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Large scale optimization in supply c...
~
Fathi, Mahdi.
Large scale optimization in supply chains and smart manufacturingtheory and applications /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Large scale optimization in supply chains and smart manufacturingedited by Jesus M. Velasquez-Bermudez, Marzieh Khakifirooz, Mahdi Fathi.
Reminder of title:
theory and applications /
other author:
Velasquez-Bermudez, Jesus M.
Published:
Cham :Springer International Publishing :2019.
Description:
xxi, 282 p. :ill., digital ;24 cm.
Contained By:
Springer Nature eBook
Subject:
Large scale systems.
Online resource:
https://doi.org/10.1007/978-3-030-22788-3
ISBN:
9783030227883$q(electronic bk.)
Large scale optimization in supply chains and smart manufacturingtheory and applications /
Large scale optimization in supply chains and smart manufacturing
theory and applications /[electronic resource] :edited by Jesus M. Velasquez-Bermudez, Marzieh Khakifirooz, Mahdi Fathi. - Cham :Springer International Publishing :2019. - xxi, 282 p. :ill., digital ;24 cm. - Springer optimization and its applications,v.1491931-6828 ;. - Springer optimization and its applications ;v. 3..
In this book, theory of large scale optimization is introduced with case studies of real-world problems and applications of structured mathematical modeling. The large scale optimization methods are represented by various theories such as Benders' decomposition, logic-based Benders' decomposition, Lagrangian relaxation, Dantzig -Wolfe decomposition, multi-tree decomposition, Van Roy' cross decomposition and parallel decomposition for mathematical programs such as mixed integer nonlinear programming and stochastic programming. Case studies of large scale optimization in supply chain management, smart manufacturing, and Industry 4.0 are investigated with efficient implementation for real-time solutions. The features of case studies cover a wide range of fields including the Internet of things, advanced transportation systems, energy management, supply chain networks, service systems, operations management, risk management, and financial and sales management. Instructors, graduate students, researchers, and practitioners, would benefit from this book finding the applicability of large scale optimization in asynchronous parallel optimization, real-time distributed network, and optimizing the knowledge-based expert system for convex and non-convex problems.
ISBN: 9783030227883$q(electronic bk.)
Standard No.: 10.1007/978-3-030-22788-3doiSubjects--Topical Terms:
247726
Large scale systems.
LC Class. No.: TA168 / .L37 2019
Dewey Class. No.: 620.001171
Large scale optimization in supply chains and smart manufacturingtheory and applications /
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In this book, theory of large scale optimization is introduced with case studies of real-world problems and applications of structured mathematical modeling. The large scale optimization methods are represented by various theories such as Benders' decomposition, logic-based Benders' decomposition, Lagrangian relaxation, Dantzig -Wolfe decomposition, multi-tree decomposition, Van Roy' cross decomposition and parallel decomposition for mathematical programs such as mixed integer nonlinear programming and stochastic programming. Case studies of large scale optimization in supply chain management, smart manufacturing, and Industry 4.0 are investigated with efficient implementation for real-time solutions. The features of case studies cover a wide range of fields including the Internet of things, advanced transportation systems, energy management, supply chain networks, service systems, operations management, risk management, and financial and sales management. Instructors, graduate students, researchers, and practitioners, would benefit from this book finding the applicability of large scale optimization in asynchronous parallel optimization, real-time distributed network, and optimizing the knowledge-based expert system for convex and non-convex problems.
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based on 0 review(s)
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EB TA168 .L322 2019 2019
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https://doi.org/10.1007/978-3-030-22788-3
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