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Optimization in Operations Research (2nd…
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Optimization in Operations Research (2nd Edition) (udgave 2016)

af Ronald L. Rardin (Forfatter)

MedlemmerAnmeldelserPopularitetGennemsnitlig vurderingSamtaler
291809,416 (3.25)Ingen
This book is specifically designed to change the way deterministic optimization is taught to introductory students. Toward this end, it exposes students to the broad scope of the topic, reinforces the basic principles, sparks students' enthusiasm about the field, provides tools of immediate relevance and develops the skills necessary to use those tools.… (mere)
Medlem:gabriel301
Titel:Optimization in Operations Research (2nd Edition)
Forfattere:Ronald L. Rardin (Forfatter)
Info:Pearson (2016), Edition: 2, 1184 pages
Samlinger:Dit bibliotek
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Nøgleord:Ingen

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Optimization in Operations Research af Ronald L. Rardin

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Indeholder "Preface", "About the Author", "Chapter 1. Problem Solving With Mathematical Models", " 1.1 OR Application Stories", " 1.2 Optimization and the Operations Research Process", " 1.3 System Boundaries, Sensitivity Analysis, Tractability and Validity", " 1.4 Descriptive Models and Simulation", " 1.5 Numerical Search and Exact versus Heuristic Solutions", " 1.6 Deterministic versus Stochastic Models", " 1.7 Perspectives", " Exercises", "Chapter 2. Deterministic Optimization Models in Operations Research", " 2.1 Decision Variables, Constraints, and Objective Functions", " 2.2 Graphic Solution and Optimization Outcomes", " 2.3 Large-Scale Optimization Models and Indexing", " 2.4 Linear and Nonlinear Programs", " 2.5 Discrete or Integer Programs", " 2.6 Multiobjective Optimization Models", " 2.7 Classification Summary", " Exercises", "Chapter 3. Improving Search", " 3.1 Improving Search, Local and Global Optima", " 3.2 Search with Improving and Feasible Directions", " 3.3 Algebraic Conditions for Improving and Feasible Directions", " 3.4 Unimodel and Convex Model Forms Tractable for Improving Search", " 3.5 Searching and Starting Feasible Solutions", " Exercises", "Chapter 4. Linear Programming Models", " 4.1 Allocation Models", " 4.2 Blending Models", " 4.3 Operations Planning Models", " 4.4 Shift Scheduling and Staff Planning Models", " 4.5 Time-Phased Models", " 4.6 Models with Linearizable Nonlinear Objectives", "Exercises", "Chapter 5. Simplex Search for Linear Programming", " 5.1 LP Optimal Solutions and Standard Form", " 5.2 Extreme-Point Search and Basic Solutions", " 5.3 The Simplex Algorithm", " 5.4 Dictionary and Tableau Representations of Simplex", " 5.5 Two Phase Simplex", " 5.6 Degeneracy and Zero-Length Simplex Steps", " 5.7 Convergence and Cycling with Simplex", " 5.8 Doing It Efficiently: Revised Simplex", " Exercises", "Chapter 6. Interior Point Methods for Linear Programming", " 6.1 Searching through the Interior", " 6.2 Scaling with the Current Solution", " 6.3 Affine Scaling Search", " 6.4 Log Barrier Methods for Interior Point Search", " 6.5 Dual and Primal-Dual Extensions", " Exercises", "Chapter 7. Duality and Sensitivity in Linear Programming", " 7.1 Generic Activities versus Resources Perspective", " 7.2 Qualitative Sensitivity to Changes in Model Coefficients", " 7.3 Quantifying Sensitivity to Changes in LP Model Coefficients: A Dual Model", " 7.4 Formulating Linear Programming Duals", " 7.5 Primal-to-Dual Relationships", " 7.6 Computer Outputs and What If Changes of Single Parameters", " 7.7 Bigger Model Changes, Reoptimization, and Parametric Programming", " Exercises", "Chapter 8. Multiobjective Optimization and Goal Programming", " 8.1 Multiobjective Optimization Models", " 8.2 Efficient Points and the Efficient Frontier", " 8.3 Preemptive Optimization and Weighted Sums of Objectives", " 8.4 Goal Programming", " Exercises", "Chapter 9. Shortest Paths and Discrete Dynamic Programming", " 9.1 Shortest Path Models", " 9.2 Dynamic Programming Approach to Shortest Paths", " 9.3 Shortest Paths From One Node to All Others: Bellman-Ford", " 9.4 Shortest Paths From All Nodes to All Others: Floyd-Warshall", " 9.5 Shortest Path From One Node to All Others With Costs Nonnegative: Dijkstra", " 9.6 Shortest Paths From One Node to All Others in Acyclic Digraphs", " 9.7 CPM Project Scheduling and Longest Paths", " 9.8 Discrete Dynamic Programming Models", " Exercises", "Chapter 10. Network Flows", " 10.1 Graphs, Networks, and Flows", " 10.2 Cycle Directions for Network Flow Search", " 10.3 Rudimentary Cycle Direction Search Algorithms for Network Flows", " 10.4 Integrality of Optimal Network Flows", " 10.5 Transportation and Assignment Models", " 10.6 Other Single-Commodity Network Flow Models", " 10.7 Network Simplex Algorithm for Optimal Flows", " 10.8 Cycle Canceling Algorithms for Optimal Flows", " 10.9 Multicommodity and Gain/Loss Flows", " Exercises", "Chapter 11. Discrete Optimization Models", " 11.1 Lumpy Linear Programs and Fixed Charges", " 11.2 Knapsack and Capital Budgeting Models", " 11.3 Set Packing, Covering, and Partitioning Models", " 11.4 Assignment and Matching Models", " 11.5 Traveling Salesman and Routing Models", " Exercises", "Chapter 12. Discrete Optimization Methods", " 12.1 Solving by Total Enumeration", " 12.2 Relaxations of Discrete Optimization Models and Their Uses", " 12.3 Stronger LP relaxations, Valid Inequalities, and Lagrangian Relaxations", " 12.4 Branch and Bound Search", " 12.5 Rounding, Parent Bounds, Enumerations Sequences, and Stopping Early in Branch and Bound", " 12.6 Improving Search Heuristics for Discrete Optimization INLPs", " 12.7 Tabu, Simulated Annealing, and Genetic Algorithm Extensions of Improving Search", " 12.8 Constructive Heuristics", " Exercises", "Chapter 13. Unconstrained Nonlinear Programming", " 13.1 Unconstrained Nonlinear Programming Models", " 13.2 One-Dimensional Search", " 13.3 Derivatives, Taylor Series, and Conditions for Local Optima", " 13.4 Convex Concave Functions and Global Optimality", " 13.5 Gradient Search", " 13.6 Newton's Method", " 13.7 Quasi-Newton Methods and BFGS Search", " 13.8 Optimization without Derivatives and Nelder-Mead", " Exercises", "Chapter 14. Constrained Nonlinear Programming", " 14.1 Constrained Nonlinear Programming Models", " 14.2 Convex, Separable, Quadratic and Posynomial Geometric Programming Special NLP Forms", " 14.3 Lagrange Multiplier Methods", " 14.4 Karush-Kuhn-Tucker Optimality Conditions", " 14.5 Penalty and Barrier Methods", " 14.6 Reduced Gradient Algorithms", " 14.7 Quadratic Programming Methods", " 14.8 Separable Programming Methods", " 14.9 Posynomial Programming Methods", " Exercises", "Selected Answers", "Index".

Ganske spændende indblik i optimeringsproblemer i størrelse stor. Og ja, posynomial er ikke en stavefejl. ( )
  bnielsen | Mar 2, 2016 |
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This book is specifically designed to change the way deterministic optimization is taught to introductory students. Toward this end, it exposes students to the broad scope of the topic, reinforces the basic principles, sparks students' enthusiasm about the field, provides tools of immediate relevance and develops the skills necessary to use those tools.

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