Operations Research: An Introduction, Global Edition

Name: Operations Research: An Introduction, Global Edition
Price: 4390 ISK
Availability: InStock
Author: Hamdy A. Taha
ISBN: 9781292468044

Höfundur: Hamdy A. Taha (Útgáfa: 11)

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Operations Research uses a balanced combination of theory, applications and computations to help you learn the basics of operating research (OR). It focuses on algorithmic and practical implementation of OR techniques. Easy-to-understand numerical examples explain often difficult math concepts, helping you grasp the foundational idea without getting stuck on complex theorems or notations. Full case studies and math-free anecdotes show how algorithms are used in real-life applications.

The 11th Edition introduces analytics, artificial intelligence, and machine learning topics that strengthen and streamline the decision-making processes involved in OR. New stories, 3 new chapters, new case studies and sections provide an up-to-date introduction to the field of OR.

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Útgefandi: Pearson International Content

ISBN: 9781292468044

Print ISBN: 9781292468037

Format: ePub

Útgáfa: 11

Höfundar: Hamdy A. Taha

Tungumál: English

Útgefið: 2025-02-12

Prent takmörkun á líftíma: 100

Prent takmörkun: 2

Afritunar takmörkun: 2

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Cover
Cover
Front Matter
Title Page
Copyright Page
Dedication
List of Aha! Moment Essays
What’s New in the Eleventh Edition
Acknowledgments
About the Author
Trademarks
Chapter 1: Overview of OR, Analytics, AI, and ML in Decision-Making
Chapter 1 Introduction
1.1 Introduction
1.2 Two Distinct Approaches for Making Decisions
1.3 OR Mathematical Modeling
1.4 Analytics Modeling
1.5 Artificial Intelligence (AI)
1.6 Machine Learning (ML)
Bibliography
Problems
Chapter 2: Modeling with Linear Programming
Chapter 2 Introduction
2.1 Two-Variable LP Model
2.2 Graphical LP Solution
2.3 Computer Solution with Solver and AMPL
2.4 Linear Programming Applications
Bibliography
Problems
Chapter 3: The Simplex Method and Sensitivity Analysis
Chapter 3 Introduction
3.1 LP Model in Equation Form
3.2 Transition from Graphical to Algebraic Solution
3.3 The Simplex Method
3.4 Starting Solution for “Ill-Behaved” LPs
3.5 Special Cases in the Simplex Method
3.6 Sensitivity Analysis
3.7 Computational Issues in Linear Programming
Bibliography
Problems
Chapter 4: Duality and Post-Optimal Analysis
Chapter 4 Introduction
4.1 Definition of the Dual Problem
4.2 Primal−Dual Relationships
4.3 Economic Interpretation of Duality
4.4 Additional Simplex Algorithms
4.5 Post-Optimal Analysis
4.6 Transition from Textbook to Commercial Software Treatment of Sensitivity Analysis
Bibliography
Problems
Chapter 5: Transportation Model and Its Variants
Chapter 5 Introduction
5.1 Definition of the Transportation Model
5.2 Nontraditional Applications of the Transportation Model
5.3 The Transportation Algorithm
5.4 The Assignment Model
5.5 The Transshipment Model
Bibliography
Problems
Chapter 6: Network Models
Chapter 6 Introduction
6.1 Scope and Definition of Network Models
6.2 Minimal Spanning Tree Algorithm
6.3 Shortest-Route Problem
6.4 Maximal Flow Model
6.5 CPM and PERT
6.6 Minimum-Cost Capacitated Flow Problem
Bibliography
Problems
Chapter 7: Advanced Linear Programming
Chapter 7 Introduction
7.1 Simplex Method Fundamentals
7.2 Revised Simplex Method
7.3 Bounded-Variables Algorithm
7.4 Duality
7.5 Parametric Linear Programming
7.6 Goal Programming (GP)
7.7 Decomposition Algorithm
7.8 Karmarkar Interior-Point Method
Bibliography
Problems
Chapter 8: Stochastic Linear Programming
Chapter 8 Introduction
8.1 Deterministic Nature of LP Modeling
8.2 Case against Associating LP Sensitivity Analysis Ranges with LP Uncertainty
8.3 Uncertainty Representation in LP Models
8.4 Solution Models for Special LPs Under Uncertainty
Bibliography
Problems
Chapter 9: Integer Linear Programming
Chapter 9 Introduction
9.1 Illustrative Applications
9.2 Integer Programming Algorithms
Bibliography
Problems
Chapter 10: Heuristic Programming
Chapter 10 Introduction
10.1 Introduction
10.2 Greedy (Local Search) Heuristics
10.3 Metaheuristic
10.4 Application of Metaheuristics to Integer Linear Programs
10.5 Introduction to Constraint Programming (CP)
Bibliography
Problems
Chapter 11: Traveling Salesperson Problem (TSP)
Chapter 11 Introduction
11.1 Scope of the TSP
11.2 TSP Mathematical Model
11.3 Exact TSP Algorithms
11.4 Local Search Heuristics
11.5 Metaheuristics
Bibliography
Problems
Chapter 12: Dynamic Programming
Chapter 12 Introduction
12.1 Recursive Nature of Dynamic Programming (DP) Computations
12.2 Forward and Backward Recursion
12.3 Deterministic DP Applications
12.4 Problem of Dimensionality
12.5 Probabilistic DP Applications
Bibliography
Problems
Chapter 13: Inventory Modeling
Chapter 13 Introduction
13.1 The Inventory Problem
13.2 A Supply Chain Perspective of the Inventory Problem
13.3 Deterministic Static EOQ Models
13.4 Deterministic Dynamic EOQ Models
13.5 Probabilistic Continuous Review Models
13.6 Probabilistic Single-Period Models
13.7 Probabilistic Multiperiod Model
Bibliography
Problems
Chapter 14: Yield Management
Chapter 14 Introduction
14.1 What is Yield Management (YM)?
14.2 Nature of Demand versus Price in YM
14.3 Elasticity and Fare Pricing
14.4 Concluding Remarks
Bibliography
Problems
Chapter 15: Decision Analysis and Games
Chapter 15 Introduction
15.1 Special Decision-Making Situations
15.2 Decision-Making Under Certainty—Analytic Hierarchy Process (AHP)
15.3 Bayes’ Probabilities with ML Applications
15.4 Decision Tree Models
15.5 Decisions Under Uncertainty
15.6 Game Theory
Bibliography
Problems
Chapter 16: Markov Chains
Chapter 16 Introduction
16.1 Definition of a Markov Chain
16.2 Absolute and n-Step Transition Probabilities
16.3 Classification of the States in a Markov Chain
16.4 Ergodic (Regular) Markov Chain
16.5 First Passage Time
16.6 Analysis of Absorbing States
Bibliography
Problems
Chapter 17: Markovian Decision Process
Chapter 17 Introduction
17.1 Scope of the Markovian Decision Problem
17.2 Finite-Stage Dynamic Programming Model
17.3 Infinite-Stage Model
17.4 Linear Programming Solution
Bibliography
Problems
Chapter 18: Queuing Systems
Chapter 18 Introduction
18.1 Why Study Queues?
18.2 Elements of a Queuing Model
18.3 Role of Exponential Distribution
18.4 Pure Birth and Death Models (Relationship between the Exponential and Poisson Distributions)
18.5 General Poisson Queuing Model
18.6 Specialized Poisson Queues
18.7 (M/G/1):(GD/∞/∞)—Pollaczek–Khintchine (P–K) Formula
18.8 Other Queuing Models
18.9 Queuing Decision Models
Bibliography
Problems
Chapter 19: Discrete-Event and Monte Carlo Simulations
Chapter 19 Introduction
19.1 What Is Simulation?
19.2 Monte Carlo Simulation
19.3 Spreadsheet-Based Simulation of a Single-Server Model
19.4 Methods for Gathering Statistical Observations
Appendix: Uniform 0-1 Random Numbers
Bibliography
Problems
Chapter 20: Classical Optimization Theory
Chapter 20 Introduction
20.1 Unconstrained Problems
20.2 Constrained Problems
Bibliography
Problems
Chapter 21: Nonlinear Programming Algorithms
Chapter 21 Introduction
21.1 Unconstrained Algorithms
21.2 Constrained Algorithms
Bibliography
Problems
Chapter 22: Case Analysis
Chapter 22 Introduction
Case 1: Crowdsourcing Analytics and Operations Research Expertise . . . Tales of Two Studies with Different Outcomes
Case 2: Airline Fuel Allocation Using Optimum Tankering
Case 3: Optimization of Heart Valves Production
Case 4: Scheduling Appointments at Australian Tourist Commission Trade Events
Case 5: Saving Federal Travel Dollars
Case 6: Optimal Ship Routing and Personnel Assignments for Recruits to the Royal Thai Navy
Case 7: Allocation of Operating Room Time in Mount Sinai Hospital
Case 8: Optimizing Trailer Payloads at PFG Building Glass
Case 9: Optimization of Crosscutting and Log Allocation at Weyerhaeuser
Case 10: Layout Planning for a Computer Integrated Manufacturing (CIM) Facility
Case 11: Booking Limits in Hotel Reservations
Case 12: Casey’s Problem: Interpreting and Evaluating a New Test
Case 13: Ordering Golfers on the Final Day of Ryder Cup Matches
Case 14: Kroger Improves Pharmacy Inventory Management
Case 15: Inventory Decisions in Dell’s Supply Chain
Case 16: Forest Cover Change Prediction Using Markov Chain Model: A Case Study on Sub-Himalayan Town Gangtok, India
Case 17: Analysis of an Internal Transport System in a Manufacturing Plant
Case 18: Telephone Sales Workforce Planning at Qantas Airways
Appendix A: Statistical Tables
Appendix A: Statistical Tables
Appendix B: Partial Answers to Selected Problems
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
Chapter 18
Chapter 19
Chapter 20
Chapter 21
Appendix E
Appendix C: AMPL Modeling Language
C.1 Rudimentary AMPL Model
C.2 Components of AMPL Model
C.3 Mathematical Expressions and Computed Parameters
C.4 Subsets and Indexed Sets
C.5 Accessing External Files
C.6 Interactive Commands
C.7 Iterative and Conditional Execution of AMPL Commands
C.8 Sensitivity Analysis Using AMPL
C.9 Selected AMPL Models
Bibliography
Problems
Appendix D: Review of Vectors and Matrices
D.1 Vectors
D.2 Matrices
D.3 Quadratic Forms
D.4 Convex and Concave Functions
Bibliography
Problems
Appendix E: Review of Basic Probability
E.1 Laws of Probability
E.2 Random Variables and Probability Distributions
E.3 Expectation of a Random Variable
E.4 Four Common Probability Distributions
E.5 Empirical Distributions
Bibliography
Problems
Appendix F: Forecasting Models
F.1 Moving Average Technique
F.2 Exponential Smoothing
F.3 Regression
Bibliography
Problems