Operations Research Applications and Algorithms pdf epub mobi txt 电子书 下载 2026
- 建模
- 运筹学
- 优化算法
- 数学建模
- 应用研究
- 线性规划
- 整数规划
- 网络优化
- 启发式算法
- 模拟优化
- 决策分析
具体描述
(Wayne Winston 4th.Edition,2003)
The market-leading textbook for the course, Winston's OPERATIONS RESEARCH owes much of its success to its practical orientation and consistent emphasis on model formulation and model building. It moves beyond a mere study of algorithms without sacrificing the rigor that faculty desire. As in every edition, Winston reinforces the book's successful features and coverage with the most recent developments in the field. The Student Suite CD-ROM, which now accompanies every new copy of the text, contains the latest versions of commercial software for optimization, simulation, and decision analysis.
作者简介
目录信息
1. INTRODUCTION TO MODEL BUILDING. An Introduction to Modeling. The Seven-Step Model-Building Proces*****amples.
2. BASIC LINEAR ALGEBRA. Matrices and Vectors. Matrices and Systems of Linear Equations. The Gauss-Jordan Method for Solving Systems of Linear Equations. Linear Independence and Linear Dependence. The Inverse of a Matrix. Determinants.
3. INTRODUCTION TO LINEAR PROGRAMMING. What is a Linear Programming Problem? The Graphical Solution of Two-Variable Linear Programming Problems. Special Cases. A Diet Problem. A Work-Scheduling Problem. A Capital Budgeting Problem. Short-term Financial Planning. Blending Problems. Production Process Models. Using Linear Programming to Solve Multiperiod Decision Problems: An Inventory Model. Multiperiod Financial Models. Multiperiod Work Scheduling.
4. THE SIMPLEX ALGORITHM AND GOAL PROGRAMMING. How to Convert an LP to Standard Form. Preview of the Simplex Algorithm. The Simplex Algorithm. Using the Simplex Algorithm to Solve Minimization Problems. Alternative Optimal Solutions. Unbounded LPs. The LINDO Computer Package. Matrix Generators, LINGO, and Scaling of LPs. Degeneracy and the Convergence of the Simplex Algorithm. The Big M Method. The Two-Phase Simplex Method. Unrestricted-in-Sign Variables. Karmarkar's Method for Solving LPs. Multiattribute Decision-Making in the Absence of Uncertainty: Goal Programming. Solving LPs with Spreadsheets.
5. SENSITIVITY ANALYSIS: AN APPLIED APPROACH. A Graphical Introduction to Sensitivity Analysis. The Computer and Sensitivity Analysis. Managerial Use of Shadow Prices. What Happens to the Optimal z-value if the Current Basis is No Longer Optimal?
6. SENSITIVITY ANALYSIS AND DUALITY. A Graphical Introduction to Sensitivity Analysis. Some Important Formulas. Sensitivity Analysis. Sensitivity Analysis When More Than One Parameter is Changed: The 100% Rule. Finding the Dual of an LP. Economic Interpretation of the Dual Problem. The Dual Theorem and Its Consequences. Shadow Prices. Duality and Sensitivity Analysis.
7. TRANSPORTATION, ASSIGNMENT, AND TRANSSHIPMENT PROBLEMS. Formulating Transportation Problems. Finding Basic Feasible Solutions for Transportation Problems. The Transportation Simplex Method. Sensitivity Analysis for Transportation Problems. Assignment Problems. Transshipment Problems.
8. NETWORK MODELS. Basic Definitions. Shortest Path Problems. Maximum Flow Problems. CPM and PERT. Minimum Cost Network Flow Problems. Minimum Spanning Tree Problems. The Network Simplex Method.
9. INTEGER PROGRAMMING. Introduction to Integer Programming. Formulation Integer Programming Problems. The Branch-and-Bound Method for Solving Pure Integer Programming Problems. The Branch-and-Bound Method for Solving Mixed Integer Programming Problems. Solving Knapsack Problems by the Branch-and-Bound Method. Solving Combinatorial Optimization Problems by the Branch-and-Bound Method. Implicit Enumeration. The Cutting Plane Algorithm.
10. ADVANCED TOPICS IN LINEAR PROGRAMMING. The Revised Simplex Algorithm. The Product Form of the Inverse. Using Column Generation to Solve Large-Scale LPs. The Dantzig-Wolfe Decomposition Algorithm. The Simplex Methods for Upper-Bounded Variables. Karmarkar's Method for Solving LPs.
11. NONLINEAR PROGRAMMING. Review of Differential Calculus. Introductory Concepts. Convex and Concave Functions. Solving NLPs with One Variable. Golden Section Search. Unconstrained Maximization and Minimization with Several Variables. The Method of Steepest Ascent. Lagrange Multiples. The Kuhn-Tucker Conditions. Quadratic Programming. Separable Programming. The Method of Feasible Directions. Pareto Optimality and Tradeoff Curves.
12. REVIEW OF CALCULUS AND PROBABILITY. Review of Integral Calculus. Differentiation of Integrals. Basic Rules of Probability. Bayes' Rule. Random Variables. Mean Variance and Covariance. The Normal Distribution. Z-Transforms. Review Problems.
13. DECISION MAKING UNDER UNCERTAINTY. Decision Criteria. Utility Theory. Flaws in Expected Utility Maximization: Prospect Theory and Framing Effects. Decision Trees. Bayes' Rule and Decision Trees. Decision Making with Multiple Objectives. The Analytic Hierarchy Process. Review Problems.
14. GAME THEORY. Two-Person Zero-Sum and Constant-Sum Games: Saddle Points. Two-Person Zero-Sum Games: Randomized Strategies, Domination, and Graphical Solution. Linear Programming and Zero-Sum Games. Two-Person Nonconstant-Sum Games. Introduction to n-Person Game Theory. The Core of an n-Person Game. The Shapley Value.
15. DETERMINISTIC EOQ INVENTORY MODELS. Introduction to Basic Inventory Models. The Basic Economic Order Quantity Model. Computing the Optimal Order Quantity When Quantity Discounts Are Allowed. The Continuous Rate EOQ Model. The EOQ Model with Back Orders Allowed. Multiple Product Economic Order Quantity Models. Review Problems.
16. PROBABILISTIC INVENTORY MODELS. Single Period Decision Models. The Concept of Marginal Analysis. The News Vendor Problem: Discrete Demand. The News Vendor Problem: Continuous Demand. Other One?Period Models. The EOQ with Uncertain Demand: the (r, q) and (s,S models). The EOQ with Uncertain Demand: the Service Level Approach to Determining Safety Stock Level. Periodic Review Policy. The ABC Inventory Classification System. Exchange Curves. Review Problems.
17. MARKOV CHAINS. What is a Stochastic Process. What is a Markov Chain? N-Step Transition Probabilities. Classification of States in a Markov Chain. Steady-State Probabilities and Mean First Passage Times. Absorbing Chains. Work-Force Planning Models.
18.DETERMINISTIC DYNAMIC PROGRAMMING. Two Puzzles. A Network Problem. An Inventory Problem. Resource Allocation Problems. Equipment Replacement Problems. Formulating Dynamic Programming Recursions. The Wagner-Whitin Algorithm and the Silver-Meal Heuristic. Forward Recursions. Using Spreadsheets to Solve Dynamic Programming Problems. Review Problems.
19. PROBABILISTIC DYNAMIC PROGRAMMING. When Current Stage Costs are Uncertain but the Next Period's State is Certain. A Probabilistic Inventory Model. How to Maximize the Probability of a Favorable Event Occurring. Further Examples of Probabilistic Dynamic Programming Formulations. Markov Decision Processes. Review Problems.
20. QUEUING THEORY. Some Queuing Terminology. Modeling Arrival and Service Processes. Birth-Death Processes. M/M/1/GD/o/o Queuing System and the Queuing Formula L=o W, The M/M/1/GD/o Queuing System. The M/M/S/ GD/o/o Queuing System. The M/G/ o/GD/oo and GI/G/o/GD/o/oModels. The M/ G/1/GD/o/o Queuing System. Finite Source Models: The Machine Repair Model. Exponential Queues in Series and Opening Queuing Networks. How to Tell whether Inter-arrival Times and Service Times Are Exponential. The M/G/S/GD/S/o System (Blocked Customers Cleared). Closed Queuing Networks. An Approximation for the G/G/M Queuing System. Priority Queuing Models. Transient Behavior of Queuing Systems. Review Problems.
21.SIMULATION. Basic Terminology. An Example of a Discrete Event Simulation. Random Numbers and Monte Carlo Simulation. An Example of Monte Carlo Simulation. Simulations with Continuous Random Variables. An Example of a Stochastic Simulation. Statistical Analysis in Simulations. Simulation Languages. The Simulation Process.
22.SIMULATION WITH PROCESS MODEL. Simulating an M/M/1 Queuing System. Simulating an M/M/2 System. A Series System. Simulating Open Queuing Networks. Simulating Erlang Service Times. What Else Can Process Models Do?
23. SPREADSHEET SIMULATION WITH @RISK. Introduction to @RISK: The Newsperson Problem. Modeling Cash Flows From A New Product. Bidding Models. Reliability and Warranty Modeling. Risk General Function. Risk Cumulative Function. Risktrigen Function. Creating a Distribution Based on a Point Forecast. Forecasting Income of a Major Corporation. Using Data to Obtain Inputs For New Product Simulations. Playing Craps with @RISK. Project Management. Simulating the NBA Finals.
24. FORECASTING. Moving Average Forecasting Methods. Simple Exponential Smoothing. Holt's Method: Exponential Smoothing with Trend. Winter's Method: Exponential Smoothing with Seasonality. Ad Hoc Forecasting, Simple Linear Regression. Fitting Non-Linear Relationships. Multiple Regression. Answers to Selected Problems. Index.
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作为一个热衷于算法美学的计算机科学家，我阅读这本书更多的是将其视为一本关于计算效率和复杂性理论的典范之作。它在描述经典算法（如单纯形法、内点法）的收敛性分析时，达到了极高的学术水准，严谨的分析让人感受到运筹学作为一门学科的优雅和力量。书中对于多项式时间算法的证明和对NP-难问题的讨论，都体现了作者深厚的理论功底。然而，这种对理论极致的追求，似乎在某些方面牺牲了对“工程优化”的关注。例如，在讨论启发式和元启发式算法时，如遗传算法或模拟退火，它们在许多实际问题中往往比精确算法更具实用价值，尤其是在目标函数非凸或约束条件难以精确描述时。这本书对这些方法的介绍相对简略，更像是一种“提及”，而非深入的剖析。它没有花足够篇幅去比较不同元启发式算法在处理特定类型约束（如组合约束）时的实际性能差异，也没有深入探讨如何设计一个高效的邻域搜索策略，这恰恰是算法实践中决定成败的关键。对我而言，这本书更像是一本关于“寻找最优解的数学途径”的百科全书，而不是一本关于“在时间和资源限制下找到足够好解的工程艺术”的指南。它教会我如何证明一个解的完美性，但对于如何快速、稳健地找到那个“足够好”的解，我还需要向其他更侧重工程实践的书籍寻求帮助。
评分我从一个应用数据科学家的角度来审视这本书，主要关注它如何处理不确定性。在金融风险管理和资源分配的领域，我们每天都在和随机性打交道，线性规划的确定性模型往往只能提供一个理想化的基准，而真正的价值在于如何处理波动。这本书的随机规划部分内容扎实，对两阶段随机规划的建立以及相应的Benders分解法进行了详尽的阐述，这无疑是理论上的瑰宝。然而，在将这些模型应用于真实世界时，我们面临的核心挑战是“情景的生成与修剪”。书中对如何构建合理的情景树（Scenario Tree）的讨论相对抽象，大多依赖于基于历史数据的假设分布（如正态分布或泊松分布）。但在实际中，尤其是在处理突发事件或金融危机时，极端尾部事件的概率分布往往是难以用简单模型捕获的。我期待看到更多关于“情景空间降维”、“基于机器学习的情景生成”或“分布健壮优化”（Robust Optimization）的深入对比和应用案例。这本书的侧重点似乎更偏向于在给定情景集合下求解最优决策，而非如何经济高效地获取和管理这些情景本身。因此，对于我们这种需要将模型部署到需要实时响应市场波动的系统中的团队来说，这本书提供了强大的理论框架，但缺少了将框架与不确定性量化技术无缝集成的“操作指南”。
评分作为一名教授运筹学入门课程的教师，我一直致力于寻找一本既能涵盖基础知识的严谨性，又能激发学生对前沿研究兴趣的教材。这本书在后者方面做得非常出色，但在基础的“循序渐进”上，确实需要读者具备相当的数学功底。书中对各种优化模型——从简单的指派问题到复杂的随机规划框架——的描述，其深度远超一般的本科教材。学生们经常反映，书中的例子虽然经典且具有启发性，但讲解的跳跃性较大。比如，在引入马尔可夫决策过程（MDP）时，它迅速地从基本定义跳跃到了最优策略的近似计算方法，而对于初学者来说，最基本的“价值迭代”和“策略迭代”的直观理解和手工计算练习相对不足。我不得不花费大量课堂时间来“反向工程”，将书中的高阶概念拆解回更易于理解的步骤。更让我感到挑战的是，这本书似乎更倾向于介绍“已解决”或“有成熟理论框架”的问题。对于新兴的研究热点，例如机器学习与优化决策的交叉领域（如强化学习中的决策制定），虽然有所提及，但深度和广度上不如那些专门的交叉学科著作。因此，它更像是一本给研究生或高年级本科生作为“进阶阅读”或“参考手册”的资料，而非一本可以带着学生从零开始构建完整知识体系的基石。它要求读者已经具备了一定的数理基础，否则很容易在开篇就被其严谨的论证风格所震慑。
评分这本号称“应用与算法”的著作，着实让我这个多年在制造业一线摸爬滚打的工程师大开眼界，但也让我体会到理论的深度与实践的鸿沟。初捧此书，我本期望能找到一些立即可用的工具箱，哪怕是针对特定瓶颈的“特效药”，比如供应链的最后一公里优化，或者复杂的排程问题。然而，这本书的开篇便如同一场严谨的数学洗礼，详尽地铺陈了线性规划的对偶性原理、非线性优化的KKT条件，甚至是动态规划的Bellman方程的数学推导。对于我这种更偏爱“快速建模与快速迭代”的实干家来说，那些密集的希腊字母和证明过程，就像一道道无形的墙，将我拦在了模型的“黑箱”之外。我理解，没有坚实的数学基础，便无法真正驾驭这些强大的优化工具，但坦白说，书中的许多章节在论述上显得过于学术化，缺乏与实际工业场景的紧密对接。比如，书中对大规模混合整数规划的求解器内部机制讲解得极为透彻，但对于如何处理现实中数据的不确定性（Stochastic Programming），或者如何将这些模型有效地嵌入到现有的ERP/MES系统中，给出的指导就显得有些意犹未尽了。我花了大量时间去理解如何将书中的理论转化为可执行的决策流程，发现更多时候，我需要回头去查阅更偏向实施案例的资料，这本书更像是理论研究者的案头宝典，而非工程师的行动指南。它给了我“为什么有效”的答案，但“如何高效地做”的环节，还需要自己费力去补足。
评分我最近参与了一个复杂物流网络的重新设计项目，希望能用运筹学的尖端方法来削减成本。在寻找参考资料时，我听人推荐了这本书，想着它名字中的“Algorithms”部分应该能提供最新的计算突破。坦白说，它在网络流和图论算法的章节确实令人印象深刻，特别是对于一些经典问题的算法改进路径进行了细致的梳理，比如最小费用最大流的最新变种，或者大规模网络拓扑的近似算法分析。然而，在实际应用层面，我发现书中的算法往往假设了理想的计算环境和完美的数据结构。例如，在讨论启发式算法时，重点放在了证明其收敛速度和渐近性能上，对于如何在实际的并行计算环境中部署这些算法，比如如何利用GPU加速某些矩阵运算，或者如何应对内存限制导致的动态数据加载问题，探讨得相对较少。我的团队面临的挑战是，我们的网络规模已经达到了百万级的节点和边，传统的教科书算法在单机上运行时间过长，迫切需要分布式计算的解决方案。这本书更多地停留在算法本身的“完美形态”的探讨，而对于“在噪声和限制下的工程实现”的讨论，则显得过于保守和理论化了。这使得我常常需要在阅读完它的精妙理论后，再转向那些侧重于高性能计算和工程实践的文献，去寻找将这些优美算法“落地”的桥梁。对于那些渴望直接将最新算法投入到超大规模工业应用的读者来说，可能需要有心理准备，这本书提供了蓝图，但没有提供施工队。
评分语言很直白，通俗易懂，比国内的写的好太多。
评分语言很直白，通俗易懂，比国内的写的好太多。
评分语言很直白，通俗易懂，比国内的写的好太多。
评分语言很直白，通俗易懂，比国内的写的好太多。
评分语言很直白，通俗易懂，比国内的写的好太多。
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