Optimization in Large Scale Problems

Industry 4.0 and Society 5.0 Applications
 
 
Springer (Verlag)
  • erschienen am 20. November 2019
  • |
  • XI, 340 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Wasserzeichen-DRM | Systemvoraussetzungen
978-3-030-28565-4 (ISBN)
 

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.

1st ed. 2019
  • Englisch
  • Cham
  • |
  • Schweiz
Springer International Publishing
  • 43
  • |
  • 44 farbige Abbildungen, 41 farbige Tabellen, 43 s/w Abbildungen
  • |
  • 43 schwarz-weiße und 44 farbige Abbildungen, 41 farbige Tabellen, Bibliographie
  • 7,42 MB
978-3-030-28565-4 (9783030285654)
10.1007/978-3-030-28565-4
weitere Ausgaben werden ermittelt

Mahdi Fathi is a Postdoctoral Associate at the Department of Industrial and Systems Engineering at Mississippi State University. He received his BS and MS from the Department of Industrial Engineering, Amirkabir University of Technology (Tehran Polytechnic) and Ph.D. from Iran University of Science and Technology, Tehran, Iran in 2006, 2008 and 2013, respectively. He is the recipient of three postdoctoral fellowships and was a visiting scholar at Center for Applied Optimization, Dept. of Industrial and Systems Engineering-University of Florida (USA) and Dept. of Electrical Engineering-National Tsing Hua University in Taiwan. He worked at Optym as a senior systems engineer and at A Model Of Reality Inc. as a system design engineer in the USA and several other companies in different industry sectors. Prof. Fahti is an active member of several societies and institutions and serves on the editorial board of several journals. His research interests include Queuing Theory and Its Applications; Stochastic Process; Optimization; Artificial Intelligent; Uncertain Quantification; Smart Manufacturing & Industry 4.0; Reliability with their applications in Health Care, Bio-medicine, Agriculture & Energy.

Marzieh Khaki?rooz has a Ph.D. in Industrial Engineering and Engineering Management and an M.S. degree in Industrial Statistics from the National Tsing Hua University (NTHU), Hsinchu, Taiwan. Currently, she is an assistant professor at school of science and engineering, Tecnológico de Monterrey, Monterrey, Mexico. Khaki?rooz has outstanding practical experience from her various global consultancies for high-tech industries. Her research interests include the application of optimization in smart manufacturing, Industry 4.0, decision making and machine teaching. She is active member of System Dynamic Society, Institute of Electrical and Electronics Engineers (IEEE), and Institute of Industrial and Systems Engineers (IISE).

Panos M. Pardalos serves as distinguished professor of industrial and systems engineering at the University of Florida. Additionally, he is the Paul and Heidi Brown Preeminent Professor of industrial and systems engineering. Pardalos is also an affiliated faculty member of the computer and information science department, the Hellenic Studies Center, and the biomedical engineering program. Additionally, he serves as the director of the Center for Applied Optimization. Pardalos is a world leading expert in global and combinatorial optimization. His recent research interests include network design problems, optimization in telecommunications, e-commerce, data mining, biomedical applications, and massive computing. Pardalos is a prolific author who lectures all over the world. He is the recipient of a multitude of fellowships and awards, the most recent of which is the Humboldt Research Award (2018).

Part 1.- Risk Based Optimization of Integrated Fabrication/Fulfillment Supply Chains (Nasim Nezamoddini, Faisal Aqlan, Amirhosein Gholami).- µ?-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 Sandıkçı and Osman Y. Özaltın).- 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).
DNB DDC Sachgruppen

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