No science has ever been born on a specific day. Operations research is no exception. Its roots are as old as science and society. Though the roots of OR extend to even early 1800s, it was in 1885 when Ferderick W. Taylor emphasized the application of scientific analysis to methods of production, that the real start took place.

During World War II, the military management in England called on a team of scientists to study the strategic and tactical problems of air and land defence. This team was under the direction of Professor P.M.S. Blackett of Univ. of Manchester and a former naval officer. 'Blackett's circus", as the group was called, included three physiologists, two mathematical physicists, one astrophysicist, one army officer, one surveyor, one general physicist and two mathematicians. Many of these problems were of executive- type. The objective was to find out the most effective allocation of limited military resources to the various military operations and to the activities within each operation. The application included the effective use of newly invented radar, allocation of British Air Force Planes to missions and the determination of best patterns for searching submarines. This group of scientists formed the first OR team.

The name operations research (or operational research) was apparently coined in 1940 because the team was carrying out research on (military) operations. The encouraging results of these efforts led to the formation of more such teams in British Armed Services and the use of such scientific teams soon spread to Western Allies - the United States, Canada and France. Thus though this science of operations research originated in England, the United States soon took the lead. In United States these OR teams helped in developing strategies for mining operations, inventing new flight patterns and planning of sea mines.

Post - World War II: Immediately after the war, the success of military teams attracted the attention of industrial managers who were seeking solutions to their problems. Industrial operations research in U.K. and U.S.A. developed along different lines. In U.K. the critical economic situation required drastic increase in production efficiency and creation of new markets. Nationalisation of a few key industries further increased the potential field for OR. Consequently OR soon spread from military to government, industrial, social and economic planning.

Today, the impact of operations research can be felt in many areas. This is shown by the ever increasing number of educational institutions offering this subject at degree level. The fast increasing number of management consulting firms speaks of the popularity of the subject. OR activities have spread to diverse fields such as hospitals, libraries, city planning, transportation systems, crime investigation, etc. Some of the Indian organisations using OR techniques are: Indian - Airlines, Railways; Defence Organizations, Fertilizer Corporation of India, Delhi Cloth Mills, Tata Iron and Steel Co., etc.

From the definitions of OR following characteristics can be extracted out.

1. Use of Interdisciplinary Teams

OR involves many number of variables and constraints. For a single person it is not possible to understand and analyze justifiably. Hence people from various disciplines are required to understand the OR problem, who applies their special knowledge acquired through experience to get a better view of cause and effects of the events in the problem and to get a better solution to the problem. This type of team approach will reduce the risk of making wrong decisions.

2. Complete System Orientation

A business may be considered as a system having various sub-systems. The decision made by any sub-system will have its effect on other sub-systems. When dealing with OR problems, one has to consider the entire system, and characteristics or sub- systems, the inter-relationship between sub-systems and then analyze the problem, search for a suitable model and get the solution for the problem. Hence it can be concluded that OR is a Systems Approach rather, than individual approach.

3. Involvement of Scientific Method

Various scientific methods are involved in OR to solve different kinds of problems. Scientific methods are based on derived logics and empirical relations from the past experiences. So, application of scientific methods leads to logical and sequential results, which are not depending on irrelevant assumptions.

4. Improvement in Quality of Decisions

OR provides various alternatives and let the user to select an optimal choice. This will definitely help him in making better and quick decisions. Hence, quality of decision can be improved.

5. Uncovering Hidden Problems

Sometimes, during solving the adopted problem, new problems are uncovered. These problems are mostly overlooked. For example, excess inventory provides flexibilities in managing the orders but on other hand it hides many problems related to manufacturing, human, finance etc. As uncovered problem can also affect the existing problem, it is very essential to solve these problem using different OR techniques.

An OR study is rooted in teamwork, where the OR analysts and the client work side by side. The OR analysts’ expertise in modeling must be complemented by the experience and cooperation of the client for whom the study is being carried out.

The principal phases for implementing OR in practice include

1. Definition of the problem.

2. Construction of the model.

3. Solution of the model.

4. Validation of the model.

5. Implementation of the solution.

1. Definition of the problem

Problem definition involves defining the scope of the problem under investigations. This function should be carried out by the entire OR team. The aim is to identify three principal elements of the decision problem: (1) description of the decision alternatives, (2) determination of the objective of the study, and (3) specification of the limitations under which the modeled system operates.

2. Construction of the model

Model construction entails an attempt to translate the problem definition into mathematical relationships. If the resulting model fits one of the standard mathematical models, such as linear programming, we can usually reach a solution by using available algorithms. Alternatively, if the mathematical relationship are too complex to allow the determination of an analytic solution, the OR team may opt to simplify the model and use a heuristic approach, or they may consider the use of simulation, if appropriate. In some cases, mathematical, simulation, and heuristic models may be combined to solve the decision problem.

3. Solution of the model

Model solution is by far the simplest of all OR phases because it entails the use of well-defined optimization algorithms. An important aspect of the model solution phase is sensitivity analysis. It deals with obtaining additional information about the behaviour of the optimum solution when the model undergoes some parameter changes. Sensitivity analysis is particularly needed when the parameters of the model cannot be estimated accurately. In these cases, it is important to study the behaviour of the optimum solution in the neighborhood of the estimated parameters.

4. Validation of the model

Model validity checks whether or not the proposed model does what it purports to do - that is, does it predict adequately the behaviour of the system under study? Initially, the OR team should be convinced that the model’s output does not include “surprises”. In other words, does the solution make sense? Are the results intuitively acceptable? On the formal side, a common method for checking the validity of a model is to compare its output with historical output data. The model is valid if, under similar input conditions, it reasonably duplicates past performance. Generally, however, there is no assurance that future performance will continue to duplicate past behaviour. Also, because the model is usually based on careful examination of past data, the proposed comparison is usually favorable.

5. Implementation of the solution

Problem definition involves defining the scope of the problem under investigations. This function should be carried out by the entire OR team. The aim is to identify three principal elements of the decision problem: (1) description of the decision alternatives, (2) determination of the objective of the study, and (3) specification of the limitations under which the modeled system operates.

When we broaden the scope of OR, we find that it has really been practised for hundreds of years even before World War II. Whenever there is problem of optimization, there is scope for the application of OR. Its techniques have been used in a wide range of situations:

1. In Industry

In the field of industrial management there is of chain of problems starting from the purchase of raw materials to the dispatch of finished goods. The management is interested in having an overall view of the method of optimizing profits. In order to take decision on scientific basis, OR team will have to consider various alternative methods of producing the goods and the return in each case. OR study should also point out the possible changes in the overall structure like installation of a new machine, introduction of more automation, etc. OR has been successfully applied in industry in the fields of production, blending, product mix, inventory control, demand forecast, sale and purchase, transportation, repair and maintenance, scheduling and sequencing, planning, scheduling and control of projects and scores of other associated areas.

2. In Defence

OR has a wide scope for application in defence operations. In modern warfare the defence operations are carried out by a number of different agencies, namely airforce, army and navy. The activities performed by each of them can be further divided into sub-activities viz. operations, intelligence, administration, training and the like. There is thus a need to coordinate the various activities involved in order to arrive at optimum strategy and to achieve consistent goals. Operations research, conducted by team of experts from all the associated fields, can be quite helpful to achieve the desired results.

3. Planning

In both developing and developed economies, OR approach is equally applicable. In developing economies, there is a great scope of developing an OR approach towards planning. The basic problem is to orient the planning so that there is maximum growth of per capita income in the shortest possible time, by taking into consideration the national goals and restrictions imposed by the country. The basic problem in most of the countries in Asia and Africa is to remove poverty and hunger as quickly as possible. There is, therefore, a great scope for economists, statisticians, administrators, technicians, politicians and agriculture experts working together to solve this problem with an OR approach.

4. Agriculture

OR approach needs to be equally developed in agriculture sector on national or international basis. With population explosion and consequent shortage of food, every country is facing the problem of optimum allocation of land to various crops in accordance with climatic conditions and available facilities. The problem of optimal distribution of water from the various water resources is faced by each developing country and a good amount of scientific work can be done in this direction.

5. Public Utilities

OR methods can also be applied in big hospitals to reduce waiting time of outdoor patients and to solve the administrative problems.

Monte Carlo methods can be applied in the area of transport to regulate train arrivals and their running times. Queuing theory can be applied to minimize congestion and passengers’ waiting time.

OR is directly applicable to business and society. For instance, it is increasingly being applied in L.I.C. office to decide the premium rates of various policies. It has also been extensively used in petroleum, paper, chemical, metal processing, aircraft, rubber, transport and distribution, mining and textile industries.

OR approach is equally applicable to big and small organizations' For example, whenever a departmental store faces a problem like employing additional sales girls, purchasing an additional van, etc., techniques of OR can be applied to minimize cost and maximize benefit for each such decision.

Thus we find that OR has a diversified and wide scope in the social, economic and industrial problems of today.

1. 1. Magnitude of Computations:

      1. 1. 1. 1. O.R. tries to find out optimal solution taking into account all the factors. In the modern society these factors are enormous and expressing them in quantity and establishing relationships among these require voluminous calculations which can only be handled by machines.

   2. Non-Quantifiable Factors:

      1. 1. 1. 1. O.R. provides solution only when all elements related to a problem can be quantified. All relevant variables do not lend themselves to quantification. Factors which cannot be quantified, find no place in O.R. Models in O.R. do not take into account qualitative factors or economical factors which may be quite important.

   3. Distance between Manager and Operations Research:

      1. 1. 1. 1. O.R. being specialist’s job requires a mathematician or a statistician, who might not be aware of the business problems. Similarly, a manager fails to understand the complex working of O.R. Thus there is a gap between the two. Management itself may offer a lot of resistance due to conventional thinking.

   4. Money and Time Costs:

      1. 1. 1. 1. When the basic data are subjected to frequent changes, incorporating them into the O.R. models is a costly affair. Moreover, a fairly good solution at present may be more desirable than a perfect O.R. solution available after sometimes.