Optimization in large scale problems : Industry 4.0 and Society 5.0 applications
Author(s)
Bibliographic Information
Optimization in large scale problems : Industry 4.0 and Society 5.0 applications
(Springer optimization and its applications, v. 152)
Springer, c2019
Available at 2 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references
Description and Table of Contents
Description
This volume provides resourceful thinking and insightful management solutions to the many challenges that decision makers face in their predictions, preparations, and implementations of the key elements that our societies and industries need to take as they move toward digitalization and smartness. The discussions within the book aim to uncover the sources of large-scale problems in socio-industrial dilemmas, and the theories that can support these challenges. How theories might also transition to real applications is another question that this book aims to uncover. In answer to the viewpoints expressed by several practitioners and academicians, this book aims to provide both a learning platform which spotlights open questions with related case studies.
The relationship between Industry 4.0 and Society 5.0 provides the basis for the expert contributions in this book, highlighting the uses of analytical methods such as mathematical optimization, heuristic methods, decomposition methods, stochastic optimization, and more. The book will prove useful to researchers, students, and engineers in different domains who encounter large scale optimization problems and will encourage them to undertake research in this timely and practical field. The book splits into two parts. The first part covers a general perspective and challenges in a smart society and in industry. The second part covers several case studies and solutions from the operations research perspective for large scale challenges specific to various industry and society related phenomena.
Table of Contents
Part 1.- Risk Based Optimization of Integrated Fabrication/Fulfillment Supply Chains (Nasim Nezamoddini, Faisal Aqlan, Amirhosein Gholami).- -EGF: A New Multi-Thread Implementation Algorithm for the Packing Problem inspired by Electromagnetic Fields and Gravitational Effects (Felix Martinez-Rios and Jose Antonio Marmolejo-Saucedo).- The Vector Optimization Method for Solving Integer Linear Programming Problems. Application for the Unit Commitment Problem in Electrical Power Production (Lenar Nizamov).- An Outer Approximation Algorithm for Capacitated Disassembly Scheduling Problem with Parts Commonality and Random Demand (Kanglin Liu, MengWang, Zhi-Hai Zhang),- Multi-Tree Decomposition Methods for Large-Scale Mixed Integer Nonlinear Optimization (Ivo Nowak, Pavlo Muts, and Eligius M.T. Hendrix).- An Embarrassingly Parallel Method for Large-Scale Stochastic Programs (Burhaneddin Sandikci and Osman Y. OEzaltin).- Part 2.- How to Effectively Train Large Scale Machines (Avan Samareh, Mahshid Salemi Parizi).- A Graph Search Algorithm for Solving Large Scale Median Problems on Real Road Networks (Saeed Ghanbartehrania, J. David Porterb, Mahnoush Samadi Dinania).- Solving Large Scale Optimization Problems in the Transportation Industry and Beyond through Column Generation (Yanqi Xu).- Dynamic Energy Management (Nicholas Moehle, Enzo Busseti, Stephen Boyd, and Matt Wytock).- An Approximation-Based Approach for Chance-Constrained Vehicle Routing and Air Traffic Control Problems (Lijian Chen).- Algorithmic Mechanism Design for Collaboration in Large-scale Transportation Networks (Minghui Lai and Xiaoqiang Cai).- Kantorovich-Rubinstein Distance Minimization: Application to Location Problems (Viktor Kuzmenko, Stan Uryasev).
by "Nielsen BookData"