运筹学导论

内容概要

　　Hamdy A.Taha 美国阿肯色大学荣休教授，世界知名运筹学家，曾在全球各地教和担任顾问，同时拥有非常丰富的教学研究和实践经验。他在Management Science和Operations Research等世界顶级学术刊物上发表了大量论文。

书籍目录

Chapter 13　Advanced Linear Programming13.1　Simplex Method Fundamentals13.1.1　From Extreme Points to Basic Solutions13.1.2　Generalized Simplex Tableau in Matrix Form13.2　Revised Simplex Method13.2.1　Development of the Optimality and FeasibilityConditions13.2.2　Revised Simplex Algorithm13.3　Bounded-Variables Algorithm13.4　Duality13.4.1　Matrix Definition of the Dual Problem13.4.2　Optimal Dual Solution13.5　Parametric Linear Programming13.5.1　Parametric Changes in C13.5.2　Parametric Changes in bReferencesChapter 14　Review of Basic Probability14.1　Laws of Probability14.1.1　Addition Law of Probability14.1.2　Conditional Law of Probability14.2　Random Variables and Probability Distributions14.3　Expectation of a Random Variable14.3.1　Mean and Variance (Standard Deviation) of a Random Variable14.3.2　Mean and Variance of Joint Random Variables14.4　Four Common Probability Distributions14.4.1　Binomial Distribution14.4.2　Poisson Distribution14.4.3　Negative Exponential Distribution14.4.4　Normal Distribution14.5　Empirical DistributionsReferencesChapter 15　Probabilistic Inventory Models15.1　Continuous Review Models15.1.1　“Probabilitized” EOQ Model15.1.2　Probabilistic EOQ Model15.2　Single-Period Models15.2.1　No-Setup Model (Newsvendor Model)15.2.2　Setup Model (s-S Policy)15.3　Multiperiod ModelReferencesChapter 16　Simulation Modeling16.1　Monte Carlo Simulation16.2　Types of Simulation16.3　Elements of Discrete-Event Simulation16.3.1　Generic Definition of Events16.3.2　Sampling from Probability Distributions16.4　Generation of Random Numbers16.5　Mechanics of Discrete Simulation16.5.1　Manual Simulation of a Single-Server Model16.5.2　Spreadsheet-Based Simulation of the Single-Server Model16.6　Methods for Gathering Statistical Observations16.6.1　Subinterval Method16.6.2　Replication Method16.6.3　Regenerative (Cycle) Method16.7　Simulation LanguagesReferencesChapter 17　Markov Chains17.1　Definition of a Markov Chain17.2　Absolute and n-Step Transition Probabilities17.3　Classification of the States in a Markov Chain17.4　Steady-State Probabilities and Mean Return Times of Ergodic Chains17.5　First Passage Time17.6　Analysis of Absorbing StatesReferencesChapter 18　Classical Optimization Theory18.1　Unconstrained Problems18.1.1　Necessary and Sufficient Conditions18.1.2　The Newton-Raphson Method18.2　Constrained Problems18.2.1　Equality Constraints18.2.2　Inequality Constraints-Karush-Kuhn-Tucker (KKT)ConditionsReferencesChapter 19　Nonlinear Progra mming Algorivthms19.1　Unconstrained Algorithms19.1.1　Direct Search Method19.1.2　Gradient Method19.2　Constrained Algorithms19.2.1　Separable Programming19.2.2　Quadratic Programming19.2.3　Chance-Constrained Programming19.2.4　Linear Combinations MethodReferencesChapter 20　Additional Network and LP Algorithms20.1　Minimum-Cost Capacitated Flow Problem20.1.1　Network Representation20.1.2　Linear Programming Formulation20.1.3　Capacitated Network Simplex Algorithm20.2　Decomposition Algorithm20.3　Karmarkar Interior-Point Method20.3.1　Basic Idea of the Interior-Point Algorithm20.3.2　Interior-Point AlgorithmReferencesChapter 21　Forecasting Models21.1　Moving Average Technique21.2　Exponential Smoothing21.3　RegressionReferencesChapter 22　Probabilistic Dynamic Programming22.1　A Game of Chance22.2　Investment Problem22.3　Maximization of the Event of Achieving a GoalReferencesChapter 23　Markovian Decision Process23.1　Scope of the Markovian Decision Problem23.2　Finite-Stage Dynamic Programming Model23.3　Infinite-Stage Model23.3.1　Exhaustive Enumeration Method23.3.2　Policy Iteration Method Without Discounting23.3.3　Policy Iteration Method with Discounting23.4　Linear Programming SolutionReferencesChapter 24　Case AnalysisCase 1： Airline Fuel Allocation Using Optimum TankeringCase 2： Optimization of Heart Valves ProductionCase 3： Scheduling Appointments at Australian Tourist Commission Trade EventsCase 4： Saving Federal Travel DollarsCase 5： Optimal Ship Routing and Personnel Assignment for Naval Recruitment in ThailandCase 6： Allocation of Operating Room Time in Mount Sinai HospitalCase 7： Optimizing Trailer Payloads at PFG Building GlassCase 8： Optimization of Crosscutting and Log Allocation at WeyerhaeuserCase 9： Layout Planning for a Computer Integrated Manufacturing (CIM) Facility　Case 10： Booking Limits in Hotel ReservationsCase 11： Caseys Problem： Interpreting and Evaluating a New TestCase 12： Ordering Golfers on the Final Day of Ryder Cup MatchesCase 13： Inventory Decisions in Dells Supply ChainCase 14： Analysis of an Internal Transport System in a Manufacturing PlantCase 15： Telephone Sales Manpower Planning at Qantas AirwaysAppendix B　Statistical TablesAppendix C　Partial Solutions to Answers ProblemsAppendix D　Review of Vectors and MatricesD.1　VectorsD.1.1　Definition of a VectorD.1.2　Addition (Subtraction) of VectorsD.1.3　Multiplication of Vectors by ScalarsD.1.4　Linearly Independent VectorsD.2　MatricesD.2.1　Definition of a MatrixD.2.2　Types of MatricesD.2.3　Matrix Arithmetic OperationsD.2.4　Determinant of a Square MatrixD.2.5　Nonsingular MatrixD.2.6　Inverse of a Nonsingular MatrixD.2.7　Methods of Computing the Inverse of MatrixD.2.8　Matrix Manipulations Using ExcelD.3　Quadratic FormsD.4　Convex and Concave FunctionsProblemsSelected ReferencesAppendix E Case Studies

作者简介

《运筹学导论:高级篇(英文版·第8版)》是运筹学方面的经典著作之一，为全球众多高校采用. 高级篇共12章，内容包括高级线性规划、概率论基础复习、概率库存模型、模拟模型、马尔可夫链、经典最优化理论、非线性规划算法、网络和线性规划算法进阶、预测模型、概率动态规划、马尔可夫决策过程、案例分析等，并附有统计表、部分习题的解答、向量和矩阵复习及案例研究。 

《运筹学导论:高级篇(英文版·第8版)》可供经管类专业和数学专业研究生以及MBA作为教材或者参考书，也可供相关研究人员参考。

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