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Understand common scheduling as well as other advanced operational problems with this valuable reference from a recognized leader in the field. Beginning with basic principles and an overview of linear and mixed-integer programming, this unified treatment introduces the fundamental ideas underpinning most modeling approaches, and will allow you to easily develop your own models. With more than 150 figures, the basic concepts and ideas behind the development of different approaches are clearly illustrated. Addresses a wide range of problems arising in diverse industrial sectors, from oil and gas to fine chemicals, and from commodity chemicals to food manufacturing. A perfect resource for engineering and computer science students, researchers working in the area, and industrial practitioners.
Book Synopsis Chemical Production Scheduling by : Christos T. Maravelias
Download or read book Chemical Production Scheduling written by Christos T. Maravelias and published by Cambridge University Press. This book was released on 2021-05-06 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand common scheduling as well as other advanced operational problems with this valuable reference from a recognized leader in the field. Beginning with basic principles and an overview of linear and mixed-integer programming, this unified treatment introduces the fundamental ideas underpinning most modeling approaches, and will allow you to easily develop your own models. With more than 150 figures, the basic concepts and ideas behind the development of different approaches are clearly illustrated. Addresses a wide range of problems arising in diverse industrial sectors, from oil and gas to fine chemicals, and from commodity chemicals to food manufacturing. A perfect resource for engineering and computer science students, researchers working in the area, and industrial practitioners.
Book Synopsis Mixed-integer Programming Methods for Chemical Production Scheduling by : Arul Sundaramoorthy
Download or read book Mixed-integer Programming Methods for Chemical Production Scheduling written by Arul Sundaramoorthy and published by . This book was released on 2011 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Optimization-based chemical production scheduling allows for efficient utilization of available assets and brings significant operational benefits including reduction in costs. Unfortunately, application of such techniques to industrial settings is challenging due to multiple reasons: (i) the optimization models need to be general to accommodate different production processes, (ii) the solution of such models need to be quick to allow for frequent updates to the schedules, and (iii) the models should be capable of providing multiple alternative schedules for the practitioners to compare and implement. The goal of this work is to address the aforementioned challenges and bring optimization-based scheduling techniques closer to industrial applications. First, we develop mathematical programming models for simultaneous batching and scheduling in general sequential production environment while taking into account various process features including storage policies and limited shared utilities. The models are based on novel modeling approaches which allow for exploitation of instance characteristics, thus leading to solution of large-scale instances. Second, we develop a novel framework for a solution algorithm that harnesses the advantages of discrete- and continuous-time scheduling models. Specifically, we propose an algorithm that has modeling flexibility and computational efficiency of discrete-time models, as well as high solution accuracy of their continuous counterparts. We investigate in detail how the algorithm can be improved and extended to solve real-world industrial instances that are thought to be computationally near impossible if transitional methods were to be used. Finally, we develop systematic methods to generate multiple alternative schedules, specifically to account for modeling simplifications introduced in the scheduling models and plant nervousness when revising schedules. We generate alternative schedules by quantifying specific characteristics of a schedule using explicitly defined metrics, which are favored at different degrees by penalizing them in the objective function with varying penalty weights. We show that, by leveraging penalty weights, schedules with desirable properties can be readily found.
Book Synopsis Mixed-integer Programming Models and Solution Methods for Chemical Production Scheduling by : Ho Jae Lee
Download or read book Mixed-integer Programming Models and Solution Methods for Chemical Production Scheduling written by Ho Jae Lee and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization-based chemical production scheduling allows for efficient utilization of available assets and brings significant operational benefits including reduction in costs. Unfortunately, application of such techniques to industrial settings is challenging due to multiple reasons: (i) the optimization models need to be general to accommodate different production processes, (ii) the solution of such models need to be quick to allow for frequent updates to the schedules, and (iii) the models should be capable of providing multiple alternative schedules for the practitioners to compare and implement. The goal of this work is to address the aforementioned challenges and bring optimization-based scheduling techniques closer to industrial applications. First, we develop mathematical programming models for simultaneous batching and scheduling in general sequential production environment while taking into account various process features including storage policies and limited shared utilities. The models are based on novel modeling approaches which allow for exploitation of instance characteristics, thus leading to solution of large-scale instances. Second, we develop a novel framework for a solution algorithm that harnesses the advantages of discrete- and continuous-time scheduling models. Specifically, we propose an algorithm that has modeling flexibility and computational efficiency of discrete-time models, as well as high solution accuracy of their continuous counterparts. We investigate in detail how the algorithm can be improved and extended to solve real-world industrial instances that are thought to be computationally near impossible if transitional methods were to be used. Finally, we develop systematic methods to generate multiple alternative schedules, specifically to account for modeling simplifications introduced in the scheduling models and plant nervousness when revising schedules. We generate alternative schedules by quantifying specific characteristics of a schedule using explicitly defined metrics, which are favored at different degrees by penalizing them in the objective function with varying penalty weights. We show that, by leveraging penalty weights, schedules with desirable properties can be readily found.
Chemical production scheduling optimization has the potential to reduce operating cost, increase profits, and improve efficiency. These optimization problems often formulated as mixed integer programming models which, despite advances in computer hardware and optimization software, remain hard to solve. We first formulate a more general model and then develop several solution methods to speed up the computational times. We show that the production environment can be defined by material handling constraints and formulate a general model that is valid for all production environments. We develop new formulations for processes with changeovers and compare their relative tightness and present computational results for several example problems. In the first solution method, customer orders are propagated backwards through the network to find the minimum amount of material each task must process, providing a lower bound on the number of times each task must run. We extend these methods to the general model. This method is most effective for cost minimization and can lead to a 2-3 order-of-magnitude improvement in computational time. The next method reduces the size of the model by using different time grids for each task, unit, material, and utility. We prove that this formulation will have the same optimal solution as a single-grid formulation. This method is most effective for makespan. The third method uses a parallel batch-and-bound algorithm. The scheduling problem is divided into subproblems by branching on the number of times each task runs. Each of these subproblems is solved in parallel by a separate core and may be divided further. Many difficult problems can be solved to optimality with this method. The final method is the simplest and most effective. Many equivalent schedules can be formed by simply shifting tasks in units with idle time earlier or later. These schedules have the same number of batches and similar objectives. Introducing a new integer variable representing the number of batches of each task allows the solver to branch on this variable to find truly different schedules quickly. This method is the most effective with over 2, 3, or 4 orders-of-magnitude improvements for makespan, profit, and cost optimization respectively.
Book Synopsis Models and Solution Methods for Chemical Production Scheduling by :
Download or read book Models and Solution Methods for Chemical Production Scheduling written by and published by . This book was released on 2014 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chemical production scheduling optimization has the potential to reduce operating cost, increase profits, and improve efficiency. These optimization problems often formulated as mixed integer programming models which, despite advances in computer hardware and optimization software, remain hard to solve. We first formulate a more general model and then develop several solution methods to speed up the computational times. We show that the production environment can be defined by material handling constraints and formulate a general model that is valid for all production environments. We develop new formulations for processes with changeovers and compare their relative tightness and present computational results for several example problems. In the first solution method, customer orders are propagated backwards through the network to find the minimum amount of material each task must process, providing a lower bound on the number of times each task must run. We extend these methods to the general model. This method is most effective for cost minimization and can lead to a 2-3 order-of-magnitude improvement in computational time. The next method reduces the size of the model by using different time grids for each task, unit, material, and utility. We prove that this formulation will have the same optimal solution as a single-grid formulation. This method is most effective for makespan. The third method uses a parallel batch-and-bound algorithm. The scheduling problem is divided into subproblems by branching on the number of times each task runs. Each of these subproblems is solved in parallel by a separate core and may be divided further. Many difficult problems can be solved to optimality with this method. The final method is the simplest and most effective. Many equivalent schedules can be formed by simply shifting tasks in units with idle time earlier or later. These schedules have the same number of batches and similar objectives. Introducing a new integer variable representing the number of batches of each task allows the solver to branch on this variable to find truly different schedules quickly. This method is the most effective with over 2, 3, or 4 orders-of-magnitude improvements for makespan, profit, and cost optimization respectively.
Disjunctive Programming is a technique and a discipline initiated by the author in the early 1970's, which has become a central tool for solving nonconvex optimization problems like pure or mixed integer programs, through convexification (cutting plane) procedures combined with enumeration. It has played a major role in the revolution in the state of the art of Integer Programming that took place roughly during the period 1990-2010. The main benefit that the reader may acquire from reading this book is a deeper understanding of the theoretical underpinnings and of the applications potential of disjunctive programming, which range from more efficient problem formulation to enhanced modeling capability and improved solution methods for integer and combinatorial optimization. Egon Balas is University Professor and Lord Professor of Operations Research at Carnegie Mellon University's Tepper School of Business.
Book Synopsis Disjunctive Programming by : Egon Balas
Download or read book Disjunctive Programming written by Egon Balas and published by Springer. This book was released on 2018-11-27 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Disjunctive Programming is a technique and a discipline initiated by the author in the early 1970's, which has become a central tool for solving nonconvex optimization problems like pure or mixed integer programs, through convexification (cutting plane) procedures combined with enumeration. It has played a major role in the revolution in the state of the art of Integer Programming that took place roughly during the period 1990-2010. The main benefit that the reader may acquire from reading this book is a deeper understanding of the theoretical underpinnings and of the applications potential of disjunctive programming, which range from more efficient problem formulation to enhanced modeling capability and improved solution methods for integer and combinatorial optimization. Egon Balas is University Professor and Lord Professor of Operations Research at Carnegie Mellon University's Tepper School of Business.
This textbook provides a comprehensive modeling, reformulation and optimization approach for solving production planning and supply chain planning problems, covering topics from a basic introduction to planning systems, mixed integer programming (MIP) models and algorithms through the advanced description of mathematical results in polyhedral combinatorics required to solve these problems. Based on twenty years worth of research in which the authors have played a significant role, the book addresses real life industrial production planning problems (involving complex production structures with multiple production stages) using MIP modeling and reformulation approach. The book provides an introduction to MIP modeling and to planning systems, a unique collection of reformulation results, and an easy to use problem-solving library. This approach is demonstrated through a series of real life case studies, exercises and detailed illustrations. Review by Jakub Marecek (Computer Journal) The emphasis put on mixed integer rounding and mixing sets, heuristics in-built in general purpose integer programming solvers, as well as on decompositions and heuristics using integer programming should be praised... There is no doubt that this volume offers the present best introduction to integer programming formulations of lotsizing problems, encountered in production planning. (2007)
Book Synopsis Production Planning by Mixed Integer Programming by : Yves Pochet
Download or read book Production Planning by Mixed Integer Programming written by Yves Pochet and published by Springer Science & Business Media. This book was released on 2006-04-19 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a comprehensive modeling, reformulation and optimization approach for solving production planning and supply chain planning problems, covering topics from a basic introduction to planning systems, mixed integer programming (MIP) models and algorithms through the advanced description of mathematical results in polyhedral combinatorics required to solve these problems. Based on twenty years worth of research in which the authors have played a significant role, the book addresses real life industrial production planning problems (involving complex production structures with multiple production stages) using MIP modeling and reformulation approach. The book provides an introduction to MIP modeling and to planning systems, a unique collection of reformulation results, and an easy to use problem-solving library. This approach is demonstrated through a series of real life case studies, exercises and detailed illustrations. Review by Jakub Marecek (Computer Journal) The emphasis put on mixed integer rounding and mixing sets, heuristics in-built in general purpose integer programming solvers, as well as on decompositions and heuristics using integer programming should be praised... There is no doubt that this volume offers the present best introduction to integer programming formulations of lotsizing problems, encountered in production planning. (2007)
Many engineering, operations, and scientific applications include a mixture of discrete and continuous decision variables and nonlinear relationships involving the decision variables that have a pronounced effect on the set of feasible and optimal solutions. Mixed-integer nonlinear programming (MINLP) problems combine the numerical difficulties of handling nonlinear functions with the challenge of optimizing in the context of nonconvex functions and discrete variables. MINLP is one of the most flexible modeling paradigms available for optimization; but because its scope is so broad, in the most general cases it is hopelessly intractable. Nonetheless, an expanding body of researchers and practitioners — including chemical engineers, operations researchers, industrial engineers, mechanical engineers, economists, statisticians, computer scientists, operations managers, and mathematical programmers — are interested in solving large-scale MINLP instances.
Book Synopsis Mixed Integer Nonlinear Programming by : Jon Lee
Download or read book Mixed Integer Nonlinear Programming written by Jon Lee and published by Springer Science & Business Media. This book was released on 2011-12-02 with total page 687 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many engineering, operations, and scientific applications include a mixture of discrete and continuous decision variables and nonlinear relationships involving the decision variables that have a pronounced effect on the set of feasible and optimal solutions. Mixed-integer nonlinear programming (MINLP) problems combine the numerical difficulties of handling nonlinear functions with the challenge of optimizing in the context of nonconvex functions and discrete variables. MINLP is one of the most flexible modeling paradigms available for optimization; but because its scope is so broad, in the most general cases it is hopelessly intractable. Nonetheless, an expanding body of researchers and practitioners — including chemical engineers, operations researchers, industrial engineers, mechanical engineers, economists, statisticians, computer scientists, operations managers, and mathematical programmers — are interested in solving large-scale MINLP instances.
Book Synopsis Rounding strategies for mixed integer programs arising from chemical production planning by : Rainer E. Burkard
Download or read book Rounding strategies for mixed integer programs arising from chemical production planning written by Rainer E. Burkard and published by . This book was released on 1997 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Batch chemical processes, so often employed in the pharmaceutical and agrochemical fields, differ significantly from standard continuous operations in the emphasis upon time as a critical factor in their synthesis and design. With this inclusive guide to batch chemical processes, the author introduces the reader to key aspects in mathematical modeling of batch processes and presents techniques to overcome the computational complexity in order to yield models that are solvable in near real-time. This book demonstrates how batch processes can be analyzed, synthesized, and designed optimally using proven mathematical formulations. The text effectively demonstrates how water and energy aspects can be incorporated within the scheduling framework that seeks to capture the essence of time. It presents real-life case studies where mathematical modeling of batch plants has been successfully applied.
Book Synopsis Understanding Batch Chemical Processes by : Thokozani Majozi
Download or read book Understanding Batch Chemical Processes written by Thokozani Majozi and published by CRC Press. This book was released on 2017-03-16 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Batch chemical processes, so often employed in the pharmaceutical and agrochemical fields, differ significantly from standard continuous operations in the emphasis upon time as a critical factor in their synthesis and design. With this inclusive guide to batch chemical processes, the author introduces the reader to key aspects in mathematical modeling of batch processes and presents techniques to overcome the computational complexity in order to yield models that are solvable in near real-time. This book demonstrates how batch processes can be analyzed, synthesized, and designed optimally using proven mathematical formulations. The text effectively demonstrates how water and energy aspects can be incorporated within the scheduling framework that seeks to capture the essence of time. It presents real-life case studies where mathematical modeling of batch plants has been successfully applied.
A unique text covering basic and advanced concepts of optimization theory and methods for process systems engineers. With examples illustrating key concepts and algorithms, and exercises involving theoretical derivations, numerical problems and modeling systems, it is ideal for single-semester, graduate courses in process systems engineering.
Book Synopsis Advanced Optimization for Process Systems Engineering by : Ignacio E. Grossmann
Download or read book Advanced Optimization for Process Systems Engineering written by Ignacio E. Grossmann and published by Cambridge University Press. This book was released on 2021-03-25 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unique text covering basic and advanced concepts of optimization theory and methods for process systems engineers. With examples illustrating key concepts and algorithms, and exercises involving theoretical derivations, numerical problems and modeling systems, it is ideal for single-semester, graduate courses in process systems engineering.
