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Optimization in Operations Research 2nd Edition By Ronald Rardin

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Pages: 1,187
File size: 12.76 MB
Format: Digital PDF
Course: Education
Category: eBook[PDF]

About this ebook

The Second Edition of Optimization in Operations Research by Ronald L. Rardin covers the full spectrum of mathematical programming, algorithms, and modeling formulations. [1, 2]

The book is structured into the following 17 chapters: [1]

Foundational Concepts & Modeling

Chapter 1: Problem Solving with Mathematical Models – Introduction to operations research, engineering problem solving, and decision-making frameworks. [1, 2, 3]
Chapter 2: Deterministic Optimization Models in Operations Research – Structuring constraints, objective functions, and parameters in deterministic contexts. [1, 2, 3]
Chapter 3: Improving Search – The foundational philosophy of iterative search algorithms and moving from feasible points to local optima. [1]

Linear Programming (LP)

Chapter 4: Linear Programming Models – Formulating classic linear allocation, blending, and production models.
Chapter 5: Simplex Search for Linear Programming – Mechanics, algebra, and geometry of the traditional Simplex algorithm.
Chapter 6: Duality, Sensitivity, and Optimality in Linear Programming – Exploring shadow prices, dual formulations, and sensitivity analysis.
Chapter 7: Interior Point Methods for Linear Programming – Modern alternative algorithms to Simplex, including primal-dual barrier methods. [1, 2, 3]

Network Flows & Dynamic Programming

Chapter 8: Multiobjective Optimization and Goal Programming – Managing competing objectives and Pareto optimality.
Chapter 9: Shortest Paths and Discrete Dynamic Programming – Sequential decision problems, Bellman-Ford, Floyd-Warshall, and Dijkstra's algorithms.
Chapter 10: Network Flows and Graphs – Transportation models, transshipment, assignment problems, and maximum flow algorithms. [1, 2, 3, 4]

Discrete & Integer Optimization

Chapter 11: Discrete Optimization Models – Formulating combinatorics, binary choices, and integer constraints.
Chapter 12: Exact Discrete Optimization Methods – Branch-and-bound algorithms, cutting planes, and exact enumeration.
Chapter 13: Large-Scale Optimization Methods – Column generation, Benders decomposition, and Dantzig-Wolfe techniques.
Chapter 14: Computational Complexity Theory – Understanding P vs. NP classes, algorithmic efficiency, and scaling constraints.
Chapter 15: Heuristic Methods for Approximate Discrete Optimization – Metaheuristics, local search, simulated annealing, genetic algorithms, and tabu search. [1, 2, 3, 4, 5]

Nonlinear Programming (NLP)

Chapter 16: Unconstrained Nonlinear Programming – Gradient descent, Newton's method, and line search styles for curved functions.
Chapter 17: Constrained Nonlinear Programming – KKT conditions, reduced gradient search, quadratic programming, and sequential quadratic programming (SQP). [1, 2]