Donor challenge: Your generous donation will be matched 2-to-1 right now. Your $5 becomes $15! Dear Internet Archive Supporter,. I ask only once a year. Introduction to Operations Research Techniques Allyn and Bacon, – Operations research – pages Hans G. Daellenbach,John A. George Snippet. Introduction to Operations Research Techniques. Front Cover. Hans G. Daellenbach, John A. George. Allyn & Bacon, Incorporated, – Operations research.
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Nature and Scope of operations research. What is an Operation Research. Operations Research is the science of rational decision-making and the study, design and integration of complex situations and systems with the goal of predicting system behavior and improving or optimizing system performance. Operations Research has been defined so far in various ways and still not been defined in an authoritative way. Some important and interesting opinions about the definition of OR which have been changed according to the development of the subject been given below: Operations research is the application of the methods of science to complex problems in the direction and management of large systems of men, machines, materials and money in industry business, government and defense.
The distinctive approach is to develop a scientific model of the system incorporating measurements of factors such as chance and risk, with which to predict and compare the outcomes of alternative decisions, strategies or controls.
The purpose is to help management in determining its policy and actions scientifically.
The application of the scientific method to study of operations of large complex organizations or activities, it provides top level administrators with a quantitative basis for decisions that will increase the effectiveness of such organizations in carrying out their basic purposes.
Operations research is the systematic application of quantitative methods, techniques and tools to the analysis of problems involving the operation of systems. Operations research is essentially a collection of mathematical techniques and tools which in conjunction with a systems approach, is applied to solve practical decision problems of an economic or engineering nature.
Operations research utilizes the planned approach updated scientific method and an interdisciplinary team in order to represent complex functional relationships as mathematical models for the purpose of providing a quantitative basis for decision-making and uncovering new problems for quantitative analysis.
This new decision-making field has been characterized by the use of scientific knowledge through interdisciplinary team effort for the purpose of determining the best utilization of limited resources. Operations research, in the most general sense, can be characterized as the application of scientific methods, techniques and tools, to problems involving the operations of a system so as to provide those in control of the operations with optimum solutions to the problems.
Operations research has been described as a method, an approach, a set of techniques, a team activity, a combination of many disciplines, an extension of particular disciplines mathematics, engineering, and economicsa new discipline, a vocation, even a religion.
It is perhaps some of all these things. Operations research may be described as a scientific approach to decision-making that involves the operations of organizational system. Operations research is a scientific method of providing executive departments with a quantitative basis for decisions regarding the operations under their control. Operations research is applied decision theory It uses any scientific, mathematical, or logical means to attempt to cope with the problems that confront the executive, when he tries to achieve a thorough-going rationality in dealing with his decision problems.
Operations research is a scientific approach to problem-solving for executive management. As the discipline of operations research grew numerous names such as operations analysis, systems analysis, and decision analysis, management science, quantitative analysis, decision science were given to it This is because of the fact that the types of problems encountered are always concerned with ‘effective decision’, but the solution of these problems do not always involve research into operations or aspects of the science of management.
A decision, which taking into account all the present circumstances can be considered the best one, is called an optimal decision. It is generally agreed that operations research came into existence as a discipline during World War II when there was a critical need to manage scarce resources.
However, a particular model and technique of OR can be traced back as early as in World War I, when Thomas Edison made an effort to use a tactical game board for finding a solution to minimize shipping losses from enemy submarines, instead of risking ships in actual war conditions.
About the same time A.
Erlang, a Danish engineer, carried out experiments to study the fluctuations in demand for telephone facilities using automatic dialing equipment. Such experiments were later on were used as the basis for the development of the waiting-line theory.
Some groups were first formed by the British Air Force and later the American armed forces opetations similar groups, one of the groups in Britain came to be known reseaarch Blackett’s Circus. This group, under the leadership of Prof.
Blackett was attached to the Radar Operational Research unit and was assigned the problem of analyzing the coordination of radar equipment at gun sites.
The efforts of such groups, especially in the area of radar detection are still considered vital for Britain in winning the air battle.
Following operahions success inntroduction this group similar mixed-team inteoduction was also adopted in other allied nations. After the war was over, scientists who had been active in the military OR groups made efforts to apply the operations research approach to civilian problems rfsearch to business, industry, rdsearch development, etc. There are three important factors behind the rapid development of using the operations research approach.
This industrialization also resulted in complex managerial problems, and therefore the application of operations research operaions managerial decision-making became popular.
Consequently, some important daelllenbach was made in various operations research techniques. Inhe developed the concept of linear programming, the solution of which is found by a method known as simplex method.
Besides linear programming, many other techniques of OR, such as statistical quality control, dynamic programming, queuing theory and inventory theory were well-developed before the end of the The use of computers made it possible to apply many OR techniques for practical decision analysis. During the s there was substantial progress in the application of OR techniques for civilian activities along with a great interest in the operatilns development and education of OR.
Many colleges and universities introduced OR in their curricula. These were generally schools of engineering, public administration, business management, applied mathematics, economics, computer science, etc. Today, however, service organizations such as banks, hospitals, libraries, airlines, railways, etc.
Its journal, OR Quarterly first appeared in In the same year, The Institute of Management Sciences TIMS was founded as an international society to identify, extend and unify scientific knowledge pertaining to management. Its journal, Management Science, first appeared in Mahalanobis established an OR team in the Indian Statistical Institute, Kolkata for solving problems related to national planning and survey. By the s OR groups were formed in introduuction organizations.
Educational and professional development programmes were expanded at all levels and certain firms, specializing in decision analysis, were also formed. The American Institute for Decision Sciences came into existence in It was formed to promote, develop and apply quantitative approach to functional and behavioural problems of administration.
It started publishing a journal, Decision Science, in Because of OR’s multi-disciplinary character and its application in varied fields, it has a bright future, provided people devoted to the study of OR can help meet the needs of society.
Some of the problems in the area of hospital management, energy conservation, environmental pollution, etc. This is an indication of the techniquues that OR can also contribute towards the improvement of the social life and of areas of global need.
However, in order to make the adellenbach of OR brighter, its specialists have to make good use of the opportunities available to them. Claim and complaint procedure, and public accounting. Break even analysis, capital budgeting, cost allocation and control, and financial planning. Models do riot, and cannot, represent every aspect of reality because of the innumerable and changing characteristics of the real-life problems to be represented.
However, a model can be used introsuction understand, describe and quantity important aspects of the system and predict the response to the system to inputs. In other words, a model is developed in order to analyze and understand the given system for the purpose of improving its performance as well as to examine the behavioral changes of a system without disturbing the ongoing operations.
For example, to study the now of material through a factory, a scaled diagram on paper showing the factory floor, position or equipment, tools, and workers can be constructed. It would not be necessary to give details opsrations as the color of machines, the heights of the workers, or the temperature of the building. In other words, for a model to be effective, it must be representative of those aspects of reality that are being investigated and have a major impact on the decision situation.
A system can easily be studied by concentrating on its key features instead of concentrating on every detail of it.
Introduction to operations research techniques / by Hans G. Daellenbach and Jonh A. George
This implies that the models attempt to describe the essence of a situation so that the decision-maker can study the relationship among relevant variables quickly to arrive at a holistic view. The key to model building lies in abstracting only the relevant variables that affect the criteria of the m easures-of performance of the given system and in expressing the relationship in a suitable form.
H owever, a model should be as simple as possible so as to give the desired result.
On the other hand, over simplifying the tschniques can also lead to a poor decision. Model enrichment is accomplished through the process of changing constants into variables, adding variables, relaxing linear and other assumptions, and including randomness.
The top three qualities of any model are: Besides these three qualities, other qualities of interest are i the cost of the model and its sophistication, ii the time involved in formulating the model, etc. More important than the formal definition of operationns model is tile informal one that applies to all of us, a tool for thinking and understanding before taking action. We use models all the time, even though most of them are subjective.
For example, we formulate a model when a we think about what someone will say if we do something, b we try to decide how to spend our money, or c we attempt to predict the consequences of some activity either ours someone else’s or even a natural event. In other words, we would not be able to derive or take any purposeful action if we did not form a model of the activity fast. OR approach uses this natural tendency to create models.
Introduction to operations research techniques
This tendency forces to think more rigorously and carefully about the models we intend to use. In general models are classified in eight ways as shown in Table 1. Such a classification provides a useful frame of reference for modelers. Classification Based on Structure. These models provide a physical appearance of the real object under study, either reduced in size or scaled up. Since these models cannot be manipulated and are not very useful for prediction, problems such as portfolio selection, media selection, production scheduling, etc.
Physical models are classified into the following two categories. Iconic models retain some of the physical properties and characteristics of the system they represent. An iconic model is either in an idealized form or is a scaled version of the system. In other words, such models represent the system as it is, by scaling it up or dower i.
Examples of iconic models are blueprints of a home, introductiin, globes, photographs, drawings, air planes, trains, etc. An iconic model is used to describe the characteristics of the system rather than explaining the system. This means that such models are used to represent a static event and characteristics that are not used in determining or predicting effects that take place due to certain changes in the actual system.
For example, the color of an atom does not play any vital role in the scientific daellenbachh of its structure.
Similarly, the type of engine in a car has no role to play in the study of the problem of parking. These models represent a system by the set of properties of the original system but does not resemble physically.
For example, the oil dipstick in a ear represents the amount of oil in the oil tank; the organizational chart represents the structure, authority, responsibilities and relationship, with boxes and arrows; and maps in different colors represent water, desert and other geographical features.
Graphs ultimo series, stock-market changes, frequency curves, etc. These models are less specific and concrete but are easier to manipulate and are more general than iconic models. These models use symbols letters, numbers and functions to represent variables arid their relationships for describing the properties of the system. These models are also used to represent relationships that cart be represented in a physical form.
Symbolic models can be classified into the following two categories.