书籍详情
运筹学：决策方法
作者：（美）Wayne L.Winston著
出版社：清华大学出版社
出版时间：2004-01-01
ISBN：9787302077268
定价：¥25.00
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内容简介
由WINSTON编纂的《OPERATIONS RESEARCH》（第3版）一书系统全面地讲解了运筹学的有关内容，是一本在境外被普遍使用的教科书。其特点是重点介绍各种原理和方法的基本概念及其应用、而省略抽象的推理及详细的证明过程，同时每个章节都有丰富的例子和大量的练习题。《运筹学：决策方法（第3版）（影印版）》由WINSTON一书中的第8，13，14，15，21章组成，主要内容包括：网络模型、不确定性环境下的决策、多目标决策、对策论、概率动态规划。本书可作为工科类及管理类的本科生教材。
作者简介
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目录
1 Network Models
1.1 Basic Definitions 1
1.2 Shortest Path Problems 2
1.3 Maximum Flow Problems 8
1.4 CPM and PERT 22
1.5 Minimum Cost Network Flow Problems 42
1.6 Minimum Spanning Tree Problems 49
1.7 The Network Simplex Method 53
2 Decision Making Under Uncertainty
2.1 Decision Criteria 71
2.2 Utility Theory 75
2.3 Decision Trees 89
2.4 Bayes' Rule and Decision Trees I00
2.5 Decision Making with the Normal Distribution 106
3 Decision Making with Multiple Objectives
3.1 Multiattribute Decision Making in the Absence of Uncertainty: Goal Programming 116
3.2 Multiattribute Utility Functions 132
3.3 The Analytic Hierarchy Process 142
3.4 Pareto Optimality and Tradeoff Curves 153
4 Game Theory
4.1 Two-Person Zero-Sum and Constant-Sum Games: Saddle Points 168
4.2 Two-Person Zero-Sum Games: Randomized Strategies, Domination, and Graphical Solution 172
4.3 Linear Programming and Zero-Sum Games 181
4.4 Two-Person Non-Constant-Sum Games 194
4.5 Introduction to n-Person Game Theory 198
4.6 The Core of an n-Person Game 200
4.7 The Shapley Value 204
5 Probabilistic Dynamic Programming
5.1 When Current Stage Costs Are Uncertain, but the Next Period's State Is Certain 214
5.2 A Probabilistic Inventory Model 217
5.3 How to Maximize the Probability of a Favorable Event Occurring 221
5.4 Further Examples of Probabilistic Dynamic Programming Formulations 228
5.5 Markov Decision Processes 235
1.1 Basic Definitions 1
1.2 Shortest Path Problems 2
1.3 Maximum Flow Problems 8
1.4 CPM and PERT 22
1.5 Minimum Cost Network Flow Problems 42
1.6 Minimum Spanning Tree Problems 49
1.7 The Network Simplex Method 53
2 Decision Making Under Uncertainty
2.1 Decision Criteria 71
2.2 Utility Theory 75
2.3 Decision Trees 89
2.4 Bayes' Rule and Decision Trees I00
2.5 Decision Making with the Normal Distribution 106
3 Decision Making with Multiple Objectives
3.1 Multiattribute Decision Making in the Absence of Uncertainty: Goal Programming 116
3.2 Multiattribute Utility Functions 132
3.3 The Analytic Hierarchy Process 142
3.4 Pareto Optimality and Tradeoff Curves 153
4 Game Theory
4.1 Two-Person Zero-Sum and Constant-Sum Games: Saddle Points 168
4.2 Two-Person Zero-Sum Games: Randomized Strategies, Domination, and Graphical Solution 172
4.3 Linear Programming and Zero-Sum Games 181
4.4 Two-Person Non-Constant-Sum Games 194
4.5 Introduction to n-Person Game Theory 198
4.6 The Core of an n-Person Game 200
4.7 The Shapley Value 204
5 Probabilistic Dynamic Programming
5.1 When Current Stage Costs Are Uncertain, but the Next Period's State Is Certain 214
5.2 A Probabilistic Inventory Model 217
5.3 How to Maximize the Probability of a Favorable Event Occurring 221
5.4 Further Examples of Probabilistic Dynamic Programming Formulations 228
5.5 Markov Decision Processes 235
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