Lecture_1_Introduction and overview of OR

1.1 Origin of Operations Research

The term Operations Research (OR) was first coined by MC Closky and Trefthen in 1940 in a small town, Bowdsey of UK. The main origin of OR was during the second world war – The military commands of UK and USA engaged several inter-disciplinary teams of scientists to undertake scientific research into strategic and tactical military operations.

Their mission was to formulate specific proposals and to arrive at the decision on optimal utilization of scarce military resources and also to implement the decisions effectively. In simple words, it was to uncover the methods that can yield greatest results with little efforts. Thus it had gained popularity and was called “An art of winning the war without actually fighting it”

The name Operations Research (OR) was invented because the team was dealing with research on military operations. The encouraging results obtained by British OR teams motivated US military management to start with similar activities. The work of OR team was given various names in US: Operational Analysis, Operations Evaluation, Operations Research, System Analysis, System Research, Systems Evaluation and so on.

The first method in this direction was simplex method of linear programming developed in 1947 by G.B Dantzig, USA. Since then, new techniques and applications have been developed to yield high profit from least costs.

Now OR activities has become universally applicable to any area such as transportation, hospital management, agriculture, libraries, city planning, financial institutions, construction management and so forth. In India many of the industries like Delhi cloth mills, Indian Airlines, Indian Railway, etc are making use of OR activity.

1.2 Concept and Definition of OR

Operations research signifies research on operations. It is the organized application of modern science, mathematics and computer techniques to complex military, government, business or industrial problems arising in the direction and management of large systems of men, material, money and machines. The purpose is to provide the management with explicit quantitative understanding and assessment of complex situations to have sound basics for arriving at best decisions.
Operations research seeks the optimum state in all conditions and thus provides optimum solution to organizational problems.

Definition: OR is a scientific methodology – analytical, experimental and quantitative – which by assessing the overall implications of various alternative courses of action in a management system provides an improved basis for management decisions.

1.3 Characteristics of OR (Features)

The essential characteristics of OR are
1. Inter-disciplinary team approach – The optimum solution is found by a team of scientists selected from various disciplines.
2. Wholistic approach to the system – OR takes into account all significant factors and finds the best optimum solution to the total organization.
3. Imperfectness of solutions – Improves the quality of solution.
4. Use of scientific research – Uses scientific research to reach optimum solution.
5. To optimize the total output – It tries to optimize by maximizing the profit and minimizing the loss.

1.4 Applications of OR

Some areas of applications are
• Finance, Budgeting and Investment
? Cash flow analysis , investment portfolios
? Credit polices, account procedures
• Purchasing, Procurement and Exploration
? Rules for buying, supplies
? Quantities and timing of purchase
? Replacement policies
• Production management
? Physical distribution
? Facilities planning
? Manufacturing
? Maintenance and project scheduling
• Marketing
? Product selection, timing
? Number of salesman, advertising
• Personnel management
? Selection of suitable personnel on minimum salary
? Mixes of age and skills
• Research and development
? Project selection
? Determination of area of research and development
? Reliability and alternative design

1.5 Phases of OR

OR study generally involves the following major phases
1. Defining the problem and gathering data
2. Formulating a mathematical model
3. Deriving solutions from the model
4. Testing the model and its solutions