Operations Research : A System Engineering Approach / Prasanna Davidas Dahe

It 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