Introduction to Operations Research
12th Edition
9364444507
·
9789364444507
© 2025 | Published: July 7, 2025
Overview:Introduction to Operations Research is the classic text on operations research. While building on the classic strengths of the text, the author continues to find new ways to make the text current and relevant to students. One way is by incor…
Read More
Chapter 1. Introduction
Chapter 2. Overview of How Operations Research and Analytics Professionals Analyze Problems
Chapter 3. Introduction to Linear Programming
Chapter 4. Solving Linear Programming Problems: The Simplex Method
Chapter 5. The Theory of the Simplex Method
Chapter 6. Duality Theory
Chapter 7. Linear Programming under Uncertainty
Chapter 8. Other Algorithms for Linear Programming
Chapter 9. The Transportation and Assignment Problems
Chapter 10. Network Optimization Models
Chapter 11. Dynamic Programming
Chapter 12. Integer Programming
Chapter 13. Nonlinear Programming
Chapter 14. Metaheuristics
Chapter 15. Game Theory
Chapter 16. Decision Analysis
Chapter 17. Queueing Theory
Chapter 18. Inventory Theory and Revenue Management (Online)
Chapter 19. Markov Decision Processes
Chapter 20. Simulation
Chapter 21. The Art of Modeling with Spreadsheets (Online)
Chapter 22. Project Management with PERT/CPM(Online)
Chapter 23. Additional Special Types of Linear Programming Problems (Online)
Chapter 24. Probability Theory (Online)
Chapter 25. Reliability (Online)
Chapter 26. The Application of Queueing Theory (Online)
Chapter 27. Forecasting (Online)
Chapter 28. Markov Chains (Online)
Chapter 2. Overview of How Operations Research and Analytics Professionals Analyze Problems
Chapter 3. Introduction to Linear Programming
Chapter 4. Solving Linear Programming Problems: The Simplex Method
Chapter 5. The Theory of the Simplex Method
Chapter 6. Duality Theory
Chapter 7. Linear Programming under Uncertainty
Chapter 8. Other Algorithms for Linear Programming
Chapter 9. The Transportation and Assignment Problems
Chapter 10. Network Optimization Models
Chapter 11. Dynamic Programming
Chapter 12. Integer Programming
Chapter 13. Nonlinear Programming
Chapter 14. Metaheuristics
Chapter 15. Game Theory
Chapter 16. Decision Analysis
Chapter 17. Queueing Theory
Chapter 18. Inventory Theory and Revenue Management (Online)
Chapter 19. Markov Decision Processes
Chapter 20. Simulation
Chapter 21. The Art of Modeling with Spreadsheets (Online)
Chapter 22. Project Management with PERT/CPM(Online)
Chapter 23. Additional Special Types of Linear Programming Problems (Online)
Chapter 24. Probability Theory (Online)
Chapter 25. Reliability (Online)
Chapter 26. The Application of Queueing Theory (Online)
Chapter 27. Forecasting (Online)
Chapter 28. Markov Chains (Online)
Overview:
Introduction to Operations Research is the classic text on operations research. While building on the classic strengths of the text, the author continues to find new ways to make the text current and relevant to students. One way is by incorporating a wealth of state-of-the art, user-friendly software and more coverage of business applications than ever before. The hallmark features of this edition include new sections, updated problems, clear and comprehensive coverage of fundamentals, an extensive set of interesting problems and Indian cases, and practical examples that integrate advanced operations research software, making it an invaluable resource for learners.
Additionally, it highlights real-world applications such as sustainability, AI’s role in operations research, python for linear programming, supply chain network design, and tools like linear and integer programming, revenue management, and transportation problems, making it an invaluable resource for learners.
Key Features:
• Emphasis on Real Applications: Realistic examples and cases, plus many applications vignettes covering real life and industrial applications of various operations research tools and techniques.
• New topical coverage on the emerging need areas like sustainability, How AI is transforming Operations Research, Using Python for linear programming, Manufacturing scheduling models, Supply chain network design models, Large-scale optimization etc.
• India-centric case studies on applications of linear and integer programming, revenue management techniques, transportation problem, traveling salesman problem, vehicle routing problem, facility location, genetic algorithms, simulation, decision analysis and inventory management.
Introduction to Operations Research is the classic text on operations research. While building on the classic strengths of the text, the author continues to find new ways to make the text current and relevant to students. One way is by incorporating a wealth of state-of-the art, user-friendly software and more coverage of business applications than ever before. The hallmark features of this edition include new sections, updated problems, clear and comprehensive coverage of fundamentals, an extensive set of interesting problems and Indian cases, and practical examples that integrate advanced operations research software, making it an invaluable resource for learners.
Additionally, it highlights real-world applications such as sustainability, AI’s role in operations research, python for linear programming, supply chain network design, and tools like linear and integer programming, revenue management, and transportation problems, making it an invaluable resource for learners.
Key Features:
• Emphasis on Real Applications: Realistic examples and cases, plus many applications vignettes covering real life and industrial applications of various operations research tools and techniques.
• New topical coverage on the emerging need areas like sustainability, How AI is transforming Operations Research, Using Python for linear programming, Manufacturing scheduling models, Supply chain network design models, Large-scale optimization etc.
• India-centric case studies on applications of linear and integer programming, revenue management techniques, transportation problem, traveling salesman problem, vehicle routing problem, facility location, genetic algorithms, simulation, decision analysis and inventory management.
