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Operations Research and Management Science HandbookEdited By A. Ravi Ravindran View larger

Operations Research and Management Science HandbookEdited By A. Ravi Ravindran

M00002373

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Operations Research (OR) began as an interdisciplinary activity to solve complex military problems during World War II. Utilizing principles from mathematics, engineering, business, computer science, economics, and statistics, OR has developed into a full fledged academic discipline with practical application in business, industry, government and military. Currently regarded as a body of established mathematical models and methods essential to solving complicated management issues, OR provides quantitative analysis of problems from which managers can make objective decisions. Operations Research and Management Science (OR/MS) methodologies continue to flourish in numerous decision making fields.

Featuring a mix of international authors, Operations Research and Management Science Handbook combines OR/MS models, methods, and applications into one comprehensive, yet concise volume. The first resource to reach for when confronting OR/MS difficulties, this text – Provides a single source guide in OR/MS Bridges theory and practice Covers all topics relevant to OR/MS Offers a quick reference guide for students, researchers and practitioners Contains unified and up-to-date coverage designed and edited with non-experts in mind Discusses software availability for all OR/MS techniques Includes contributions from a mix of domestic and international experts

The 26 chapters in the handbook are divided into two parts. Part I contains 14 chapters that cover the fundamental OR/MS models and methods. Each chapter gives an overview of a particular OR/MS model, its solution methods and illustrates successful applications. Part II of the handbook contains 11 chapters discussing the OR/MS applications in specific areas. They include airlines, e-commerce, energy systems, finance, military, production systems, project management, quality control, reliability, supply chain management and water resources. Part

II ends with a chapter on the future of OR/MS applications.

Table of Contents

I OR/MS Models and Methods
Linear Programming, K. G. Murty
Brief History of Algorithms for Solving Linear Equations, Linear
Inequalities, and LPs
Applicability of the LP Model: Classical Examples of
Direct Applications
LP Models Involving Transformations of Variables
Intelligent Modeling Essential to Get Good Results, an Example from
Container Shipping
Planning Uses of LP Models
Brief Introduction to Algorithms for Solving LP Models
Software Systems Available for Solving LP Models
Multiobjective LP Models
Nonlinear Programming, T.B. Trafalis and R.C. Gilbert
Introduction
Unconstrained Optimization
Constrained Optimization
Conclusion
Integer Programming, M. Weng
Introduction
Formulation of IP Models
Branch and Bound Method
Cutting Plane Method
Other Solution Methods and Computer Solution
Network Optimization, M.B. Yildirim
Introduction
Notation
Minimum Cost Flow Problem
Shortest Path Problem
Maximum Flow Problem
Assignment Problem
Minimum Spanning Tree Problem
Minimum Cost Multicommodity Flow Problem
Conclusions
Multiple Criteria Decision Making, A.S. M. Masud and A. R. Ravindran
Some Definitions
The Concept of “Best Solution”
Criteria Normalization
Computing Criteria Weights
Multiple Criteria Methods for Finite
Alternatives
Multiple Criteria Mathematical Programming Problems
Goal Programming
Method of Global Criterion and Compromise Programming
Interactive Methods
MCDM Applications
MCDM Software
Further Readings
Decision Analysis, C. M. Klein
Introduction
Terminology for Decision Analysis
Decision Making under Risk
Decision Making under Uncertainty
Practical Decision Analysis
Conclusions
Resources
Dynamic Programming, J. A. Ventura
Introduction
Deterministic Dynamic Programming Models
Stochastic Dynamic Programming Models
Conclusions
Stochastic Processes, S. H. Xu
Introduction
Poisson Processes
Discrete-Time Markov Chains
Continuous-Time Markov Chains
Renewal Theory
Software Products Available for Solving
Stochastic Models
Queueing Theory, N. Gautam
Introduction
Queueing Theory Basics
Single-Station and Single-Class Queues
Single-Station and Multiclass Queues
Multistation and Single-Class Queues
Multistation and Multiclass Queues
Concluding Remarks
Inventory Control, F. Azadivar and A. Rangarajan
Introduction
Design of Inventory Systems
Deterministic Inventory Systems
Stochastic Inventory Systems
Inventory Control at Multiple Locations
Inventory Management in Practice
Conclusions
Current and Future Research
Complexity and Large-Scale Networks, H. P. Thadakamalla, S. R.T. Kumara, and R. Albert
Introduction
Statistical Properties of Complex Networks
Modeling of Complex Networks
Why “Complex” Networks
Optimization in Complex Networks
Conclusions
Simulation, C. M. Harmonosky
Introduction
Basics of Simulation
Simulation Languages and Software
Simulation Projects—The Bigger Picture
Summary
Metaheuristics for Discrete Optimization Problems, R.K. Kincaid
Mathematical Framework for Single Solution Metaheuristics
Network Location Problems
Multistart Local Search
Simulated Annealing
Plain Vanilla Tabu Search
Active Structural Acoustic Control (ASAC)
Nature Reserve Site Selection
Damper Placement in Flexible Truss Structures
Reactive Tabu Search
Discussion
Robust Optimization, H. J. Greenberg and T. Morrison
Introduction
Classical Models
Robust Optimization Models
More Applications
Summary
II OR/MS Applications
Project Management, A. B. Badiru
Introduction
Critical Path Method
PERT Network Analysis
Statistical Analysis of Project Duration
Precedence Diagramming Method
Software Tools for Project Management
Conclusion
Quality Control, Q. Feng and K. C. Kapur
Introduction
Quality Control and Product Life Cycle
New Trends and Relationship to Six Sigma
Statistical Process Control
Process Capability Studies
Advanced Control Charts
16.7 Limitations of Acceptance Sampling
16.8 Conclusions
Reliability, L. M. Leemis
Introduction
Reliability in System Design
Lifetime Distributions
Parametric Models
Parameter Estimation in Survival Analysis
Nonparametric Methods
Assessing Model Adequacy
Summary
Production Systems,
B. L. Foote and K. G. Murty
Production Planning Problem
Demand Forecasting
Models for Production Layout Design
Scheduling of Production and Service Systems
Energy Systems, C. R. Hudson and A. B. Badiru
Introduction
Definition of Energy
Harnessing Natural Energy
Mathematical Modeling of Energy Systems
Linear Programming Model of Energy Resource Combination
Integer Programming Model for Energy Investment Options
Simulation and Optimization of Distributed Energy Systems
Point-of-Use Energy Generation
Modeling of CHP Systems
Economic Optimization Methods
Design of a Model for Optimization of CHP System Capacities
Capacity Optimization
Implementation of the Computer Model
Other Scenarios
Airline Optimization, J. L. Snowdon and G. Paleologo
Introduction
Schedule Planning
Revenue Management
Aircraft Load Planning
Future Research Directions and Conclusions
Financial Engineering, A. R. Heching and A. J. King
Introduction
Return
Estimating an Asset’s Mean and Variance
Diversification
Efficient Frontier
Utility Analysis
Black–Litterman Asset Allocation Model
Risk Management
Options
Valuing Options
Dynamic Programming
Pricing American Options Using Dynamic Programming
Comparison of Monte Carlo Simulation and Dynamic Programming
Multi-Period Asset Liability Management
Conclusions
Supply Chain Management, D. P. Warsing
Introduction
Managing Inventories in the Supply Chain
Managing Transportation in the Supply Chain
Managing Locations in the Supply Chain
Managing Dyads in the Supply Chain
Discussion and Conclusions
E-Commerce, S. Sadagopan
Introduction
Evolution of E-Commerce
OR/MS and E-Commerce
OR Applications in E-Commerce
Tools–Applications Matrix
Way Forward
Summary
Water Resources, G.V. Loganathan
Introduction
Optimal Operating Policy for Reservoir Systems
Water Distribution Systems Optimization
Preferences in Choosing Domestic Plumbing Materials
Stormwater Management
Groundwater Management
Summary
Military Applications, J. D. Weir and M. U. Thomas
Introduction
Background on Military OR
Current Military Applications of OR
Concluding Remarks
Future of OR/MS Applications: A Practitioner’s Perspective, P. Balasubramanian
Past as a Guide to the Future
Impact of the Internet
Emerging Opportunities Index

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Reviews

"This handbook provides a comprehensive overview of Operations Research and Management Science (OR/MS) models, methods, and applications in a single volume . . . It is an ideal reference book for OR/MS practitioners in business, industry, government, and academia. It can also serve as a supplemental text in undergraduate and graduate OR/MS courses at the university level."

– Paulo Mbunga, in Zentralblatt Math, 2009

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