What is OR?
Definition of Operations Research
Operational research (OR) encompasses the development and the application of a wide range of problem-solving methods and techniques applied in the pursuit of improved decision-making and efficiency, such as mathematical optimization, simulation, queueing theory and other stochastic models. The OR methods and techniques involve the construction of mathematical models that aim at describing a problem. Because of the computational and statistical nature of most of the techniques, OR also has strong ties to computer science and analytics. Because of its emphasis on human-technology interaction and of its focus on practical applications, OR has overlap with other disciplines, in particular industrial engineering and operations management, and draws on psychology and organization science.
OR is the process of making better decisions through data analysis, mathematical modeling, optimization, and other analytical methods.
OR is a discipline that attempts to aid managerial decision making by applying a scientific approach to managerial problems that involve quantitative factors.
OR is a scientific field on better decision-making by applying an analytical approach to a variety of problems, including quantitative factors.
Overall, OR is a discipline on the process of making better decision through the development and the application of a wide range of problem-solving methods and
OR is an academic discipline. It is the interdisciplinary area of the skills and methods involved in making decisions based on scientific knowledge.
OR is a decision making process.
OR takes a scientific approach. It utilizes theory and methods in mathematics, probability, statistics, and computing adapted and applied to the identification, formulation, solution, validation, implementation, and control of decision-making problems.
OR is concerned with development and application of quantitative analyses to the solution of problems faced by managers of public and private system.
OR considers quantitative factors. The element of decisions-making environment frequently lend themselves to quantification. Appropriate analysis of these quantitative elements can yield significant inputs for the purpose of decision-making.
History of Operations Research
- ~ 17th, Probability based problem solving by Christiaan Huygens and Blaise Pascal
- ~ 1890, Scientific Management by Frederick Taylor
- ~ 1900, Control chart (Project Scheduling) by Henry Gantt
Systems change over time by Andrew A. Markov
Network model: assignment approach
- ~ 1910, Optimal Inventory Theory by F. W. Harris
Average waiting time for telephone callers (Queuing Theory) by E. K. Erlang
- ~ 1920, Quality control charts William Shewart
Quality Control (Sampling) by H. Dodge and H. Romig
- ~ 1930, Game Theory by Jon von Neuman and Oscar Morgenstern
- ~ 1940, Simplex method (Linear Programming) by George Dantzig
- ~ 1950, Non-linear Programming by H.Kuhn and A.W.Tucker
Integer Programming by Ralph Gomory
PERT and CPM (Project Scheduling)
Dynamic Programming by Richard Bellman
- ~ 1960, Queuing Theory by John D.C. Little
Management science as “the business use of OR” by Stafford Beer
- ~ 1980, Multiple-criteria decision-making (MCDM) by Stanley Zionts
New Linear Programming by N. Karmarkar
Classification of Operations Research Model
– Linear optimization: Linear programming (LP), Integer programming (IP)
Transportation and assignment model
Multi-criteria decision-making programming (MCDM)
– Nonlinear optimization: Classical model, Search model, Nonlinear programming.
– PERT-CPM, Dynamic programming, Inventory model, Simulation model
– Decision analysis model, Markov model, Queuing Model
Areas of Operations Research
- Business Analytics
- Computer Science
- Data Mining
- Decision Analysis/Data Science/Big Data
- Decision Analysis
- Game Theory
- Graph Theory
- Industrial Engineering
- Inventory Control
- Mathematical Modeling
- Mathematical Optimization
- Policy Analysis
- Probability and Statistics
- Project Management
- Queuing Theory
- Social Network / Transportation Forecasting Models
- Stochastic Process
- Supply Chain Management