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Record Nr. |
UNINA9910495200503321 |
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Autore |
Kallrath Josef |
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Titolo |
Business Optimization Using Mathematical Programming : An Introduction with Case Studies and Solutions in Various Algebraic Modeling Languages / / by Josef Kallrath |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
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ISBN |
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Edizione |
[2nd ed. 2021.] |
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Descrizione fisica |
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1 online resource (653 pages) |
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Collana |
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International Series in Operations Research & Management Science, , 2214-7934 ; ; 307 |
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Disciplina |
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Soggetti |
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Operations research |
Management science |
Mathematical optimization |
Operations Research and Decision Theory |
Operations Research, Management Science |
Continuous Optimization |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Optimization: Using Models, Validating Models, Solutions, Answers -- From the Problem to its Mathematical Formulation -- Mathematical Solution Techniques -- Problems Solvable Using Linear Programming -- How Optimization is Used in Practice: Case Studies in Linear Programming -- Modeling Structures Using Mixed Integer Programming -- Types of Mixed Integer Linear Programming Problems -- Case Studies and Problem Formulations -- User Control of the Optimization Process and Improving Efficiency -- How Optimization is Used in Practice: Case Studies in Integer Programming -- Beyond LP and MILP Problems -- Mathematical Solution Techniques - The Nonlinear World -- Global Optimization in Practice -- Polylithic Modeling and Solution Approaches -- Cutting & Packing beyond and within Mathematical Programming -- The Impact and Implications of Optimization -- Concluding Remarks and Outlook. |
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Sommario/riassunto |
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This book presents a structured approach to formulate, model, and |
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solve mathematical optimization problems for a wide range of real world situations. Among the problems covered are production, distribution and supply chain planning, scheduling, vehicle routing, as well as cutting stock, packing, and nesting. The optimization techniques used to solve the problems are primarily linear, mixed-integer linear, nonlinear, and mixed integer nonlinear programming. The book also covers important considerations for solving real-world optimization problems, such as dealing with valid inequalities and symmetry during the modeling phase, but also data interfacing and visualization of results in a more and more digitized world. The broad range of ideas and approaches presented helps the reader to learn how to model a variety of problems from process industry, paper and metals industry, the energy sector, and logistics using mathematical optimization techniques. |
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