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Beginning with an overview of the operations research models and decision-making, the book describes in detail the various optimization techniques such as linear and non-linear programming, integer linear programming, dynamic programming, genetic programming, and network techniques such as PERT (program evaluation review technique) and CPM (critical path method). It also explains the transportation and assignment problems, queuing theory, games theory, sequencing, replacement and capital investment decisions and inventory. Besides, the book discusses the Monte Carlo simulation techniques for solving queuing, demand forecasting, inventory and scheduling problems and elaborates on genetic algorithms.
Each mathematical technique is dealt with in two parts. The first part explains the theory underlying the methodology of solution to problems. The second part illustrates how the theory is applied to solve different kinds of problems.
This book is designed as a textbook for the undergraduate students of mechanical engineering, electrical engineering, production and industrial engineering, computer science and engineering and information technology. Besides, the book will also be useful to the postgraduate students of production and industrial engineering, computer applications, business administration, commerce, mathematics and statistics.
KEY FEATURES :
Includes a large number of solved problems to help students comprehend the concepts with ease.
Gives step-by-step explanation of algorithms by taking problems.
Provides chapter-end exercises to drill the students in self-study.
Contents:
Preface
1. Introduction to Operations Research Models and Decision-Making
2. Linear Programming: Basic Concepts
3. Graphical Method
4. Simplex Method
5. Big M Method
6. Duality in Linear Programming
7. Sensitivity Analysis
8. Revised Simplex Method
9. Two-phase Simplex Method
10. Dual Simplex Method
11. Integer Linear Programming: Branch and Bound Algorithms
12. Integer Linear Programming: Gomory Cutting Plane Method
13. Transportation Problem
14. Assignment Model
15. Non-linear Programming:
Classical Optimization Techniques
16. Non-linear Programming with Constraints Graphical Solution
17. Non-linear Programming: Multivariable Optimization with Equality Constraints: Lagrange Multipliers Method
18. Non-linear Programming: Multivariable Optimization with Inequality Constraints: Kuhn“Tucker Conditions
19. Non-linear Programming: Quadratic Programming and Separable Programming
20. Search Methods (Non-linear Programming)
21. Sequencing
22. Replacement and Capital Investment Decisions
23. Inventory
24. Theory of Games
25. Queueing Theory (Waiting Lines)
26. Network Problems
27. Network Techniques: Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT)
28. Dynamic Programming
29. Monte Carlo Simulation
30. Genetic Algorithms
31. Genetic Programming
Appendices ISBN 9788120346345
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