Pyomo Optimization Modeling In Python

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Pyomo – Optimization Modeling in Python

Author : William E. Hart
Publisher : Springer Science & Business Media
ISBN 13 : 146143226X
Total Pages : 245 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Pyomo – Optimization Modeling in Python by : William E. Hart

Download or read book Pyomo – Optimization Modeling in Python written by William E. Hart and published by Springer Science & Business Media. This book was released on 2012-02-15 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a complete and comprehensive reference/guide to Pyomo (Python Optimization Modeling Objects) for both beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. The text illustrates the breadth of the modeling and analysis capabilities that are supported by the software and support of complex real-world applications. Pyomo is an open source software package for formulating and solving large-scale optimization and operations research problems. The text begins with a tutorial on simple linear and integer programming models. A detailed reference of Pyomo's modeling components is illustrated with extensive examples, including a discussion of how to load data from data sources like spreadsheets and databases. Chapters describing advanced modeling capabilities for nonlinear and stochastic optimization are also included. The Pyomo software provides familiar modeling features within Python, a powerful dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions. The software supports a different modeling approach than commercial AML (Algebraic Modeling Languages) tools, and is designed for flexibility, extensibility, portability, and maintainability but also maintains the central ideas in modern AMLs.

Pyomo — Optimization Modeling in Python

Author : Michael L. Bynum
Publisher : Springer Nature
ISBN 13 : 303068928X
Total Pages : 225 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Pyomo — Optimization Modeling in Python by : Michael L. Bynum

Download or read book Pyomo — Optimization Modeling in Python written by Michael L. Bynum and published by Springer Nature. This book was released on 2021-05-01 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. In the third edition, much of the material has been reorganized, new examples have been added, and a new chapter has been added describing how modelers can improve the performance of their models. The authors have also modified their recommended method for importing Pyomo. A big change in this edition is the emphasis of concrete models, which provide fewer restrictions on the specification and use of Pyomo models. Pyomo is an open source software package for formulating and solving large-scale optimization problems. The software extends the modeling approach supported by modern AML (Algebraic Modeling Language) tools. Pyomo is a flexible, extensible, and portable AML that is embedded in Python, a full-featured scripting language. Python is a powerful and dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions.

Pyomo — Optimization Modeling in Python

Author : William E. Hart
Publisher : Springer
ISBN 13 : 3319588214
Total Pages : 280 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Pyomo — Optimization Modeling in Python by : William E. Hart

Download or read book Pyomo — Optimization Modeling in Python written by William E. Hart and published by Springer. This book was released on 2017-05-26 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. This second edition provides an expanded presentation of Pyomo’s modeling capabilities, providing a broader description of the software that will enable the user to develop and optimize models. Introductory chapters have been revised to extend tutorials; chapters that discuss advanced features now include the new functionalities added to Pyomo since the first edition including generalized disjunctive programming, mathematical programming with equilibrium constraints, and bilevel programming. Pyomo is an open source software package for formulating and solving large-scale optimization problems. The software extends the modeling approach supported by modern AML (Algebraic Modeling Language) tools. Pyomo is a flexible, extensible, and portable AML that is embedded in Python, a full-featured scripting language. Python is a powerful and dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions.

Operations Research and Cyber-Infrastructure

Author : John W. Chinneck
Publisher : Springer Science & Business Media
ISBN 13 : 0387888438
Total Pages : 460 pages
Book Rating : 4.3/5 (878 download)

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Book Synopsis Operations Research and Cyber-Infrastructure by : John W. Chinneck

Download or read book Operations Research and Cyber-Infrastructure written by John W. Chinneck and published by Springer Science & Business Media. This book was released on 2009-01-05 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Operations Research and Cyber-Infrastructure is the companion volume to the Eleventh INFORMS Computing Society Conference (ICS 2009), held in Charleston, South Carolina, from January 11 to 13, 2009. It includes 24 high-quality refereed research papers. As always, the focus of interest for ICS is the interface between Operations Research and Computer Science, and the papers in this volume reflect that interest. This is naturally an evolving area as computational power increases rapidly while decreasing in cost even more quickly, and the papers included here illustrate the wide range of topics at this interface.

Pyomo -- Optimization Modeling in Python

Author : Michael L. Bynum
Publisher :
ISBN 13 : 9783030689292
Total Pages : 231 pages
Book Rating : 4.6/5 (892 download)

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Book Synopsis Pyomo -- Optimization Modeling in Python by : Michael L. Bynum

Download or read book Pyomo -- Optimization Modeling in Python written by Michael L. Bynum and published by . This book was released on 2021 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. In the third edition, much of the material has been reorganized, new examples have been added, and a new chapter has been added describing how modelers can improve the performance of their models. The authors have also modified their recommended method for importing Pyomo. A big change in this edition is the emphasis of concrete models, which provide fewer restrictions on the specification and use of Pyomo models. Pyomo is an open source software package for formulating and solving large-scale optimization problems. The software extends the modeling approach supported by modern AML (Algebraic Modeling Language) tools. Pyomo is a flexible, extensible, and portable AML that is embedded in Python, a full-featured scripting language. Python is a powerful and dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions. Review of the Second edition: This book provides a detailed guide to Pyomo for beginners and advanced users from undergraduate students to academic researchers to practitioners. the book is a good software guide which I strongly recommend to anybody interested in looking for an alternative to commercial modeling languages in general or in learning or intensifying their Pyomo skills in particular. Christina Schenk, SIAM Review, Vol. 61 (1), March 2019.

Power System Optimization Modeling in GAMS

Author : Alireza Soroudi
Publisher : Springer
ISBN 13 : 3319623508
Total Pages : 309 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Power System Optimization Modeling in GAMS by : Alireza Soroudi

Download or read book Power System Optimization Modeling in GAMS written by Alireza Soroudi and published by Springer. This book was released on 2017-08-29 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique book describes how the General Algebraic Modeling System (GAMS) can be used to solve various power system operation and planning optimization problems. This book is the first of its kind to provide readers with a comprehensive reference that includes the solution codes for basic/advanced power system optimization problems in GAMS, a computationally efficient tool for analyzing optimization problems in power and energy systems. The book covers theoretical background as well as the application examples and test case studies. It is a suitable reference for dedicated and general audiences including power system professionals as well as researchers and developers from the energy sector and electrical power engineering community and will be helpful to undergraduate and graduate students.

Introduction to Computational Models with Python

Author : Jose M. Garrido
Publisher : CRC Press
ISBN 13 : 1498712045
Total Pages : 492 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Introduction to Computational Models with Python by : Jose M. Garrido

Download or read book Introduction to Computational Models with Python written by Jose M. Garrido and published by CRC Press. This book was released on 2015-08-28 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python programming language. The book uses the Python programming language interpreter and several packages from the huge Python Library that improve the performance of numerical computing, such as the Numpy and Scipy m

Mathematical Programming for Power Systems Operation

Author : Alejandro Garcés
Publisher : John Wiley & Sons
ISBN 13 : 1119747287
Total Pages : 293 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Mathematical Programming for Power Systems Operation by : Alejandro Garcés

Download or read book Mathematical Programming for Power Systems Operation written by Alejandro Garcés and published by John Wiley & Sons. This book was released on 2021-12-01 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the theoretical foundations and real-world power system applications of convex programming In Mathematical Programming for Power System Operation with Applications in Python, Professor Alejandro Garces delivers a comprehensive overview of power system operations models with a focus on convex optimization models and their implementation in Python. Divided into two parts, the book begins with a theoretical analysis of convex optimization models before moving on to related applications in power systems operations. The author eschews concepts of topology and functional analysis found in more mathematically oriented books in favor of a more natural approach. Using this perspective, he presents recent applications of convex optimization in power system operations problems. Mathematical Programming for Power System Operation with Applications in Python uses Python and CVXPY as tools to solve power system optimization problems and includes models that can be solved with the presented framework. The book also includes: A thorough introduction to power system operation, including economic and environmental dispatch, optimal power flow, and hosting capacity Comprehensive explorations of the mathematical background of power system operation, including quadratic forms and norms and the basic theory of optimization Practical discussions of convex functions and convex sets, including affine and linear spaces, politopes, balls, and ellipsoids In-depth examinations of convex optimization, including global optimums, and first and second order conditions Perfect for undergraduate students with some knowledge in power systems analysis, generation, or distribution, Mathematical Programming for Power System Operation with Applications in Python is also an ideal resource for graduate students and engineers practicing in the area of power system optimization.

Mixed Integer Nonlinear Programming

Author : Jon Lee
Publisher : Springer Science & Business Media
ISBN 13 : 1461419271
Total Pages : 687 pages
Book Rating : 4.4/5 (614 download)

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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.

Multiple Criteria Decision Aid

Author : Jason Papathanasiou
Publisher : Springer
ISBN 13 : 3319916483
Total Pages : 182 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Multiple Criteria Decision Aid by : Jason Papathanasiou

Download or read book Multiple Criteria Decision Aid written by Jason Papathanasiou and published by Springer. This book was released on 2018-09-19 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple criteria decision aid (MCDA) methods are illustrated in this book through theoretical and computational techniques utilizing Python. Existing methods are presented in detail with a step by step learning approach. Theoretical background is given for TOPSIS, VIKOR, PROMETHEE, SIR, AHP, goal programming, and their variations. Comprehensive numerical examples are also discussed for each method in conjunction with easy to follow Python code. Extensions to multiple criteria decision making algorithms such as fuzzy number theory and group decision making are introduced and implemented through Python as well. Readers will learn how to implement and use each method based on the problem, the available data, the stakeholders involved, and the various requirements needed. Focusing on the practical aspects of the multiple criteria decision making methodologies, this book is designed for researchers, practitioners and advanced graduate students in the applied mathematics, information systems, operations research and business administration disciplines, as well as other engineers and scientists oriented in interdisciplinary research. Readers will greatly benefit from this book by learning and applying various MCDM/A methods. (Adiel Teixeira de Almeida, CDSID-Center for Decision System and Information Development, Universidade Federal de Pernambuco, Recife, Brazil) Promoting the development and application of multicriteria decision aid is essential to ensure more ethical and sustainable decisions. This book is a great contribution to this objective. It is a perfect blend of theory and practice, providing potential users and researchers with the theoretical bases of some of the best-known methods as well as with the computing tools needed to practice, to compare and to put these methods to use. (Jean-Pierre Brans, Vrije Universiteit Brussel, Brussels, Belgium) This book is intended for researchers, practitioners and students alike in decision support who wish to familiarize themselves quickly and efficiently with multicriteria decision aiding algorithms. The proposed approach is original, as it presents a selection of methods from the theory to the practical implementation in Python, including a detailed example. This will certainly facilitate the learning of these techniques, and contribute to their effective dissemination in applications. (Patrick Meyer, IMT Atlantique, Lab-STICC, Univ. Bretagne Loire, Brest, France)

Practical Numerical and Scientific Computing with MATLAB® and Python

Author : Eihab B. M. Bashier
Publisher : CRC Press
ISBN 13 : 0429666829
Total Pages : 349 pages
Book Rating : 4.4/5 (296 download)

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Book Synopsis Practical Numerical and Scientific Computing with MATLAB® and Python by : Eihab B. M. Bashier

Download or read book Practical Numerical and Scientific Computing with MATLAB® and Python written by Eihab B. M. Bashier and published by CRC Press. This book was released on 2020-03-18 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Numerical and Scientific Computing with MATLAB® and Python concentrates on the practical aspects of numerical analysis and linear and non-linear programming. It discusses the methods for solving different types of mathematical problems using MATLAB and Python. Although the book focuses on the approximation problem rather than on error analysis of mathematical problems, it provides practical ways to calculate errors. The book is divided into three parts, covering topics in numerical linear algebra, methods of interpolation, numerical differentiation and integration, solutions of differential equations, linear and non-linear programming problems, and optimal control problems. This book has the following advantages: It adopts the programming languages, MATLAB and Python, which are widely used among academics, scientists, and engineers, for ease of use and contain many libraries covering many scientific and engineering fields. It contains topics that are rarely found in other numerical analysis books, such as ill-conditioned linear systems and methods of regularization to stabilize their solutions, nonstandard finite differences methods for solutions of ordinary differential equations, and the computations of the optimal controls. It provides a practical explanation of how to apply these topics using MATLAB and Python. It discusses software libraries to solve mathematical problems, such as software Gekko, pulp, and pyomo. These libraries use Python for solutions to differential equations and static and dynamic optimization problems. Most programs in the book can be applied in versions prior to MATLAB 2017b and Python 3.7.4 without the need to modify these programs. This book is aimed at newcomers and middle-level students, as well as members of the scientific community who are interested in solving math problems using MATLAB or Python.

Process Modelling and Simulation

Author : César de Prada
Publisher : MDPI
ISBN 13 : 3039214551
Total Pages : 298 pages
Book Rating : 4.0/5 (392 download)

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Book Synopsis Process Modelling and Simulation by : César de Prada

Download or read book Process Modelling and Simulation written by César de Prada and published by MDPI. This book was released on 2019-09-23 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since process models are nowadays ubiquitous in many applications, the challenges and alternatives related to their development, validation, and efficient use have become more apparent. In addition, the massive amounts of both offline and online data available today open the door for new applications and solutions. However, transforming data into useful models and information in the context of the process industry or of bio-systems requires specific approaches and considerations such as new modelling methodologies incorporating the complex, stochastic, hybrid and distributed nature of many processes in particular. The same can be said about the tools and software environments used to describe, code, and solve such models for their further exploitation. Going well beyond mere simulation tools, these advanced tools offer a software suite built around the models, facilitating tasks such as experiment design, parameter estimation, model initialization, validation, analysis, size reduction, discretization, optimization, distributed computation, co-simulation, etc. This Special Issue collects novel developments in these topics in order to address the challenges brought by the use of models in their different facets, and to reflect state of the art developments in methods, tools and industrial applications.

Modeling Languages in Mathematical Optimization

Author : Josef Kallrath
Publisher : Springer Science & Business Media
ISBN 13 : 1461302153
Total Pages : 427 pages
Book Rating : 4.4/5 (613 download)

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Book Synopsis Modeling Languages in Mathematical Optimization by : Josef Kallrath

Download or read book Modeling Languages in Mathematical Optimization written by Josef Kallrath and published by Springer Science & Business Media. This book was released on 2013-12-01 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents a unique combination of modeling and solving real world optimization problems. It is the only book which treats systematically the major modeling languages and systems used to solve mathematical optimization problems, and it also provides a useful overview and orientation of today's modeling languages in mathematical optimization. It demonstrates the strengths and characteristic features of such languages and provides a bridge for researchers, practitioners and students into a new world: solving real optimization problems with the most advances modeling systems.

A Student's Guide to Python for Physical Modeling

Author : Jesse M. Kinder
Publisher : Princeton University Press
ISBN 13 : 0691223661
Total Pages : 241 pages
Book Rating : 4.6/5 (912 download)

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Book Synopsis A Student's Guide to Python for Physical Modeling by : Jesse M. Kinder

Download or read book A Student's Guide to Python for Physical Modeling written by Jesse M. Kinder and published by Princeton University Press. This book was released on 2021-08-03 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fully updated tutorial on the basics of the Python programming language for science students Python is a computer programming language that has gained popularity throughout the sciences. This fully updated second edition of A Student's Guide to Python for Physical Modeling aims to help you, the student, teach yourself enough of the Python programming language to get started with physical modeling. You will learn how to install an open-source Python programming environment and use it to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; and simulation. No prior programming experience is assumed. This guide introduces a wide range of useful tools, including: Basic Python programming and scripting Numerical arrays Two- and three-dimensional graphics Animation Monte Carlo simulations Numerical methods, including solving ordinary differential equations Image processing Numerous code samples and exercises—with solutions—illustrate new ideas as they are introduced. This guide also includes supplemental online resources: code samples, data sets, tutorials, and more. This edition includes new material on symbolic calculations with SymPy, an introduction to Python libraries for data science and machine learning (pandas and sklearn), and a primer on Python classes and object-oriented programming. A new appendix also introduces command line tools and version control with Git.

Chemical Production Scheduling

Author : Christos T. Maravelias
Publisher : Cambridge University Press
ISBN 13 : 1107154758
Total Pages : 459 pages
Book Rating : 4.1/5 (71 download)

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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.

Grokking Artificial Intelligence Algorithms

Author : Rishal Hurbans
Publisher : Simon and Schuster
ISBN 13 : 1638355681
Total Pages : 392 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Grokking Artificial Intelligence Algorithms by : Rishal Hurbans

Download or read book Grokking Artificial Intelligence Algorithms written by Rishal Hurbans and published by Simon and Schuster. This book was released on 2020-07-20 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: "From start to finish, the best book to help you learn AI algorithms and recall why and how you use them." - Linda Ristevski, York Region District School Board ”This book takes an impossibly broad area of computer science and communicates what working developers need to understand in a clear and thorough way.” - David Jacobs, Product Advance Local Key Features Master the core algorithms of deep learning and AI Build an intuitive understanding of AI problems and solutions Written in simple language, with lots of illustrations and hands-on examples Creative coding exercises, including building a maze puzzle game and exploring drone optimization About The Book “Artificial intelligence” requires teaching a computer how to approach different types of problems in a systematic way. The core of AI is the algorithms that the system uses to do things like identifying objects in an image, interpreting the meaning of text, or looking for patterns in data to spot fraud and other anomalies. Mastering the core algorithms for search, image recognition, and other common tasks is essential to building good AI applications Grokking Artificial Intelligence Algorithms uses illustrations, exercises, and jargon-free explanations to teach fundamental AI concepts.You’ll explore coding challenges like detecting bank fraud, creating artistic masterpieces, and setting a self-driving car in motion. All you need is the algebra you remember from high school math class and beginning programming skills. What You Will Learn Use cases for different AI algorithms Intelligent search for decision making Biologically inspired algorithms Machine learning and neural networks Reinforcement learning to build a better robot This Book Is Written For For software developers with high school–level math skills. About the Author Rishal Hurbans is a technologist, startup and AI group founder, and international speaker. Table of Contents 1 Intuition of artificial intelligence 2 Search fundamentals 3 Intelligent search 4 Evolutionary algorithms 5 Advanced evolutionary approaches 6 Swarm intelligence: Ants 7 Swarm intelligence: Particles 8 Machine learning 9 Artificial neural networks 10 Reinforcement learning with Q-learning

Inventory Optimization

Author : Nicolas Vandeput
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3110673991
Total Pages : 305 pages
Book Rating : 4.1/5 (16 download)

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Book Synopsis Inventory Optimization by : Nicolas Vandeput

Download or read book Inventory Optimization written by Nicolas Vandeput and published by Walter de Gruyter GmbH & Co KG. This book was released on 2020-08-24 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book . . . Nicolas Vandeput hacks his way through the maze of quantitative supply chain optimizations. This book illustrates how the quantitative optimization of 21st century supply chains should be crafted and executed. . . . Vandeput is at the forefront of a new and better way of doing supply chains, and thanks to a richly illustrated book, where every single situation gets its own illustrating code snippet, so could you. --Joannes Vermorel, CEO, Lokad Inventory Optimization argues that mathematical inventory models can only take us so far with supply chain management. In order to optimize inventory policies, we have to use probabilistic simulations. The book explains how to implement these models and simulations step-by-step, starting from simple deterministic ones to complex multi-echelon optimization. The first two parts of the book discuss classical mathematical models, their limitations and assumptions, and a quick but effective introduction to Python is provided. Part 3 contains more advanced models that will allow you to optimize your profits, estimate your lost sales and use advanced demand distributions. It also provides an explanation of how you can optimize a multi-echelon supply chain based on a simple—yet powerful—framework. Part 4 discusses inventory optimization thanks to simulations under custom discrete demand probability functions. Inventory managers, demand planners and academics interested in gaining cost-effective solutions will benefit from the "do-it-yourself" examples and Python programs included in each chapter. Events around the book Link to a De Gruyter Online Event in which the author Nicolas Vandeput together with Stefan de Kok, supply chain innovator and CEO of Wahupa; Koen Cobbaert, Director in the S&O Industry practice of PwC Belgium; Bram Desmet, professor of operations & supply chain at the Vlerick Business School in Ghent; and Karl-Eric Devaux, Planning Consultant, Hatmill, discuss about models for inventory optimization. The event will be moderated by Eric Wilson, Director of Thought Leadership for Institute of Business Forecasting (IBF): https://youtu.be/565fDQMJEEg