This book is an easy guide for the lecturers and students. It helps in choosing the appropriate quantitative analysis techniques, applying the technique, making decision according to outcomes, and conducting the sensitivity analysis. This is an introductory book of operations research covers linear programming graphical solution, simplex method, integer programming, transportation problem, decision making models, and decision tree. At the beginning of each chapter the learning outcomes are listed, and review questions at the end of each chapter.

About the author

Dr. Yazan K.A-A. Migdadi is the associate professor of operations management at Yarmouk university, business administration department. He holds a bachelor degree in business administration and master of business administration from Yarmouk university Jordan, and holds Ph.D. in operations management from Bradford university school of management UK. For about ten years, he has been teaching courses related to operations research, operations management, and project management, for graduate and undergraduate students at UK and Jordan. 

Dr. Yazan has authored books in operations management and has published numerous papers in operations management and operations research, he is a reviewer for many international journals and editorial board member of some Journals. Dr. Yazan has held some academic positions as the head of business administration department at Yarmouk university Jordan. He was awarded best paper awards in some conferences.

1. Introduction to Operations Research 
   
   1. Learning outcomes 
      
   2. Terminologies 
      
   3. Definition of operations research 
      
   4. What is Operations Research? Management Science? 
      
   5. History of OR/MS 
      
   6. Operations research societies 
      
   7. Journals 
      
   8. The approach of quantitative analysis 
      
   9. The role of computer in quantitative analysis 
      
2. Linear programming: Graphical Method 
   
   1. Learning outcomes 
      
   2. Definition 
      
   3. The assumptions of linear programming 
      
   4. How to formulate the problem 
      
   5. How to represent the problem graphically and find the optimal solution 
      
   6. Sensitivity analysis 
      
   7. Review questions 
      
3. Linear Programming: Integer programming 
   
   1. Learning outcomes 
      
   2. Definition 
      
   3. Assumptions 
      
   4. Types of integer programming 
      
   5. Techniques to solve the integer problems 
      
   6. Review questions 
      
4. Linear Programming: Simplex method 
   
   1. Learning outcomes 
      
   2. Definition 
      
   3. Assumptions 
      
   4. How to make the required setup for the problem 
      
   5. Developing the initial simplex table 
      
   6. Improving the simplex table 
      
   7. Sensitivity analysis 
      
   8. Review questions 
      
5. Transportation problem 
   
   1. Learning outcomes 
      
   2. Definitions and introduction 
      
   3. Assumptions 
      
   4. How to develop the initial transportation table 
      
   5. How to improve the solution 
      
   6. Special cases in transportation 
      
   7. Review questions 
      
6. Decision making models 
   
   1. Learning outcomes 
      
   2. Components of decision 
      
   3. Principles of making uncertain decision 
      
   4. Decision making under risky environment principles 
      
   5. How to develop the payoff table 
      
   6. Decision tree 
      
   7. Review questions