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| ROilald L，Rardin，美国数学规划和优化理论及其应用运筹学方面的著名学者。于1974年从佐治亚理工学院获得博士学位，长期任普度大学工业工程系教授、普度大学能源建模研究组(PEMRG)主任和Regenstrief医疗保健工程研究中心(RCHE)主任，还曾担任美国国家自然科学基金会运筹学和服务企业项目主任。Raldin教授的教学和研究重点是大规模优化的建模与算法，包括在医疗保健系统、交通与物流系统以及能源规划方面的应用。他曾四次荣获普度大学在工业工程方面的Pritsker杰出教学奖，是美国工业工程学会、运筹学与管理科学学会以及数学规划学会的会员。Rardin教授现已加入阿肯色大学。 |
| 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 SELECTED ANSWERS INDEX |
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