2022.107859
State	Published - Aug 2022
Externally published	Yes

Bibliographical note

Funding Information:
We would like to thank Professor Qi Zhang (Department of Chemical Engineering and Materials Science, University of Minnesota) for his insightful comments and suggestions. We also would like to acknowledge financial support from NSF-CBET (award number 1926303).

Funding Information:
We would like to thank Professor Qi Zhang (Department of Chemical Engineering and Materials Science, University of Minnesota) for his insightful comments and suggestions. We also would like to acknowledge financial support from NSF-CBET (award number 1926303).

Publisher Copyright:
© 2022 Elsevier Ltd

Keywords

Decomposition based solution algorithm
Dynamic optimization
Integration of process operations

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title = "A multicut generalized benders decomposition approach for the integration of process operations and dynamic optimization for continuous systems",

abstract = "The integration of process operations and dynamic optimization leads to large scale optimization problems whose monolithic solution is challenging. In this paper we propose a new formulation of the integrated planning, scheduling, and dynamic optimization problem for continuous single stage systems. We analyze the structure of the problem using Stochastic Blockmodeling and we show that the estimated structure can be used as the basis for a multicut Generalized Benders decomposition (GBD) algorithm, which can solve the problem in reduced computational time. Furthermore, we propose an accelerated hybrid multicut algorithm which can lead to further reduction in computational time. Through case studies, we analyze the computational performance of the proposed formulation and decomposition based solution algorithms.",

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author = "Ilias Mitrai and Prodromos Daoutidis",

note = "Funding Information: We would like to thank Professor Qi Zhang (Department of Chemical Engineering and Materials Science, University of Minnesota) for his insightful comments and suggestions. We also would like to acknowledge financial support from NSF-CBET (award number 1926303). Funding Information: We would like to thank Professor Qi Zhang (Department of Chemical Engineering and Materials Science, University of Minnesota) for his insightful comments and suggestions. We also would like to acknowledge financial support from NSF-CBET (award number 1926303). Publisher Copyright: {\textcopyright} 2022 Elsevier Ltd",

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AU - Daoutidis, Prodromos

N1 - Funding Information: We would like to thank Professor Qi Zhang (Department of Chemical Engineering and Materials Science, University of Minnesota) for his insightful comments and suggestions. We also would like to acknowledge financial support from NSF-CBET (award number 1926303). Funding Information: We would like to thank Professor Qi Zhang (Department of Chemical Engineering and Materials Science, University of Minnesota) for his insightful comments and suggestions. We also would like to acknowledge financial support from NSF-CBET (award number 1926303). Publisher Copyright: © 2022 Elsevier Ltd

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AB - The integration of process operations and dynamic optimization leads to large scale optimization problems whose monolithic solution is challenging. In this paper we propose a new formulation of the integrated planning, scheduling, and dynamic optimization problem for continuous single stage systems. We analyze the structure of the problem using Stochastic Blockmodeling and we show that the estimated structure can be used as the basis for a multicut Generalized Benders decomposition (GBD) algorithm, which can solve the problem in reduced computational time. Furthermore, we propose an accelerated hybrid multicut algorithm which can lead to further reduction in computational time. Through case studies, we analyze the computational performance of the proposed formulation and decomposition based solution algorithms.

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A multicut generalized benders decomposition approach for the integration of process operations and dynamic optimization for continuous systems

Abstract

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Keywords

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