Optimization in large scale problems : Industry 4.0 and Society 5.0 applications
著者
書誌事項
Optimization in large scale problems : Industry 4.0 and Society 5.0 applications
(Springer optimization and its applications, v. 152)
Springer, c2019
大学図書館所蔵 全2件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references
内容説明・目次
内容説明
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.
目次
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).
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