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Introduction to Probability Models: Operations Research, Volume II (with CD-ROM and InfoTrac), by Wayne L. Winston
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This text, the second volume of Wayne Winston's successful OPERATIONS RESEARCH: APPLICATIONS AND ALGORITHMS, FOURTH EDITION, covers probability models with recent contributions from financial engineering, computational simulation and manufacturing. The specific attention to probability models with the addition of recent practical breakthroughs makes this the first text to introduce these ideas together at an accessible level. Excellent problem sets abound. The text provides a balanced approach by developing the underlying theory while illustrating them with interesting examples. All of the necessary mathematical requirements are reviewed in Chapter 1.
- Sales Rank: #480310 in Books
- Brand: Brand: Cengage Learning
- Published on: 2003-06-05
- Original language: English
- Number of items: 1
- Dimensions: 10.12" h x 1.33" w x 8.30" l, 3.32 pounds
- Binding: Hardcover
- 744 pages
- Used Book in Good Condition
Review
1. REVIEW OF CALCULUS AND PROBABILITY. Review of Differential Calculus. 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. 2. 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. 3. 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. 4. 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. 5. 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. 6. 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. 7. 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. 8. QUEUING THEORY. Some Queuing Terminology. Modeling Arrival and Service Processes. Birth-Death Processes. M/M/1/GD/?V/?V Queuing System and the Queuing Formula L=?U W, The M/M/1/GD/?V Queuing System. The M/M/S/ GD/?V/?V Queuing System. The M/G/ ?V/GD/?V?V and GI/G/?V/GD/?V/?VModels. The M/ G/1/GD/?V/?V 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/?V 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. 9. 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. 10. 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 Model Do? 11. SPREADSHEET SIMULATION WITH @RISK. Introduction to @RISK: The Newsperson Problem. Modeling Cash Flows from a New Product. Bidding Models. Reliability and Warranty Modeling. RISKGENERAL Function. RISKCUMULATIVE 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. 12. SPREADSHEET SIMULATION AND OPTIMIZATION WITH RISKOPTIMIZER. The Newsperson Problem. Newsperson Problem with Historical Data. Manpower Scheduling Under Uncertainty. Product Mix Problem. Job Shop Scheduling. Traveling Salesperson Problem. 13. OPTION PRICING AND REAL OPTIONS. Lognormal Model for Stock Prices. Option Definitions. Types of Real Options. Valuing Options by Arbitrage Methods. Black-Scholes Option Pricing Formula. Estimating Volatility. Risk Neutral Approach to Option Pricing. Valuing an Internet Start Up and Web TV. Relation Between Binomial and Lognormal Models. Pricing American Options with Binomial Trees. Pricing European Puts and Calls with Simulation. Using Simulation to Model Real Options. 14. PORTFOLIO RISK, OPTIMIZATION AND HEDGING. Measuring Value at Risk (VAR). Scenario Approach to Portfolio Optimization. 15. 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. 16. BROWNIAN MOTION, STOCHASTIC CALCULUS, AND OPTIMAL CONTROL. What Is Brownian Motion? Derivation of Brownian Motion as a Limit of Random Walks. Stochastic Differential Equations. Ito's Lemma. Using Ito's Lemma to Derive the Black-Scholes Equation. An Introduction to Stochastic Control.
About the Author
Wayne L. Winston is Professor Emeritus of Decision Sciences at the Kelley School of Business at Indiana University and is now a Professor of Decision and Information Sciences at the Bauer College at the University of Houston. He has won more than 45 teaching awards, including the school-wide MBA award six times. His current interest is in showing how spreadsheet models can be used to solve business problems in all disciplines, particularly in finance, sports, and marketing. In addition to publishing more than 20 articles in leading journals, Dr. Winston has written such successful textbooks as OPERATIONS RESEARCH: APPLICATIONS AND ALGORITHMS, MATHEMATICAL PROGRAMMING: APPLICATIONS AND ALGORITHMS, SIMULATION MODELING WITH @RISK, DATA ANALYSIS FOR MANAGERS, SPREADSHEET MODELING AND APPLICATIONS, MATHLETICS, DATA ANALYSIS AND BUSINESS MODELING WITH EXCEL 2013, MARKETING ANALYTICS, and FINANCIAL MODELS USING SIMULATION AND OPTIMIZATION. He received his B.S. degree in mathematics from MIT and his Ph.D. in operations research from Yale.
Most helpful customer reviews
5 of 9 people found the following review helpful.
Add this to the list of worthless math books
By Engi Nerd
As expected, the book was very scarce on good examples. The theory is there, but several steps are jumped between explanations forcing the reader to visit sources outside of the book to fully understand the concept. Transistion between lessons wasn't smooth wither; there are areas where it asks the reader to refer to Volume 1. What happens if you don't own Volume 1? The book sucks...just like the majority of other math books.
0 of 0 people found the following review helpful.
Lacks answers to chapter problems
By Justin C. Samsel
This book does a relatively good job covering the topics in probability and modeling. Would be better if more examples were provided, and explanations in the examples did not skip over some of the simple calculations. What I dislike the most is the lack of answers to chapter problems. I've taken a number of math courses and every book I've ever used gave answers to the odd problems. This is critical to the learning process as it can be nearly impossible to know whether or not you're understanding the concepts and coming up with the right answers without some answers to guide you.
It's like learning to shoot a gun without ever getting to see your target after practice.
0 of 0 people found the following review helpful.
Great price and quick shipping...
By Gary A. Johnson
Only one complaint the binding broke on the book a week or two after I received it, but have not lost a page and the semester is over next week.
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