Interfaces in Computer Science and Operations Research: Advances in Metaheuristics, Optimization, and Stochastic Modeling Technologies

Overview

The disciplines of Computer Science and Operations Research have been linked since their origins and each have contributed to the dramatic advances of the other. This volume examines some of the recent advances resulting from the confluence between these two technical communities. In the process the book brings together the original work of academic researchers and practitioners in Computer Science, Operations Research, Management Science, Artificial Intelligence, Neural Networks, and related fields. More ...

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Overview

The disciplines of Computer Science and Operations Research have been linked since their origins and each have contributed to the dramatic advances of the other. This volume examines some of the recent advances resulting from the confluence between these two technical communities. In the process the book brings together the original work of academic researchers and practitioners in Computer Science, Operations Research, Management Science, Artificial Intelligence, Neural Networks, and related fields. More specifically, it explores the connections between these two areas with an array of advances in metaheuristics, neural networks, optimization, shastic analysis, constraint logic programming, and decision support modeling.
The book's principal theme centers on the shared modeling technologies of Computer Science and Operations Research and applies these methodologies to a variety of applications in manufacturing, logistics, finance, and telecommunications.

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Product Details

  • ISBN-13: 9781461368373
  • Publisher: Springer US
  • Publication date: 12/31/2012
  • Series: Operations Research/Computer Science Interfaces Series , #7
  • Edition description: Softcover reprint of the original 1st ed. 1997
  • Edition number: 1
  • Pages: 442
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.93 (d)

Table of Contents

Part I: Metaheuristics. 1. Tabu Search and Adaptive Memory Programming - Advances, Applications and Challenges; F. Glover. Part II: Neural Networks. 2. Neural Networks in Practice: Survey Results; B.L. Golden, et al. 3. Tractable Theories for the Synthesis of Neural Networks; V. Chandru, et al. 4. Neural Network Training via Quadratic Programming; T.B. Trafalis, N.P. Couellan. 5. A Neural Network Model for Predicting Atlantic Hurricane Activity; O. Kwon, et al. Part III: Optimization. 6. An Efficient Dual Simplex Optimizer for Generalized Networks; J.L. Kennington, R.A. Mohammed. 7. Solving Large-Scale Crew Scheduling Problems; H.D. Chu, et al. Part IV: Constraint and Logic Programming. 8. HOURIA III: A Solver for Hierarchical Systems of Functional Constraints, Planning the Solution Graph for a Weighted Sum Criterion; M. Bouzoubaa, et al. 9. Some Recent Developments of Using Logical Analysis for Inferring a Boolean Function with a Few Clauses; E. Triantaphyllou, et al. Part V: Shastic Performance Analysis. 10. Computational Analysis of a G/G/1 Queue with Vacations and Exhaustive Service; H. Li, Y. Zhu. 11. Stability and Queuing-Time Analysis of a Reader-Writer Queue with Writer Preference; L.C. Puryear, V.G. Kulkarni. 12. Importance Sampling in Lattice Pricing Models; S.S. Nielsen. Part VI: Modeling and Decision Support. 13. Data and Optimization Modelling: A Tool for Elicitation and Browsing (DOME); H. Mousavi, et al. 14. Enhancing User Understanding via Model Analysis in a Decision Support System; D.M. Steiger. Part VII: Applications in Manufacturing, Logistics, and Finance. 15. Bank Failure Prediction Using DEA to Measure Management Quality; R.S. Barr, T.F. Siems. 16. A Cooperative Multi-Agent Approach to Constrained Project Scheduling; D. Zhu, R. Padman. 17. Scheduling a Flow Shop to Minimize the Maximal Lateness under Arbitrary Precedence Constraints; J. Józefowska, A. Zimmiak. 18. A Genetic Programming Approach for Heuristic Selection in Constrained Project Scheduling; R. Padman, S.F. Roehrig. 19. Coupling a Greedy Route Construction Heuristic with A Genetic Algorithm for the Vehicle Routing Problem with Time Windows; J.-Y. Potvin, F. Guertin.

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