運籌學導論

內容概要

　　《運籌學導論：高級篇（英文版·第8版）》是運籌學方面的經典著作之一，為全球眾多高校采用。 高級篇共12章，內容包括高級線性規(guī)劃、概率論基礎復習、概率庫存模型、模擬模型、馬爾可夫鏈、經典最優(yōu)化理論、非線性規(guī)劃算法、網絡和線性規(guī)劃算法進階、預測模型、概率動態(tài)規(guī)劃、馬爾可夫決策過程、案例分析等，并附有統(tǒng)計表、部分習題的解答、向量和矩陣復習及案例研究?！　　哆\籌學導論：高級篇（英文版·第8版）》可供經管類專業(yè)和數(shù)學專業(yè)研究生以及MBA作為教材或者參考書，也可供相關研究人員參考。

作者簡介

　　Hamdy A.Taha 美國阿肯色大學榮休教授，世界知名運籌學家，曾在全球各地教和擔任顧問，同時擁有非常豐富的教學研究和實踐經驗。他在Management Science和Operations Research等世界頂級學術刊物上發(fā)表了大量論文。

書籍目錄

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

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