Methodology, OR Project's Phases and Problem Formulation Assignment Help

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Methodology of Operations Research

            The systematic methodology developed for O.R. study with problems involving conflicting multiple objectives policies and alternatives. OR in the final analysis is a scientific methodology which is applied to the study of the operations of large complex organizations or activities with a view to assessing the overall implications of various alternative courses of action thus providing an improved basis for management decisions.

            Due to the complications involved OR project is broken in the following phases.

1.      Perception and formulation of the problem.

2.      Setting out or construction of a model of decision process

3.      Specification of alternative courses open.

4.      Solution of the model evaluating the outcome of each course of action of the basis of given criterion.

5.      Selection of a particular course and resolution of the decision process.

6.      Pre implementation tests an establishment of control over the solution with a degree of precision.

7.      Implementation of the decision reached.

Step 1. Problem Formulation. This is a major phase in which the OR team should formally formulate the management problem and then transform it into a research problem. For purposes of formulating the management problem in the first instance, the OR team makes a thorough analysis of organizational structure and function the communication and control systems the objectives and policies of  the organization and so on

            In O.R. approach problem formulation has a different meaning than what is understood in common parlance. The major components of a problem are:

-          A group or an individual decision maker who faces a problem.

-          The environment wherein the problem is supposed to lie. It is not unlikely that the roots of problem are traced to a different area. Therefore it is wise to call the entire organization as the second component.

-          Objective (S)

-          Alternative courses of action that affect the objective (s). The outcomes are not known apriori and the decision maker (s) are in a dilemma as to which courses of action is to be chosen.

-          Constraints

Step 2. Constructing a Mathematical Model. The next stage in the O.R process is to try to express the relevant features of the system under study in terms of a mathematical model. The general form a mathematical model is:

                                                            E = f (xi, yi)

Where f represents a system of mathematical relationships between the measure of effectiveness of the objectives sought (E) and the variables. Both controllable (xi) and uncontrollable (yi).

Step 3. Deriving Solutions from the Model. Once the mathematical model is formulated the next step is to determine the value of decision variables that optimize the given objective function. The various mathematical techniques for arriving at such solutions comprise much of the contents of this text.

Remark. In addition to the solution of the model, it is also sometimes essential to perform sensitivity analysis, i.e., determine the behavior of the system changes in the system parameters and specifications. This is done because the input data (parameters) may not be accurate or stable and the structural assumptions of the model may not be valid. Thus sensitivity analysis is an essential part of this phase of the methodology and must not be overlooked.

Step 4. Testing the model. Since in once a model is only a partial and simplified representation of reality, the results are to be tested against the real word experience in order to establish the model's credibility. Several simplifying assumptions are made while initiating the model building. These assumptions can be relaxed one by one to see the reaction of the model results to such relaxation.

Similarly inclusion of some inappropriate variables in the model and exclusion of some appropriate variables from the model, just for purposes of abstraction and simplification may require review so that their impact on the model vis-à-vis reality is analysed form a proper perspective. Model distortion through simplification intentional or otherwise has to be properly assessed with the help of previous experience judgment actual test data and similar devices.

Step 5. Establishing controls over the solution. Complex models of specific problems produce decision rue ties which could be used a procedures to take care of repetitive situations without having to build a model ever time. This naturally calls for incorporation of controls into the model so that they are adaptable for a range of problem. In the dynamic business world, values of parameters keep changing; some parameters become outdated new parameters emerge unless taken care of will leave the model solution utterly useless for later similarly problems. A conscious control procedure is to be established for detecting significant changes in the parameters and relationships and for specifying the action to be taken or adjustments to be made in the solution when a significant change occurs.

Step 6. Implementing the Solution.  This is certainly the most important phase of the study, because it is only after a proposal has been implemented that the benefits accrue. When an operationally feasible solution has been decided upon, stage is set to put that solution into practice. This is often far from straight forwardness as solutions which look feasible on paper may conflict drastically with the ideas and capabilities of the people involved in the system. Many perfectly sound (theoretically) OR recommendations do not reach implementation because they prove unworkable in practice.

            Finally no system is even completely static and it is always necessary to monitor the environment within which a system operates to ensure that changing conditions do not render a solution inappropriate. It is important to ensure that any solution implemented is continuously reviewed and (if necessary) updated and modified in the light of a changing environment. A changing economy, fluctuating demand and model enhancements requested by managers and decision makers are only a few examples of changes that might require the analysis to be modified.

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