了得網研究生_運籌學導論·高級篇(英文版·第8版)

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運籌學導論·高級篇(英文版·第8版)
該商品所屬分類：研究生 -> 公共課
【市場價】	292-424元
【優惠價】	183-265元
【作者】	（美）塔哈（Taha，HA）　著
【所屬類別】	圖書教材研究生/本科/專科教材公共課
【出版社】	人民郵電出版社
【ISBN】	9787115160652
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內容介紹

紙張：膠版紙

包裝：平裝

是否套裝：否

國際標準書號ISBN：9787115160652

叢書名：圖靈原版數學·統計學繫列

作者：（美）塔哈（Taha，H.A.）著

出版社：人民郵電出版社

出版時間：2007年07月

內容簡介

本書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*化理論、非線性規劃算法、網絡和線性規劃算法進階、預測模型、概率動態規劃、馬爾可夫決策過程、案例分析等，並附有統計表、部分習題的解答、向量和矩陣復習及案例研究。
本書可供經管類專業和數學專業研究生以及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*學術刊物上發表了大量論文。

Chapter 13Advanced Linear Programming
13.1Simplex Method Fundamentals
13.1.1From Extreme Points to Basic Solutions
13.1.2Generalized Simplex Tableau in Matrix Form
13.2Revised Simplex Method
13.2.1Development of the Optimality and FeasibilityConditions
13.2.2Revised Simplex Algorithm
13.3Bounded-Variables Algorithm
13.4Duality
13.4.1Matrix Definition of the Dual Problem
13.4.2Optimal Dual Solution
13.5Parametric Linear Programming
13.5.1Parametric Changes in C
13.5.2Parametric Changes in bChapter 13Advanced Linear Programming
13.1Simplex Method Fundamentals
13.1.1From Extreme Points to Basic Solutions
13.1.2Generalized Simplex Tableau in Matrix Form
13.2Revised Simplex Method
13.2.1Development of the Optimality and FeasibilityConditions
13.2.2Revised Simplex Algorithm
13.3Bounded-Variables Algorithm
13.4Duality
13.4.1Matrix Definition of the Dual Problem
13.4.2Optimal Dual Solution
13.5Parametric Linear Programming
13.5.1Parametric Changes in C
13.5.2Parametric Changes in b
References
Chapter 14Review of Basic Probability
14.1Laws of Probability
14.1.1Addition Law of Probability
14.1.2Conditional Law of Probability
14.2Random Variables and Probability Distributions
14.3Expectation of a Random Variable
14.3.1Mean and Variance (Standard Deviation) of a Random Variable
14.3.2Mean and Variance of Joint Random Variables
14.4Four Common Probability Distributions
14.4.1Binomial Distribution
14.4.2Poisson Distribution
14.4.3Negative Exponential Distribution
14.4.4Normal Distribution
14.5Empirical Distributions
References
Chapter 15Probabilistic Inventory Models
15.1Continuous Review Models
15.1.1“Probabilitized” EOQ Model
15.1.2Probabilistic EOQ Model
15.2Single-Period Models
15.2.1No-Setup Model (Newsvendor Model)
15.2.2Setup Model (s-S Policy)
15.3Multiperiod Model
References
Chapter 16Simulation Modeling
16.1Monte Carlo Simulation
16.2Types of Simulation
16.3Elements of Discrete-Event Simulation
16.3.1Generic Definition of Events
16.3.2Sampling from Probability Distributions
16.4Generation of Random Numbers
16.5Mechanics of Discrete Simulation
16.5.1Manual Simulation of a Single-Server Model
16.5.2Spreadsheet-Based Simulation of the Single-Server Model
16.6Methods for Gathering Statistical Observations
16.6.1Subinterval Method
16.6.2Replication Method
16.6.3Regenerative (Cycle) Method
16.7Simulation Languages
References
Chapter 17Markov Chains
17.1Definition of a Markov Chain
17.2Absolute and n-Step Transition Probabilities
17.3Classification of the States in a Markov Chain
17.4Steady-State Probabilities and Mean Return Times of Ergodic Chains
17.5First Passage Time
17.6Analysis of Absorbing States
References
Chapter 18Classical Optimization Theory
18.1Unconstrained Problems
18.1.1Necessary and Sufficient Conditions
18.1.2The Newton-Raphson Method
18.2Constrained Problems
18.2.1Equality Constraints
18.2.2Inequality Constraints-Karush-Kuhn-Tucker (KKT)Conditions
References
Chapter 19Nonlinear Progra mming Algorivthms
19.1Unconstrained Algorithms
19.1.1Direct Search Method
19.1.2Gradient Method
19.2Constrained Algorithms
19.2.1Separable Programming
19.2.2Quadratic Programming
19.2.3Chance-Constrained Programming
19.2.4Linear Combinations Method
References
Chapter 20Additional Network and LP Algorithms
20.1Minimum-Cost Capacitated Flow Problem
20.1.1Network Representation
20.1.2Linear Programming Formulation
20.1.3Capacitated Network Simplex Algorithm
20.2Decomposition Algorithm
20.3Karmarkar Interior-Point Method
20.3.1Basic Idea of the Interior-Point Algorithm
20.3.2Interior-Point Algorithm
References
Chapter 21Forecasting Models
21.1Moving Average Technique
21.2Exponential Smoothing
21.3Regression
References
Chapter 22Probabilistic Dynamic Programming
22.1A Game of Chance
22.2Investment Problem
22.3Maximization of the Event of Achieving a Goal
References
Chapter 23Markovian Decision Process
23.1Scope of the Markovian Decision Problem
23.2Finite-Stage Dynamic Programming Model
23.3Infinite-Stage Model
23.3.1Exhaustive Enumeration Method
23.3.2Policy Iteration Method Without Discounting
23.3.3Policy Iteration Method with Discounting
23.4Linear Programming Solution
References
Chapter 24Case Analysis
Case 1: Airline Fuel Allocation Using Optimum Tankering
Case 2: Optimization of Heart Valves Production
Case 3: Scheduling Appointments at Australian Tourist Commission Trade Events
Case 4: Saving Federal Travel Dollars
Case 5: Optimal Ship Routing and Personnel Assignment for Naval Recruitment in Thailand
Case 6: Allocation of Operating Room Time in Mount Sinai Hospital
Case 7: Optimizing Trailer Payloads at PFG Building Glass
Case 8: Optimization of Crosscutting and Log Allocation at Weyerhaeuser
Case 9: Layout Planning for a Computer Integrated Manufacturing (CIM) FacilityCase 10: Booking Limits in Hotel Reservations
Case 11: Casey's Problem: Interpreting and Evaluating a New Test
Case 12: Ordering Golfers on the Final Day of Ryder Cup Matches
Case 13: Inventory Decisions in Dell's Supply Chain
Case 14: Analysis of an Internal Transport System in a Manufacturing Plant
Case 15: Telephone Sales Manpower Planning at Qantas Airways
Appendix BStatistical Tables
Appendix CPartial Solutions to Answers Problems
Appendix DReview of Vectors and Matrices
D.1Vectors
D.1.1Definition of a Vector
D.1.2Addition (Subtraction) of Vectors
D.1.3Multiplication of Vectors by Scalars
D.1.4Linearly Independent Vectors
D.2Matrices
D.2.1Definition of a Matrix
D.2.2Types of Matrices
D.2.3Matrix Arithmetic Operations
D.2.4Determinant of a Square Matrix
D.2.5Nonsingular Matrix
D.2.6Inverse of a Nonsingular Matrix
D.2.7Methods of Computing the Inverse of Matrix
D.2.8Matrix Manipulations Using Excel
D.3Quadratic Forms
D.4Convex and Concave Functions
Problems
Selected References
Appendix E Case Studies

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