Introduction to Operations Research
Námskeið
- T-403-ADGE Aðgerðagreining
- T-810-OPTI Bestunaraðferðir
Lýsing:
For over four decades, Introduction to Operations Research has been the classic text on operations research. While building on the classic strengths of the text, the author continues to find new ways to make the text current and relevant to students. One way is by incorporating a wealth of state-of-the art, user-friendly software and more coverage of business applications than ever before. When the first co-author received the prestigious Expository Writing Award from INFORMS for a recent edition, the award citation described the reasons for the book’s great success as follows: “Two features account for this success.
First, the editions have been outstanding from students’ points of view due to excellent motivation, clear and intuitive explanations, good examples of professional practice, excellent organization of material, very useful supporting software, and appropriate but not excessive mathematics. Second, the editions have been attractive from instructors’ points of view because they repeatedly infuse state-of-the-art material with remarkable lucidity and plain language.
Annað
- Höfundur: Frederick Hillier
- Útgáfa:11
- Útgáfudagur: 2020-02-04
- Hægt að prenta út 2 bls.
- Hægt að afrita 2 bls.
- Format:ePub
- ISBN 13: 9781260579376
- Print ISBN: 9781260575873
- ISBN 10: 1260579379
Efnisyfirlit
- cover
- Title Page
- Copyright Page
- About the Authors
- About the Case Writers
- Dedication
- Table of Contents
- Supplements Available on the Text Website
- Preface
- Chapter 1: Introduction
- 1.1 The Origins of Operations Research
- 1.2 The Nature of Operations Research
- 1.3 The Relationship between Analytics and Operations Research
- 1.4 The Impact of Operations Research
- 1.5 Some Trends that Should Further Increase the Future Impact of Operations Research
- 1.6 Algorithms and or Courseware
- Selected References
- Problems
- Chapter 2: Overview of How Operations Research and Analytics Professionals Analyze Problems
- 2.1 Defining the Problem
- 2.2 Gathering and Organizing Relevant Data
- 2.3 Using Descriptive Analytics to Analyze Big Data
- 2.4 Using Predictive Analytics to Analyze Big Data
- 2.5 Formulating a Mathematical Model to Begin Applying Prescriptive Analytics
- 2.6 Learning How to Derive Solutions from the Model
- 2.7 Testing the Model
- 2.8 Preparing to Apply the Model
- 2.9 Implementation
- 2.10 Conclusions
- Selected References
- Problems
- Chapter 3: Introduction to Linear Programming
- 3.1 Prototype Example
- 3.2 The Linear Programming Model
- 3.3 Assumptions of Linear Programming
- 3.4 Additional Examples
- 3.5 Formulating and Solving Linear Programming Models on a Spreadsheet
- 3.6 Formulating Very Large Linear Programming Models
- 3.7 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Case 3.1 Reclaiming Solid Wastes
- Previews of Added Cases on Our Website
- Case 3.2 Cutting Cafeteria Costs
- Case 3.3 Staffing a Call Center
- Case 3.4 Promoting a Breakfast Cereal
- Case 3.5 Auto Assembly
- Chapter 4: Solving Linear Programming Problems: The Simplex Method
- 4.1 The Essence of the Simplex Method
- 4.2 Setting Up the Simplex Method
- 4.3 The Algebra of the Simplex Method
- 4.4 The Simplex Method in Tabular Form
- 4.5 Tie Breaking in the Simplex Method
- 4.6 Reformulating Nonstandard Models to Prepare for Applying the Simplex Method
- 4.7 The Big M Method for Helping to Solve Reformulated Models
- 4.8 The Two-Phase Method is an Alternative to the Big M Method
- 4.9 Postoptimality Analysis
- 4.10 Computer Implementation
- 4.11 The Interior-Point Approach to Solving Linear Programming Problems
- 4.12 Conclusions
- Appendix 4.1: An Introduction to Using LINDO and LINGO
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Case 4.1 Fabrics and Fall Fashions
- Previews of Added Cases on Our Website
- Case 4.2 New Frontiers
- Case 4.3 Assigning Students to Schools
- Chapter 5: The Theory of the Simplex Method
- 5.1 Foundations of the Simplex Method
- 5.2 The Simplex Method in Matrix Form
- 5.3 A Fundamental Insight
- 5.4 The Revised Simplex Method
- 5.5 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Chapter 6: Duality Theory
- 6.1 The Essence of Duality Theory
- 6.2 Primal-Dual Relationships
- 6.3 Adapting to Other Primal Forms
- 6.4 The Role of Duality Theory in Sensitivity Analysis
- 6.5 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Chapter 7: Linear Programming under Uncertainty
- 7.1 The Essence of Sensitivity Analysis
- 7.2 Applying Sensitivity Analysis
- 7.3 Performing Sensitivity Analysis on a Spreadsheet
- 7.4 Robust Optimization
- 7.5 Chance Constraints
- 7.6 Stochastic Programming with Recourse
- 7.7 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Case 7.1 Controlling Air Pollution
- Previews of Added Cases on Our Website
- Case 7.2 Farm Management
- Case 7.3 Assigning Students to Schools, Revisited
- Case 7.4 Writing a Nontechnical Memo
- Chapter 8: Other Algorithms for Linear Programming
- 8.1 The Dual Simplex Method
- 8.2 Parametric Linear Programming
- 8.3 The Upper Bound Technique
- 8.4 An Interior-Point Algorithm
- 8.5 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Chapter 9: The Transportation and Assignment Problems
- 9.1 The Transportation Problem
- 9.2 A Streamlined Simplex Method for the Transportation Problem
- 9.3 The Assignment Problem
- 9.4 A Special Algorithm for the Assignment Problem
- 9.5 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Case 9.1 Shipping Wood to Market
- Previews of Added Cases on Our Website
- Case 9.2 Continuation of the Texago Case Study
- Case 9.3 Project Pickings
- Chapter 10: Network Optimization Models
- 10.1 Prototype Example
- 10.2 The Terminology of Networks
- 10.3 The Shortest-Path Problem
- 10.4 The Minimum Spanning Tree Problem
- 10.5 The Maximum Flow Problem
- 10.6 The Minimum Cost Flow Problem
- 10.7 The Network Simplex Method
- 10.8 A Network Model for Optimizing a Project’s Time-Cost Trade-Off
- 10.9 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Case 10.1 Money in Motion
- Previews of Added Cases on Our Website
- Case 10.2 Aiding Allies
- Case 10.3 Steps to Success
- Chapter 11: Dynamic Programming
- 11.1 A Prototype Example for Dynamic Programming
- 11.2 Characteristics of Dynamic Programming Problems
- 11.3 Deterministic Dynamic Programming
- 11.4 Probabilistic Dynamic Programming
- 11.5 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Chapter 12: Integer Programming
- 12.1 Prototype Example
- 12.2 Some BIP Applications
- 12.3 Using Binary Variables to Deal with Fixed Charges
- 12.4 A Binary Representation of General Integer Variables
- 12.5 Some Perspectives on Solving Integer Programming Problems
- 12.6 The Branch-and-Bound Technique and its Application to Binary Integer Programming
- 12.7 A Branch-and-Bound Algorithm for Mixed Integer Programming
- 12.8 The Branch-and-Cut Approach to Solving BIP Problems
- 12.9 The Incorporation of Constraint Programming
- 12.10 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Case 12.1 Capacity Concerns
- Previews of Added Cases on Our Website
- Case 12.2 Assigning Art
- Case 12.3 Stocking Sets
- Case 12.4 Assigning Students to Schools, Revisited Again
- Chapter 13: Nonlinear Programming
- 13.1 Sample Applications
- 13.2 Graphical Illustration of Nonlinear Programming Problems
- 13.3 Types of Nonlinear Programming Problems
- 13.4 One-Variable Unconstrained Optimization
- 13.5 Multivariable Unconstrained Optimization
- 13.6 The Karush-Kuhn-Tucker (KKT) Conditions for Constrained Optimization
- 13.7 Quadratic Programming
- 13.8 Separable Programming
- 13.9 Convex Programming
- 13.10 Nonconvex Programming (with Spreadsheets)
- 13.11 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Case 13.1 Savvy Stock Selection
- Previews of Added Cases on Our Website
- Case 13.2 International Investments
- Case 13.3 Promoting a Breakfast Cereal, Revisited
- Chapter 14: Metaheuristics
- 14.1 The Nature of Metaheuristics
- 14.2 Tabu Search
- 14.3 Simulated Annealing
- 14.4 Genetic Algorithms
- 14.5 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Chapter 15: Game Theory
- 15.1 The Formulation of Two-Person, Zero-Sum Games
- 15.2 Solving Simple Games—A Prototype Example
- 15.3 Games with Mixed Strategies
- 15.4 Graphical Solution Procedure
- 15.5 Solving by Linear Programming
- 15.6 Extensions
- 15.7 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Chapter 16: Decision Analysis
- 16.1 A Prototype Example
- 16.2 Decision Making without Experimentation
- 16.3 Decision Making with Experimentation
- 16.4 Decision Trees
- 16.5 Utility Theory
- 16.6 The Practical Application of Decision Analysis
- 16.7 Multiple Criteria Decision Analysis, Including Goal Programming
- 16.8 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Case 16.1 Brainy Business
- Preview of Added Cases on Our Website
- Case 16.2 Smart Steering Support
- Case 16.3 Who Wants to Be a Millionaire?
- Case 16.4 University Toys and the Engineering Professor Action Figures
- Chapter 17: Queueing Theory
- 17.1 Prototype Example
- 17.2 Basic Structure of Queueing Models
- 17.3 Some Common Types of Real Queueing Systems
- 17.4 The Role of the Exponential Distribution
- 17.5 The Birth-and-Death Process
- 17.6 Queueing Models Based on the Birth-and-Death Process
- 17.7 Queueing Models Involving Nonexponential Distributions
- 17.8 Priority-Discipline Queueing Models
- 17.9 Queueing Networks
- 17.10 The Application of Queueing Theory
- 17.11 Behavioral Queueing Theory
- 17.12 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Case 17.1 Reducing In-Process Inventory
- Preview of an Added Case on Our Website
- Case 17.2 Queueing Quandary
- Chapter 18: Inventory Theory
- 18.1 Examples
- 18.2 Components of Inventory Models
- 18.3 Deterministic Continuous-Review Models
- 18.4 A Deterministic Periodic-Review Model
- 18.5 Deterministic Multiechelon Inventory Models for Supply Chain Management
- 18.6 A Stochastic Continuous-Review Model
- 18.7 A Stochastic Single-Period Model for Perishable Products
- 18.8 Revenue Management
- 18.9 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Case 18.1 Brushing Up on Inventory Control
- Previews of Added Cases on Our Website
- Case 18.2 TNT: Tackling Newsboy’s Teaching
- Case 18.3 Jettisoning Surplus Stock
- Chapter 19: Markov Decision Processes
- 19.1 A Prototype Example
- 19.2 A Model for Markov Decision Processes
- 19.3 Linear Programming and Optimal Policies
- 19.4 Markov Decision Processes in Practice
- 19.5 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Chapter 20: Simulation
- 20.1 The Essence of Simulation
- 20.2 Some Common Types of Applications of Simulation
- 20.3 Generation of Random Numbers
- 20.4 Generation of Random Observations from a Probability Distribution
- 20.5 Simulation Optimization
- 20.6 Outline of a Major Simulation Study
- 20.7 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Case 20.1 Reducing In-Process Inventory, Revisted
- Previews of Added Cases on Our Website
- Case 20.2 Planning Planers
- Case 20.3 Pricing under Pressure
- Appendix 1. Documentation for the OR Courseware
- Appendix 2. Convexity
- Appendix 3. Classical Optimization Methods
- Appendix 4. Matrices and Matrix Operations
- Appendix 5. Table for a Normal Distribution
- Partial Answers to Selected Problems
- Author Index
- Subject Index
- Supplements
- Chapter 3: Additional Cases
- Supplement to Chapter 6
- An Economic Interpretation of the Dual Problem and the Simplex Method
- Problem
- Supplement 1 to Chapter 9
- A Case Study with Many Transportation Problems
- Supplement 2 to Chapter 9
- The Construction of Initial BF Solutions for Transportation Problems
- Problems
- Supplement to Chapter 12
- Some Innovative Uses of Binary Variables in Model Formulation
- Problems
- Supplement to Chapter 16
- Preemptive Goal Programming and Its Solution Procedures
- Problems
- Case 16S-1 A Cure for Cuba
- Case 16S-2 Airport Security
- Supplement to Chapter 18
- Stochastic Periodic-Review Models
- Problems
- Supplement 1 to Chapter 19
- A Policy Improvement Algorithm for Finding Optimal Policies
- Problems
- Supplement 2 to Chapter 19
- A Discounted Cost Criterion
- Problems
- Chapter 20: Additional Cases
- Supplement 1 to Chapter 20
- Variance-Reducing Techniques
- Problems
- Supplement 2 to Chapter 20
- Regenerative Method of Statistical Analysis
- Problems
- Chapter 21
- The Art of Modeling with Spreadsheets
- 21.1 A Case Study: The Everglade Golden Years Company Cash Flow Problem
- 21.2 Overview of the Process of Modeling with Spreadsheets
- 21.3 Some Guidelines for Building “Good” Spreadsheet Models
- 21.4 Debugging a Spreadsheet Model
- 21.5 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Case 21.1 Prudent Provisions for Pensions
- Chapter 22
- Project Management with PERT/CPM
- 22.1 A Prototype Example—The Reliable Construction Co. Project
- 22.2 Using a Network to Visually Display a Project
- 22.3 Scheduling a Project with PERT/CPM
- 22.4 Dealing with Uncertain Activity Durations
- 22.5 Considering Time-Cost Trade-Offs
- 22.6 Scheduling and Controlling Project Costs
- 22.7 An Evaluation of PERT/CPM
- 22.8 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Case 22.1 “School’s out forever . . .”
- Chapter 23
- Additional Special Types of Linear Programming Problems
- 23.1 The Transshipment Problem
- 23.2 Multidivisional Problems
- 23.3 The Decomposition Principle for Multidivisional Problems
- 23.4 Multitime Period Problems
- 23.5 Multidivisional Multitime Period Problems
- 23.6 Conclusions
- Selected References
- Problems
- Chapter 24
- Probability Theory
- 24.1 Sample Space
- 24.2 Random Variables
- 24.3 Probability and Probability Distributions
- 24.4 Conditional Probability and Independent Events
- 24.5 Discrete Probability Distributions
- 24.6 Continuous Probability Distributions
- 24.7 Expectation
- 24.8 Moments
- 24.9 Bivariate Probability Distribution
- 24.10 Marginal and Conditional Probability Distributions
- 24.11 Expectations for Bivariate Distributions
- 24.12 Independent Random Variables and Random Samples
- 24.13 Law of Large Numbers
- 24.14 Central Limit Theorem
- 24.15 Functions of Random Variables
- Selected References
- Problems
- Chapter 25
- Reliability
- 25.1 Structure Function of a System
- 25.2 System Reliability
- 25.3 Calculation of Exact System Reliability
- 25.4 Bounds on System Reliability
- 25.5 Bounds on Reliability Based upon Failure Times
- 25.6 Conclusions
- Selected References
- Problems
- Chapter 26
- The Application of Queueing Theory
- 26.1 Examples
- 26.2 Decision Making
- 26.3 Formulation of Waiting-Cost Functions
- 26.4 Decision Models
- 26.5 The Evaluation of Travel Time
- 26.6 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Chapter 27
- Forecasting
- 27.1 Some Applications of Forecasting
- 27.2 Judgmental Forecasting Methods
- 27.3 Time Series
- 27.4 Forecasting Methods for a Constant-Level Model
- 27.5 Incorporating Seasonal Effects into Forecasting Methods
- 27.6 An Exponential Smoothing Method for a Linear Trend Model
- 27.7 Forecasting Errors
- 27.8 The ARIMA Method
- 27.9 Causal Forecasting with Linear Regression
- 27.10 Conclusions
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
- Case 27.1 Finagling the Forecasts
- Chapter 28
- Markov Chains
- 28.1 Stochastic Processes
- 28.2 Markov Chains
- 28.3 Chapman-Kolmogorov Equations
- 28.4 Classification of States of a Markov Chain
- 28.5 Long-Run Properties of Markov Chains
- 28.6 First Passage Times
- 28.7 Absorbing States
- 28.8 Continuous Time Markov Chains
- Selected References
- Learning Aids for this Chapter on Our Website
- Problems
UM RAFBÆKUR Á HEIMKAUP.IS
Bókahillan þín er þitt svæði og þar eru bækurnar þínar geymdar. Þú kemst í bókahilluna þína hvar og hvenær sem er í tölvu eða snjalltæki. Einfalt og þægilegt!
Rafbók til eignar
Rafbók til eignar þarf að hlaða niður á þau tæki sem þú vilt nota innan eins árs frá því bókin er keypt.
Þú kemst í bækurnar hvar sem er
Þú getur nálgast allar raf(skóla)bækurnar þínar á einu augabragði, hvar og hvenær sem er í bókahillunni þinni. Engin taska, enginn kyndill og ekkert vesen (hvað þá yfirvigt).
Auðvelt að fletta og leita
Þú getur flakkað milli síðna og kafla eins og þér hentar best og farið beint í ákveðna kafla úr efnisyfirlitinu. Í leitinni finnur þú orð, kafla eða síður í einum smelli.
Glósur og yfirstrikanir
Þú getur auðkennt textabrot með mismunandi litum og skrifað glósur að vild í rafbókina. Þú getur jafnvel séð glósur og yfirstrikanir hjá bekkjarsystkinum og kennara ef þeir leyfa það. Allt á einum stað.
Hvað viltu sjá? / Þú ræður hvernig síðan lítur út
Þú lagar síðuna að þínum þörfum. Stækkaðu eða minnkaðu myndir og texta með multi-level zoom til að sjá síðuna eins og þér hentar best í þínu námi.
Fleiri góðir kostir
- Þú getur prentað síður úr bókinni (innan þeirra marka sem útgefandinn setur)
- Möguleiki á tengingu við annað stafrænt og gagnvirkt efni, svo sem myndbönd eða spurningar úr efninu
- Auðvelt að afrita og líma efni/texta fyrir t.d. heimaverkefni eða ritgerðir
- Styður tækni sem hjálpar nemendum með sjón- eða heyrnarskerðingu
- Gerð : 208
- Höfundur : 14878
- Útgáfuár : 2020
- Leyfi : 380