Is Operational Research A Science Essay

Is Operational Research A Science ? Essay, Research Paper

TABLE OF CONTENTSINTRODUCTION. DEFINITION OF SCIENCE & O.R.. HISTORY OF O.R.. METHODOLOGY OF O.R. 1 formulating the problem 2 constructing the model 3 deriving a solution 4 testing the model & evaluating the solution 5 implementing & maintaining the solution. APPROACHING THE PROBLEM (AN EXAMPLE). CONCLUSION. BIBLIOGRAPHYINTRODUCTION The purpose of this essay is to critically determine whether the nature of OR is scientific or not. The procedure will be to give a brief introduction of OR and the methodology that it follows, to asses the scientific nature of the steps involved and, finally, to observe any points of general differences between Or and other sciences, in order to determine if OR is a science.Definition of Science , Philosophy of (Encyclopedia Britannica)A discipline in which the elements involved in scientific inquiry – · observational procedures · patterns of argument · methods of representation and calculation · metaphysical presupposition. Are analyzed and discussed; and grounds of their validity are evaluated from the points of view of formal logic, practical methodologies and metaphysics. The subject has been approached both with ontal preoccupation with a concern for what kinds of entities can properly figure in scientific theories: and with epistemic interests with concern for the concepts and methods employed in studying natural and human phenomena.Definition of OR (Encyclopedia Britannica)The application of scientific methods to the management of organized military, governmental, commercial and industrial systems. It is distinguished from systems engineering in that it focuses on systems in which human behavior is important. Essential characteristics: from problem solving to model testing and solution. · techniques · allocation · inventory · replacement and maintenance · queuingHISTORY OF ‘ OR’In order to understand OR it is essential to give a brief history of its development. In a sense OR has existed since the contemporary second industrial revolution which took place in the last century and it was concerned with automation or mechanization of menial work. However it really began as a separate discipline in 1937 in Britain as a result of the initiative of A.P.Rowe superintended of the Bawdsey Research Station. He led a group of scientists of mixed disciplines to teach the army how to use the then newly developed radar to locate enemy aircraft’s. This was followed by the aim to analyze complex military operations with a view of improving their efficiency. After the war the discipline was spread in the army and the industry around the world. The first course in nonmilitary techniques was introduced at Massachusetts Institute of Technology in 1948. In the UK courses were initiated at the University of Birmingham in the early 1950’s.The methodology of OR includes the following steps. 1. Formulating the Problem 2. Constructing the Model 3. Deriving a solution 4. Testing the model and evaluating the solution 5. Implementing and maintaining the solution.To determine if OR is a science we have to analyze the steps it involves and asses their scientific value.1. Formulating the ProblemPitfalls of Analysis “Problem formulation is the intellectual Process by which a problem situation is translated into a specific problem” OR (in order to formulate a problem) takes into consideration five factors. These include: 1. The time available for the OR team to work on the formulation of the problem. Depending on the urgency, impact and money involved the OR team can provide more research or less in order to formulate a problem. It is obvious that some problems need more time to be formulated, depending on their complexity or less time when a decision needs to be taken urgently. 2. The second factor is the decision maker/s, obviously the person/s who is solving this problem needs to be taken into consideration. This person/s is dissatisfied with a situation, is involved in and is looking for an alternative state of affairs . 3. As a natural sequence to that, the third factor is the objectives that the decision maker has. Obviously these objectives have not been achieved under the present situation to the degree desired by the decision maker. 4. The forth factor involved is the system , or environment that the suggested solution is going to operate in. 5. And finally the fifth factor involved is the alternative decision that can be taken. Obviously the problem exists because there are alternative courses of action. At this point someone can argue that there is a number of constraints more difficult to detect, such a cultural, institutional, and professional backgrounds of client and analyst. Which could effect the formulation of a problem, and partially predetermine the final conclusions of the work. But this “subjectivity” that these constraints are leading to are not effecting the scientific “objectivity” of the “formulation” process as the same constraints are also affecting other sciences, such as economics. Economics is developed by scientists/economists preoccupied (like analysts) with beliefs, ideas and education influencing their science.2. Constructing a ModelIn OR the construction of the model of a problem is crucial. Models are an approximation of reality easier to control and manipulate in experiments. Generally there are three kinds of models 1. The Iconic, where the relevant properties of the real thing are represented by the properties themselves usually with a change in scale. For example, models of the Galougy are used on a smaller scale, maps, photographs. These models are specific but difficult to use for experiment purposes and manipulated. 2. The Analogues models are a set of properties to represent another set of properties. For example contour lines on a map can be used to represent the elevation of the landscape. In general analogues are less specific but easier to manipulate than Iconic models in an experiment. 3. The Symbolic models use letters and symbols to represent different variables and their relationships. Because of their abstraction, they are the easiest to manipulate and control experimentally, usually they take the form of mathematical relationships that reflect the structure of what they represent. As these models have to be as accurate as possible and as easy to solve as possible, the OR team has to keep in mind that the decision maker must understand the solution and be capable of using it. Therefore the model has to be simplified, to the point where there is no significant loss of accuracy. This task is not easy, and only through experience the OR team can do it. Also the availability of data is an important factor in the construction of the model. Because social and economic statistics are collected according to classification schemes that are often dictated not by logical to theoretical considerations but by expediency the availability of data the feasibility of making estimates and the operating procedures of the data gathering office. The skeptic will argue, that due to the simplification of the models the problems arising from the availability of data, the procedure is not scientific. But in the first case the simplification of the problem only goes to the point where accuracy is not lost. In the case of problems arising due to availabili

ty of data , the scientific objectivity is not lost because economics for instance is based on the same sources of data, and it still is scientific. Of course there are cases where manipulation of data can bring the desired results or even support contradicting theories but, this is due to the communication and interpretation problems which can be surpassed through more careful examination of the data. . Deriving a solutionThe procedure of deriving a solution is realized through the use of variety of techniques which are shown below: Operational Research Techniques – by S. French, R. Hartley, L.C. Thomas and D.J. White 1. Linear programming 2. The transportation algorithm 3. The Hungarian assignment algorithm 4. Sequencing and Scheduling 5. Critical path analysis 6. Optimal Routing 7. Deterministic inventory control 8. Probabilistic inventory 9. Queuing theory 10. Replacement maintenance 11. Dynamic Programming 12. Simulation In order, for the model to help as evaluate alternative policies efficiently we need to select the appropriate techniques. This depends on the characteristics of the model. There are two types of techniques: 1. Analytic 2. Numerical Analytic procedures are deductive and numerical procedures are inductive.4. Testing the model and evaluating the solution A model should be tested continuously while it is being constructed. It is a good model if it can predict the effect of changes in the system on the system’s overall effectiveness. The adequacy of the model can be tested by determining how well it does predict the effect of these changes. The solution can be evaluated by comparing the results obtained without applying the solution with those obtained when the solution is applied.5. Implementing and maintaining the solutionThere is a common tendency to think of the implementation of the problems solution that is obtained by research as an activity that is initiated after the research is completed. However in OR, because the objective is to improve the performance of the systems involved the research is not completed until that improvement is obtained and unless it is maintained. The tested solution must be translated into a set of operating procedures capable of being understood and applied by the personnel who will be responsible for their use. Approaching the problem(an example)As we saw the different steps involved in the methodology of OR are scientifically backed. This was proved with the use of examples of procedures used in OR parallel to other sciences. However the solutions that OR would produce to a problem, even if they offer the optimum, do not guarantee their use. As we said before in the paper, the OR team will formulate the problem, and take into consideration the constraints of the immediate environment of it. Lets consider the example of the OR team investigating the problem which is the most profitable option, out of two companies to supply a number of Hi-tech military Helicopters for the British army. The OR team is working for the British army and the D.M. is the government. The constraints involved are the long and short run costs ,the reliability of the helicopters and is also known that company ‘B’ is British whereas company ‘A’ is not . After considering all constraints to minimize costs (buying and running costs, spare parts etc.).The team decides the most economical option is to buy the helicopter from company A. As it is almost 30% – 40% cheaper as an option, in the short and long run and more reliable than company B. The suggestion is made through the army to the decision maker that happens to be the government. Contrary to the suggestion the decision maker decides to buy the helicopters from company B although there are serious disadvantages. But the choice was made, taking into consideration the suggestion and another constraint that was out of the jurisdiction of the OR team Company B was British whereas Company A was not. This is a clear example on how in OR there can be taken under consideration a factor which cannot be quantified, that is analyzed objectively, detailively. Therefore although OR as a methodology is quite scientific, it can be not so objective, depending on what the decision maker and the ‘OR’ team consider as constraints in the problem. In the previous example, the constraints of the British identity of company B outweighed all the objective, numerically calculated advantages of the other constraints such as running costs. It is obvious that ‘O.R.’ is used and restricted by the goals ,of the organization is serving , is pursuing .Therefore the case for ‘O.R’ as a science is significantly weakened . There is however an alternative , based on the paper of P.KEYS ‘O.R is considered as a Technology’ . Technology as such in its modern view has two basic characteristics , its aim and the methods used . In the case of ‘O.R’ its aim may be the production of information about systems which may be able to improve the effectiveness of an organization . And the methods used to produce this information are scientific in character , as we did see previously. ‘O.R’as other technologies are serving an organization , not a person , or society as a whole . For ‘O.R’ to produce information , will design abstract systems and they will take the form of physical systems which will be the methods of achieving its aims. ConclusionCONCLUSION OR started its formal existence in the mid 1930’s, because like many other sciences it emerged out of a convergence of an increased interest in some class of problem and the development of scientific methods, techniques and tools which are adequate to solve this problem. OR is applying scientific methods to give solutions . Therefore in method it is scientific like the other sciences, but unlike the other sciences it takes into consideration constraints that sometimes cannot be numerically evaluated. Whether it takes “subjective” constraints into consideration or not, is heavily dependent on how it is perceived as a way of deriving the “optimal solution”.For instance, when it is considering constraints, ethics and attitudes as in the example of helicopters. Different decision makers would derive different solutions because their subjective attitudes would most probably differ between them . Therefore it is a science in methodology, but because of its flexibility and the number of different techniques it is using, it can be applied in ways where “subjective” judgment can outweigh the objectivity of other factors. Therefor it could be considered as a technology based on the fact that as other technologies , it has aims , to serve an organization , and is using scientific methods as we did see . So ‘O.R’ could be considered partially scientific or more as a technology , depending on the prospective of the user or as a technology based on its similarities with the way other technologies operate.Bibliography1. Operational Research Techniques by S. French, R. Hartley, L.C. Thomas, & D.J. White 2. Operational Research Techniques by Douglas White, William Donaldson, & Norman Lawrie 3. Introduction to Operations Research by C. West Churchman, Russell L. Ackoff, & E. Leonard Arnoff 4. Pitfalls of Analysis Edited by Giandomenico Majone & Edward S. Quade 5. Fundamentals of Operations Research by Russell L. Ackoff & Maurice W. Sasieni 6 O.R is considered as a technology by P.Keys