运筹学：优化模型与算法
出版时间： 2007

内容简介
　　本书是一本适应当今运筹学发展趋势的优秀的综合性入门教材，主要特点是重视建模和算法的结合，引入了相关的建模工具以及用其进行模型开发的基本技巧。全书共分14章，前3章介绍数学模型的问题求解和改进搜索的基本概念与原理，其余内容则覆盖了确定型优化领域的几乎全部内容，除了传统的线性规划的模型、算法、对偶理论和灵敏度分析等内容以外，还包括了网络流、整数／组合优化、非线性规划和目标规划等领域的基本模型和主要算法。此外，本书还包含了遗传算法、模拟退火、禁忌搜索和分支切割算法等前沿内容。全书采用统一的理论框架，以简单的“改进搜索”思路贯穿始终，全面且循序渐进地演绎了各种优化算法和方法，包括传统的单纯形法、牛顿法、网络流算法以及各种启发式算法，使读者感受到每次引入的新算法都建立在以往算法的基础上，直观且逻辑性强，易于理解。本书收录了丰富的实际案例，并有大量上机习题，便于理论结合实践。

CHAPTER I 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
　5.9 Simplex with Simple Upper and Lower Bounds
　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 MULTIOBYECTIVE 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
CHAPTER 10 NETWORK FLOWS
CHAPTER 11 DISCRETE OPTIMIZATION MODELS
CHAPTER 12 DISCRETE OPTIMIZATION METHODS
CHAPTER 13 UNCONSTRAINED NONLNEAR PROGRAMMING
CHAPTER 14 CONSTRAINED NONLINEAR PROGRAMMING
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SELECTED ANSWERS
INDEX