Optimization and Industry: New Frontiers / Edition 1by Panos M. Pardalos
Pub. Date: 03/31/2003
Publisher: Springer US
Optimization from Human Genes to Cutting Edge Technologies The challenges faced by industry today are so complex that they can only be solved through the help and participation of optimization ex perts. For example, many industries in e-commerce, finance, medicine, and engineering, face several computational challenges due to the mas sive data sets that… See more details below
Optimization from Human Genes to Cutting Edge Technologies The challenges faced by industry today are so complex that they can only be solved through the help and participation of optimization ex perts. For example, many industries in e-commerce, finance, medicine, and engineering, face several computational challenges due to the mas sive data sets that arise in their applications. Some of the challenges include, extended memory algorithms and data structures, new program ming environments, software systems, cryptographic prools, storage devices, data compression, mathematical and statistical methods for knowledge mining, and information visualization. With advances in computer and information systems technologies, and many interdisci plinary efforts, many of the "data avalanche challenges" are beginning to be addressed. Optimization is the most crucial component in these efforts. Nowadays, the main task of optimization is to investigate the cutting edge frontiers of these technologies and systems and find the best solutions for their realization. Optimization principles are evident in nature (the perfect optimizer) and appeared early in human history. Did you ever watch how a spider catches a fly or a mosquito? Usually a spider hides at the edge of its net. When a fly or a mosquito hits the net the spider will pick up each line in the net to choose the tense line? Some biologists explain that the line gives the shortest path from the spider to its prey.
Table of Contents
Preface. 1. Addressing the Solution of Nonsmooth Optimization Problems Arising in Industry; S.K. Filipowski, M.E. Berge, D.J. Pierce, J. Wu. 2. Heuristic Approaches to Production-Inventory-Distribution Problems in Supply Chains; B. Eksioglu, A. Migdalas, P.M. Pardalos. 3. Optimization in the Automotive Industry; D.R. Jones. 4. Combinatorial Optimization in Telecommunications; M.G.C. Resende. 5. Air Traffic Management at Sydney with Cancellations and Curfew Penalties; J.A. Filar, P. Manyem, M.S. Visser, K. White. 6. Nash Equilibria in Electricity Markets with Fixed Prices Points; E.J. Anderson, H. Xu. 7. Metaheuristic Algorithms for the Strip Packing Problem; M. Iori, S. Maertello, M. Monaci. 8. On the Role of Nonlocal Correlations in Optimization; G. Korotkikh, V. Korotkikh. 9. Global Minimization of Lennard-Jones Clusters by a Two-Phase Monotonic Method; M. Locatelli, F. Schoen. 10. Optimal Placement of Backbone Structures in a Rural Telecommunication Network; M.M. Ali, C. Opperman, B. Thomas, E.M. Wolmarans. 11. Computational Methods for Epilepsy Diagnosis. Visual Perception and EEG; R. Ruseckaite. 12. Evolving Agents for Global Optimization; Baolin Wu, Xinghuo Yu. 13. A Decomposition Approach for Optimal Processing of Telecommunications and Cyberspace Systems; V. Korotkikh, N. Patson. 14. Outsourcing Decision Support: A Web-Based Approach; M.J. Nealon, M.E. Johnston. 15. Intelligent Agent Application within Distributed Networking Simulation System; A.M. Anvar.
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