Operations Research A Practical Introduction

Offering an in depth guide to the use of Operations Research techniques in scientific decision making, design analysis and management. The clear readable text not only presents the standard mathematical models but also provides an up-to-date examination of practical issues pertinent to the development and use of computer implementations of solution methods.

Students with diverse backgrounds will face a multitude of decisions in a variety of engineering, scientific, industrial, and financial settings. They will need to know how to identify problems that the methods of operations research (OR) can solve, how to structure the problems into standard mathematical models, and finally how to apply or develop computational tools to solve the problems. Perfect for any one-semester course in OR, Operations Research: A Practical Introduction answers all of these needs. In addition to providing a practical introduction and guide to using OR techniques, it includes a timely examination of innovative methods and practical issues related to the development and use of computer implementations. It provides a sound introduction to the mathematical models relevant to OR and illustrates the effective use of OR techniques with examples drawn from industrial, computing, engineering, and business applications. Many students will take only one course in the techniques of Operations Research. Operations Research: A Practical Introduction offers them the greatest benefit from that course through a broad survey of the techniques and tools available for quantitative decision making. It will also encourage other students to pursue more advanced studies and provides you a concise, well-structured, vehicle for delivering the best possible overview of the discipline.

INTRODUCTION TO OPERATIONS RESEARCHThe Origins and Applications of Operations ResearchSystem Modeling PrinciplesAlgorithm Efficiency and Problem ComplexityOptimality and PracticalityGuide to Software ToolsLINEAR PROGRAMMINGThe Linear Programming ModelThe Art of Problem FormulationGraphical Solution of Linear Programming ProblemsPreparation for the Simplex MethodThe Simplex MethodInitial Solutions for General ConstraintsInformation in the TableauDuality and Sensitivity AnalysisRevised Simplex and Computational EfficiencyGuide to Software ToolsIllustrative ApplicationsNETWORK ANALYSISGraphs and Networks: Preliminary DefinitionsMaximum Flow in NetworksMinimum Cost Network Flow ProblemsNetwork ConnectivityShortest Path ProblemsDynamic ProgrammingProject ManagementGuide to Software ToolsIllustrative ApplicationsINTEGER PROGRAMMINGFundamental ConceptsTypical Integer Programming ProblemsZero-One Model FormulationsBranch-and-BoundCutting Planes and FacetsCover InequalitiesLagrangian RelaxationColumn GenerationGuide to Software ToolsIllustrative ApplicationsNONLINEAR OPTIMIZATIONPreliminary Notation and ConceptsUnconstrained OptimizationConstrained OptimizationGuide to Software ToolsIllustrative ApplicationsMARKOV PROCESSESState TransitionsState ProbabilitiesFirst Passage ProbabilitiesProperties of the States in a Markov ProcessSteady-State AnalysisExpected First Passage TimesAbsorbing ChainsGuide to Software ToolsIllustrative ApplicationsQUEUING MODELS Basic Elements of Queuing SystemsArrival and Service PatternsAnalysis of Simple Queuing SystemsGuide to Software ToolsIllustrative ApplicationsSIMULATION Simulation: Purposes and ApplicationsDiscrete Simulation ModelsObservations of SimulationsGuide to Software ToolsIllustrative ApplicationsDECISION ANALYSISThe Decision Making ProcessAn Introduction to Game TheoryDecision TreesUtility TheoryThe Psychology of Decision MakingGuide to Software ToolsIllustrative ApplicationsHEURISTIC TECHNIQUES FOR OPTIMIZATION Local Improvement HeuristicsOptimization by Simulated AnnealingParallel AnnealingGenetic AlgorithmsNeural NetworksGuide to Software ToolsIllustrative ApplicationsAPPENDIX: Review of Essential Mathematics

Michael W. Carter and Camille C. Price