The Impact of Emerging Technologies on Computer Science and Operations Research / Edition 1by Stephen G. Nash
Pub. Date: 02/28/1995
Publisher: Springer US
The emergence of high-performance computers and sophisticated software tech nology has led to significant advances in the development and application of operations research. In turn, the growing complexity of operations research models has posed an increasing challenge to computational methodology and computer technology. This volume focuses on recent advances in the fields of Computer Science and Operations Research, on the impact of technologi cal innovation on these disciplines, and on the close interaction between them. The papers cover many relevant topics: computational probability; design and analysis of algorithms; graphics; heuristic search and learning; knowledge-based systems; large-scale optimization; logic modeling and computation; modeling languages; parallel computation; simulation; and telecommunications. 1 This volume developed out of a conference held in Williamsburg, Virginia, January 5-7, 1994. It was sponsored by the Computer Science Technical Section of the Operations Research Society of America. The conference was attended by over 120 people from across the United States, and from many other countries. We would like to take this opportunity to thank the participants of the con ference, the authors, the anonymous referees, and the publisher for helping produce this volume. We express our special thanks to Bill Stewart and Ed Wasil for serving as Area Editors.
- Springer US
- Publication date:
- Operations Research/Computer Science Interfaces Series, #4
- Edition description:
- Product dimensions:
- 6.10(w) x 9.25(h) x 0.03(d)
Table of ContentsPreface. 1. An Upper Bound Suitable for Parallel Vector Processing for the Objective Function in a Class of Stochastic Optimization Problems; K.A. Ariyawansa. 2. On Embedded Languages, Meta-Level Reasoning, and Computer-Aided Modeling; H.K. Bhargava, S.O. Kimbrough. 3. Mapping Tasks to Processors to Minimize Communication Time in a Multiprocessor System; J. Chakrapani, J. Skorin-Kapov. 4. Refinements to the So-called Simple Approximations for the Bulk-Arrival Queues: MX/G/1; M.L. Chaudhry. 5. A Nearly Asynchronous Parallel LP-Based Algorithm for the Convex Hull Problem in Multidimensional Space; J.H. Dulá, R.V. Helgason, N. Venugopal. 6. A Dynamically Generated Rapid Response Capacity Planning Model for Semiconductor Fabrication Facilities; K. Fordyce, G. Sullivan. 7. Queueing Analysis in TK Solver (QTK); D. Gross, C.M. Harris. 8. On-Line Algorithms for a Single Machine Scheduling Problem; Weizhen Mao, R.K. Kincaid, A. Rifkin. 9. Modeling Experience Using Multivariate Statistics; J.H. May, L.G. Vargas. 10. Optimal Spare Parts Allocation and Industrial Applications; W. Mergenthaler, S. Felgenhauer, P. Hardie, M. Groh, J. Lugger. 11. A C++ Class Library for Mathematical Programming; S.S. Nielsen. 12. Integrating Operations Research and Neural Networks for Vehicle Routing; J.-Y. Potvin, C. Robillard. 13. Using Artificial Intelligence to Enhance Model Analysis; R. Sharda, D.M. Steiger. 14. Solving Quadratic Assignment Problems Using the Reverse Elimination Method; S. Voß. 15. Neural Networks for HeuristicSelection: an Application in Resource-Constrained Project Scheduling; Dan Zhu, R. Padman.
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