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100			_911150 _aDahe, Prasanna Davidas
245			_aOperations Research : _bA System Engineering Approach / _cPrasanna Davidas Dahe
260			_aDelhi _bCengage Learning India Pvt. Ltd. _c2019
300			_bxvi, 445p. : ill. ; _c24cm
520			_aIt includes acknowledgement, appendix and index pages. Overview: This book, intended for the course on operations research, is particularly useful for UG/PG degree programmes in engineering and computer applications and may also suit other streams such as management, sciences, etc. The content is designed to address the requirements of the engineering programmes in Indian universities. The systems engineering approach is unique and is expected to receive appreciation especially from the academic community. Operations research is related to the analytical part of systems engineering. This book discusses the operations research techniques using the fundamental concepts of systems engineering to make a difficult but important subject easy to understand. This course in systems engineering concepts coupled with the application of operations research techniques shall help engineers and managers to develop the viewpoint and tools necessary for handling real-life problems. Difficult analytical techniques and mathematical procedures are explained from first principles assuming bare minimum pre-requisites, to encourage the learner and to make learning enjoyable. A step-by-step explanation presents the concepts and principles and a clear link is established to the already-digested concepts to keep the student involved. Features: Basic concepts of systems engineering integrated with operations research techniques to enhance real-life problem solving. Simple, everyday-life examples are used to facilitate a smooth transfer of knowledge in an interesting manner. High-quality diagrams illustrate the subject matter. A comprehensive collection of solved examples in a chronological order with increasing level of difficulty help to assimilate the concepts and induce problem-solving skills. Extensive end-of-chapter key concepts and exercises help to review the learning. Table of Contents: PART I SYSTEMS ENGINEERING 1. Introduction to Systems Engineering 1.1 Background 1.2 Nature of Real-life Engineering Systems 1.3 Necessity, Significance, and Scope of Systems Engineering 1.4 Role of Systems Engineering 1.5 Justification for Systems Engineering 1.6 An Example: Proposal for Constructing a House for Oneself 2. The Concept of System 2.1 Introduction and Definition 2.2 System Concepts 2.3 Characteristics of a System 2.4 Examples of Systems 2.4.1 A Computer System 2.4.2 The Hydrologic Cycle 2.5 Types of Systems 2.6 Hierarchy of Systems 2.6.1 Subsystems and Suprasystems 2.7 Identification and Formulation of Systems 2.7.1 Building Systems from Subsystems 2.7.2 An Example 3. Systems Engineering 3.1 Introduction to Systems Engineering 3.1.1 Definition 3.2 Systems Approach and Systems Analysis 3.2.1 Systems Approach 3.2.2 Systems Analysis 3.3 System Models and Their Role 3.3.1 Models 3.3.2 Development of System Models 3.3.3 Role of System Models 3.3.4 Types of System Models 3.3.5 An Example 3.4 Examples: Informal Applications of Systems Engineering 4. Systems Analysis 4.1 Introduction 4.2 Systems Analysis Techniques 4.3 The Concept and Process of Optimization 4.4 Optimization by Method of Calculus 4.4.1 Function of a Single Variable 4.4.2 Function of Multiple Variables 4.4.3 Unconstrained Systems 4.4.4 Constrained Systems 4.5 Terminology and Definition of Terms 4.6 Non-linear Programming PART II DETERMINISTIC MODELS 5. Linear Programming 5.1 Introduction 5.2 General Form of Linear Programming Model 5.3 Assumptions in Linear Programming 5.4 Solution of Linear Programming Models by Graphical Method 5.5 Solution of Linear Programming Models by Simplex Method 5.5.1 The Simplex Algorithm 5.6 Handling Artificial Variables: The Big-M and Two-phase Methods 5.6.1 The Big-M Method 5.6.2 The Two-phase Method 5.7 Introduction to the Theory of Duality 5.8 Applications of Linear Programming Models 5.9 Limitations of Linear Programming 5.10 Examples: Formulation of Linear Programming Problems 5.10.1 The Crop Planning Problem 5.10.2 The Product Mix Problem 6. Transportation, Transshipment, and Assignment Problems 6.1 Introduction 6.2 The Transportation Problem 6.2.1 Formulation and Discussion 6.2.2 Solution to the Transportation Problem 6.2.3 Transportation Algorithm: Finding the Initial Basic Feasible Solution 6.2.4 Transportation Algorithm: The Check for Optimality 6.2.5 Transportation Algorithm: Iterating the Algorithm 6.2.6 Degeneracy 6.2.7 Closure 6.2.8 Exercises 6.3 The Transshipment Problem 6.3.1 Steps in Solving the Transshipment Problem 6.3.2 Closure 6.3.3 Exercises 6.4 The Assignment Problem 6.4.1 The Hungarian Method 6.4.2 Algorithm for the Hungarian Method 6.4.3 Closure 6.4.4 Exercises 7. Dynamic Programming 7.1 Introduction 7.2 Approach and Methodology 7.3 Applications of Dynamic Programming 7.3.1 Shortest Route Problem 1 7.3.2 Shortest Route Problem 2 7.3.3 Shortest Route Problem 3 7.3.4 Resource Allocation Problem 1 7.3.5 Resource Allocation Problem 2 7.4 Curse of Dimensionality in Dynamic Programming 7.5 Formulation of Dynamic Programming Problems 8. Inventory Models 8.1 Introduction 8.2 Selective Inventory Control 8.2.1 ABC Analysis 8.2.2 VED Analysis 8.2.3 SDE Analysis 8.2.4 FSN Analysis 8.3 General Inventory Model 8.3.1 Inventory Parameters 8.3.2 Cost Considerations in Inventory Problem 8.3.3 Assumptions 8.4 Infinite Delivery Rate with No Backordering 8.4.1 Derivation of the Economic Order Quantity Formula 8.4.2 Price Breaks 8.5 Finite Delivery Rate with No Backordering 8.6 Infinite Delivery Rate with Backordering 8.7 Finite Delivery Rate with Backordering 9. Sequencing Models 9.1 Introduction 9.2 Elements and Assumptions 9.2.1 Elements of Sequencing Problems 9.2.2 Assumptions in Sequencing Problems 9.3 Processing N Jobs Through One Machine 9.4 Processing N Jobs Through Two Machines 9.5 Processing N Jobs Through Three Machines PART III PROBABILISTIC MODELS 10. Probability Concepts and Forecasting Techniques 10.1 Introduction 10.2 Basic Concepts of Probability and Statistics 10.2.1 Probability of an Event 10.2.2 Random Variables 10.2.3 Discrete Random Variables 10.2.4 Important Probability Distributions of a Discrete Random Variable 10.2.5 Continuous Random Variables 10.2.6 Important Probability Distributions of Continuous Random Variables 10.3 Forecasting Techniques 10.3.1 Forecasting Process 10.3.2 Classification of Forecasting Techniques 10.3.3 Qualitative Forecasting Techniques 10.3.4 Quantitative Forecasting Techniques 10.3.5 Regression and Correlation Analysis 10.3.6 Accuracy and Control of Forecasts 11. Queuing Theory—Waiting Line Models 11.1 Introduction 11.2 Approaches to Resolve the Queuing Problem 11.3 Queuing Parameters 11.3.1 The Input Process 11.3.2 The Service Mechanism 11.3.3 Queue Discipline 11.3.4 Customer Behaviour 11.4 Assumptions, Concepts, and Definitions 11.4.1 Assumptions 11.4.2 Transient and Steady-state System 11.4.3 Traffic Intensity 11.4.4 The Queue System 11.4.5 Notation 11.4.6 Kendall’s Notation 11.4.7 Balance Diagram and Balance Equations 11.5 Queuing Model M/M/1/∞ 11.6 Queuing Model M/M 11.7 Queuing Model M/M/s/∞ 11.8 Queuing Model M/M/s/N 12. Replacement Models 12.1 Introduction 12.2 Replacement of Items that Deteriorate with Time 12.2.1 Time Value of Money is Not Considered 12.2.2 Time Value of Money is Considered 12.3 Replacement of Items that Fail Suddenly 12.4 Replacement Policies 12.4.1 Individual Replacement Policy 12.4.2 Group Replacement Policy 12.4.3 Mortality 12.4.4 Group Replacement 12.4.5 Preventive Replacement 13. Decision Theory and Games 13.1 Introduction 13.2 Decision-making Under Risk 13.2.1 Expected Value Criterion 13.2.2 Decision Procedure with Bayes Probabilities 13.3 Decision Trees 13.4 Decision-making Under Uncertainty 13.4.1 Maximax and Minimin Criteria 13.4.2 Minimax and Maximin Criteria 13.4.3 Laplace Criterion 13.4.4 Hurwicz Criterion 13.5 Game Theory—Competitive Strategy 13.5.1 Concepts and Terminology 13.5.2 Solution of Two-person Zero-sum Games with Pure Strategies 13.5.3 Mixed Strategies 14. Simulation 14.1 Introduction 14.2 The Concept of Simulation 14.2.1 Classification of Simulation Models 14.2.2 Advantages and Limitations of Simulation Models 14.2.3 Application of Simulation Models 14.3 Monte Carlo Simu 14.4 Examples of Monte Carlo Simulation 14.4.1 To Create Cumulative Probability of Demand Based on Normal Distribution 15. Project Management 15.1 Introduction 15.1.1 Cost of Delays in Implementation of Projects 15.1.2 Projects and the Planning Process 15.1.3 Project Life Cycle 15.1.4 Role of Project Management Techniques—Critical Path Method and Performance Evaluation and Review Technique 15.2 Principles of Network Technique 15.2.1 Activities and Events 15.2.2 Event and Activity Numbering 15.2.3 Developing a Network 15.3 Project Time Analysis for Network Techniques—Critical Path Method and Performance Evaluation and Review Technique 15.3.1 Activity Duration 15.3.2 Event Time and Activity Time 15.3.3 Time Analysis for a Project 15.3.4 Event Slack and Activity Float 15.3.5 Critical Path 15.4 Performance Evaluation and Review Technique 15.4.1 Performance Evaluation and Review Technique Statistics 15.4.2 Probability of Completing a Project on Schedule 15.4.3 Criticism of Performance Evaluation and Review Technique and Its Utility 15.5 Project Time–Cost Relationship 15.5.1 Shortening Project Duration 15.5.2 Activity Time–Cost relationship 15.5.3 Project Time–Cost Relationship 15.5.4 Time–Cost Trade-off Analysis 15.6 Resource Allocation and Scheduling 15.6.1 Project Resources 15.6.2 Resource Usage 15.6.3 Resources Smoothening 15.6.4 Resources Leveling 15.7 Project Scheduling and Monitoring 15.7.1 Methods for Scheduling 15.7.2 Presenting the Project Schedule 15.7.3 Monitoring and Control of a Project
650		0	_911151 _aOperations research; Programming (Mathematics)
856			_uhttps://www.cengage.co.in/book-list/print/operations-research-a-systems-engineering-approach-on
942			_2ddc _cBK _e23rd _h658.4034 _mDAH