Uncertainty-aware Integration of Control with Process Operations and Multi-parametric Programming Under Global Uncertainty

Uncertainty-aware Integration of Control with Process Operations and Multi-parametric Programming Under Global Uncertainty

Author: Vassilis M. Charitopoulos

Publisher: Springer Nature

Published: 2020-02-05

Total Pages: 285

ISBN-13: 3030381374

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This book introduces models and methodologies that can be employed towards making the Industry 4.0 vision a reality within the process industries, and at the same time investigates the impact of uncertainties in such highly integrated settings. Advances in computing power along with the widespread availability of data have led process industries to consider a new paradigm for automated and more efficient operations. The book presents a theoretically proven optimal solution to multi-parametric linear and mixed-integer linear programs and efficient solutions to problems such as process scheduling and design under global uncertainty. It also proposes a systematic framework for the uncertainty-aware integration of planning, scheduling and control, based on the judicious coupling of reactive and proactive methods. Using these developments, the book demonstrates how the integration of different decision-making layers and their simultaneous optimisation can enhance industrial process operations and their economic resilience in the face of uncertainty.


Book Synopsis Uncertainty-aware Integration of Control with Process Operations and Multi-parametric Programming Under Global Uncertainty by : Vassilis M. Charitopoulos

Download or read book Uncertainty-aware Integration of Control with Process Operations and Multi-parametric Programming Under Global Uncertainty written by Vassilis M. Charitopoulos and published by Springer Nature. This book was released on 2020-02-05 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces models and methodologies that can be employed towards making the Industry 4.0 vision a reality within the process industries, and at the same time investigates the impact of uncertainties in such highly integrated settings. Advances in computing power along with the widespread availability of data have led process industries to consider a new paradigm for automated and more efficient operations. The book presents a theoretically proven optimal solution to multi-parametric linear and mixed-integer linear programs and efficient solutions to problems such as process scheduling and design under global uncertainty. It also proposes a systematic framework for the uncertainty-aware integration of planning, scheduling and control, based on the judicious coupling of reactive and proactive methods. Using these developments, the book demonstrates how the integration of different decision-making layers and their simultaneous optimisation can enhance industrial process operations and their economic resilience in the face of uncertainty.

Uncertainty-aware Integration of Control with Process Operations and Multi-parametric Programming Under Global Uncertainty

Uncertainty-aware Integration of Control with Process Operations and Multi-parametric Programming Under Global Uncertainty

Author: Vasileios Charitopoulos

Publisher:

Published: 2018

Total Pages: 0

ISBN-13:

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Book Synopsis Uncertainty-aware Integration of Control with Process Operations and Multi-parametric Programming Under Global Uncertainty by : Vasileios Charitopoulos

Download or read book Uncertainty-aware Integration of Control with Process Operations and Multi-parametric Programming Under Global Uncertainty written by Vasileios Charitopoulos and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

13th International Symposium on Process SystemsEngineering – PSE 2018, July 1-5 2018

13th International Symposium on Process SystemsEngineering – PSE 2018, July 1-5 2018

Author: Mario R. Eden

Publisher: Elsevier

Published: 2018-07-19

Total Pages: 2602

ISBN-13: 0444642420

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Process Systems Engineering brings together the international community of researchers and engineers interested in computing-based methods in process engineering. This conference highlights the contributions of the PSE community towards the sustainability of modern society and is based on the 13th International Symposium on Process Systems Engineering PSE 2018 event held San Diego, CA, July 1-5 2018. The book contains contributions from academia and industry, establishing the core products of PSE, defining the new and changing scope of our results, and future challenges. Plenary and keynote lectures discuss real-world challenges (globalization, energy, environment and health) and contribute to discussions on the widening scope of PSE versus the consolidation of the core topics of PSE. Highlights how the Process Systems Engineering community contributes to the sustainability of modern society Establishes the core products of Process Systems Engineering Defines the future challenges of Process Systems Engineering


Book Synopsis 13th International Symposium on Process SystemsEngineering – PSE 2018, July 1-5 2018 by : Mario R. Eden

Download or read book 13th International Symposium on Process SystemsEngineering – PSE 2018, July 1-5 2018 written by Mario R. Eden and published by Elsevier. This book was released on 2018-07-19 with total page 2602 pages. Available in PDF, EPUB and Kindle. Book excerpt: Process Systems Engineering brings together the international community of researchers and engineers interested in computing-based methods in process engineering. This conference highlights the contributions of the PSE community towards the sustainability of modern society and is based on the 13th International Symposium on Process Systems Engineering PSE 2018 event held San Diego, CA, July 1-5 2018. The book contains contributions from academia and industry, establishing the core products of PSE, defining the new and changing scope of our results, and future challenges. Plenary and keynote lectures discuss real-world challenges (globalization, energy, environment and health) and contribute to discussions on the widening scope of PSE versus the consolidation of the core topics of PSE. Highlights how the Process Systems Engineering community contributes to the sustainability of modern society Establishes the core products of Process Systems Engineering Defines the future challenges of Process Systems Engineering

Parametric Programming Techniques for Process Engineering Problems Under Uncertainty

Parametric Programming Techniques for Process Engineering Problems Under Uncertainty

Author: Vivek Dua

Publisher:

Published: 2000

Total Pages:

ISBN-13:

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Book Synopsis Parametric Programming Techniques for Process Engineering Problems Under Uncertainty by : Vivek Dua

Download or read book Parametric Programming Techniques for Process Engineering Problems Under Uncertainty written by Vivek Dua and published by . This book was released on 2000 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Process Operations with Uncertainty and Integration Considerations

Process Operations with Uncertainty and Integration Considerations

Author: Zukui Li

Publisher:

Published: 2010

Total Pages: 136

ISBN-13:

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There has been a lot of attention in recent years towards the application of mathematical modeling and optimization approaches for the solution of production planning and scheduling problems. This is mainly due to the changing economic environment which pushes for more efficient process operations. However, there are still a number of challenges that restrict the effective application of optimization for planning and scheduling problem especially in the process industry. First, decision making in process operations is frequently based on parameters of which the values are uncertain. A systematic treatment of those uncertainties (e.g., processing time variations, rush orders, failed batches, machine breakdowns, etc) is necessary to satisfy the customer demands, increase the efficiency of operations and improve the plant profitability. Moreover, the interactions between the different decision-making levels were often ignored in existing solution approaches, which leads to sub-optimal and even infeasible solutions. Thus the integration of different decision making levels has been recognized by the research community as an imperative problem. In this work, systematic methods have been developed to address the above challenges. First, different methodologies are proposed to address the uncertainties in process scheduling problem: robust optimization based preventive scheduling strategy which aims at generating a robust preventive schedule to handle the possible parameter perturbations; parametric programming based reactive scheduling strategy which aims at responsive schedule regeneration or updating upon the happening of disruptive events. To address the interaction between planning and scheduling decision levels, a dual decomposition based approach that targets the solution of large scale planning and scheduling integration problem was proposed, which aims at decreasing the computational complexity through decomposition and parallel computation. Finally, the rolling horizon method which provides a promising modeling framework for integrated planning and scheduling and incorporation of uncertainty is studied. A novel method of generating production capacity model through parametric programming technique is proposed, and it is verified that the incorporation of the capacity model into the rolling horizon framework can improve the solution quality.


Book Synopsis Process Operations with Uncertainty and Integration Considerations by : Zukui Li

Download or read book Process Operations with Uncertainty and Integration Considerations written by Zukui Li and published by . This book was released on 2010 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: There has been a lot of attention in recent years towards the application of mathematical modeling and optimization approaches for the solution of production planning and scheduling problems. This is mainly due to the changing economic environment which pushes for more efficient process operations. However, there are still a number of challenges that restrict the effective application of optimization for planning and scheduling problem especially in the process industry. First, decision making in process operations is frequently based on parameters of which the values are uncertain. A systematic treatment of those uncertainties (e.g., processing time variations, rush orders, failed batches, machine breakdowns, etc) is necessary to satisfy the customer demands, increase the efficiency of operations and improve the plant profitability. Moreover, the interactions between the different decision-making levels were often ignored in existing solution approaches, which leads to sub-optimal and even infeasible solutions. Thus the integration of different decision making levels has been recognized by the research community as an imperative problem. In this work, systematic methods have been developed to address the above challenges. First, different methodologies are proposed to address the uncertainties in process scheduling problem: robust optimization based preventive scheduling strategy which aims at generating a robust preventive schedule to handle the possible parameter perturbations; parametric programming based reactive scheduling strategy which aims at responsive schedule regeneration or updating upon the happening of disruptive events. To address the interaction between planning and scheduling decision levels, a dual decomposition based approach that targets the solution of large scale planning and scheduling integration problem was proposed, which aims at decreasing the computational complexity through decomposition and parallel computation. Finally, the rolling horizon method which provides a promising modeling framework for integrated planning and scheduling and incorporation of uncertainty is studied. A novel method of generating production capacity model through parametric programming technique is proposed, and it is verified that the incorporation of the capacity model into the rolling horizon framework can improve the solution quality.

Multi-level Mixed-Integer Optimization

Multi-level Mixed-Integer Optimization

Author: Styliani Avraamidou

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2022-06-06

Total Pages: 154

ISBN-13: 3110760312

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This book provides the fundamental underlying mathematical theory, numerical algorithms and effi cient computational tools for the solution of multi-level mixedinteger optimization problems. It can enable a vast array of decision makers and engineers (e.g. process engineers, bioengineers, chemical and civil engineers, and economists) to model, formulate and solve hierarchical decision making problems. The book gives detailed insights on multi-level optimization by comprehensive explanations, step-by-step numerical examples and case studies, plots, and diagrams.


Book Synopsis Multi-level Mixed-Integer Optimization by : Styliani Avraamidou

Download or read book Multi-level Mixed-Integer Optimization written by Styliani Avraamidou and published by Walter de Gruyter GmbH & Co KG. This book was released on 2022-06-06 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the fundamental underlying mathematical theory, numerical algorithms and effi cient computational tools for the solution of multi-level mixedinteger optimization problems. It can enable a vast array of decision makers and engineers (e.g. process engineers, bioengineers, chemical and civil engineers, and economists) to model, formulate and solve hierarchical decision making problems. The book gives detailed insights on multi-level optimization by comprehensive explanations, step-by-step numerical examples and case studies, plots, and diagrams.

Decision Making Under Uncertainty

Decision Making Under Uncertainty

Author: Mykel J. Kochenderfer

Publisher: MIT Press

Published: 2015-07-24

Total Pages: 350

ISBN-13: 0262331713

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An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.


Book Synopsis Decision Making Under Uncertainty by : Mykel J. Kochenderfer

Download or read book Decision Making Under Uncertainty written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2015-07-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.

Decision Making under Deep Uncertainty

Decision Making under Deep Uncertainty

Author: Vincent A. W. J. Marchau

Publisher: Springer

Published: 2019-04-04

Total Pages: 408

ISBN-13: 3030052524

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This open access book focuses on both the theory and practice associated with the tools and approaches for decisionmaking in the face of deep uncertainty. It explores approaches and tools supporting the design of strategic plans under deep uncertainty, and their testing in the real world, including barriers and enablers for their use in practice. The book broadens traditional approaches and tools to include the analysis of actors and networks related to the problem at hand. It also shows how lessons learned in the application process can be used to improve the approaches and tools used in the design process. The book offers guidance in identifying and applying appropriate approaches and tools to design plans, as well as advice on implementing these plans in the real world. For decisionmakers and practitioners, the book includes realistic examples and practical guidelines that should help them understand what decisionmaking under deep uncertainty is and how it may be of assistance to them. Decision Making under Deep Uncertainty: From Theory to Practice is divided into four parts. Part I presents five approaches for designing strategic plans under deep uncertainty: Robust Decision Making, Dynamic Adaptive Planning, Dynamic Adaptive Policy Pathways, Info-Gap Decision Theory, and Engineering Options Analysis. Each approach is worked out in terms of its theoretical foundations, methodological steps to follow when using the approach, latest methodological insights, and challenges for improvement. In Part II, applications of each of these approaches are presented. Based on recent case studies, the practical implications of applying each approach are discussed in depth. Part III focuses on using the approaches and tools in real-world contexts, based on insights from real-world cases. Part IV contains conclusions and a synthesis of the lessons that can be drawn for designing, applying, and implementing strategic plans under deep uncertainty, as well as recommendations for future work. The publication of this book has been funded by the Radboud University, the RAND Corporation, Delft University of Technology, and Deltares.


Book Synopsis Decision Making under Deep Uncertainty by : Vincent A. W. J. Marchau

Download or read book Decision Making under Deep Uncertainty written by Vincent A. W. J. Marchau and published by Springer. This book was released on 2019-04-04 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book focuses on both the theory and practice associated with the tools and approaches for decisionmaking in the face of deep uncertainty. It explores approaches and tools supporting the design of strategic plans under deep uncertainty, and their testing in the real world, including barriers and enablers for their use in practice. The book broadens traditional approaches and tools to include the analysis of actors and networks related to the problem at hand. It also shows how lessons learned in the application process can be used to improve the approaches and tools used in the design process. The book offers guidance in identifying and applying appropriate approaches and tools to design plans, as well as advice on implementing these plans in the real world. For decisionmakers and practitioners, the book includes realistic examples and practical guidelines that should help them understand what decisionmaking under deep uncertainty is and how it may be of assistance to them. Decision Making under Deep Uncertainty: From Theory to Practice is divided into four parts. Part I presents five approaches for designing strategic plans under deep uncertainty: Robust Decision Making, Dynamic Adaptive Planning, Dynamic Adaptive Policy Pathways, Info-Gap Decision Theory, and Engineering Options Analysis. Each approach is worked out in terms of its theoretical foundations, methodological steps to follow when using the approach, latest methodological insights, and challenges for improvement. In Part II, applications of each of these approaches are presented. Based on recent case studies, the practical implications of applying each approach are discussed in depth. Part III focuses on using the approaches and tools in real-world contexts, based on insights from real-world cases. Part IV contains conclusions and a synthesis of the lessons that can be drawn for designing, applying, and implementing strategic plans under deep uncertainty, as well as recommendations for future work. The publication of this book has been funded by the Radboud University, the RAND Corporation, Delft University of Technology, and Deltares.

Probabilistic Robotics

Probabilistic Robotics

Author: Sebastian Thrun

Publisher: MIT Press

Published: 2005-08-19

Total Pages: 668

ISBN-13: 0262201623

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An introduction to the techniques and algorithms of the newest field in robotics. Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.


Book Synopsis Probabilistic Robotics by : Sebastian Thrun

Download or read book Probabilistic Robotics written by Sebastian Thrun and published by MIT Press. This book was released on 2005-08-19 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the techniques and algorithms of the newest field in robotics. Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

Reinforcement Learning and Stochastic Optimization

Reinforcement Learning and Stochastic Optimization

Author: Warren B. Powell

Publisher: John Wiley & Sons

Published: 2022-03-15

Total Pages: 1090

ISBN-13: 1119815037

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REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.


Book Synopsis Reinforcement Learning and Stochastic Optimization by : Warren B. Powell

Download or read book Reinforcement Learning and Stochastic Optimization written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2022-03-15 with total page 1090 pages. Available in PDF, EPUB and Kindle. Book excerpt: REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.