Home | | **Operations Research An Introduction** | | **Resource Management Techniques** | Phases of an OR Study

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.

**PHASES OF AN OR STUDY**

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.

As a
decision-making tool, OR is both a science and an art. It is a science by
virtue of the mathematical techniques it embodies, and it is an art because the
success of the phases leading to the solution of the mathematical model depends
largely on the creativity and experience of the operations research team.
Willemain (1994) advises that "effective [OR] practice requires more than
analytical competence: It also
re-quires, among other attributes, technical judgement (e.g., when and how to
use a given technique) and skills in communication and organizational
survival."

It is
difficult to prescribe specific courses of action (similar to those dictated by
the precise theory of mathematical models) for these intangible factors. We
can, how-ever, offer general guidelines for the implementation of OR in
practice.

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.

Phase 3,
dealing with *model solution,* is the
best defined and generally the easiest to implement in an OR study, because it
deals mostly with precise mathematical models. Implementation of the remaining
phases is more an art than a theory.

**Problem definition **involves
defining the scope of the problem under investigation. 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
alter-natives, (2) determination of the objective of the study, and (3)
specification of the limitations under which the modeled system operates.

**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 relationships 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, as the case analyses in Chapter
24 demonstrate.

**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 behavior 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 behavior of the
optimum solution in the neighborhood of the estimated parameters.

**Model validity **checks whether or not the
proposed model does what it purports** **to
do-that is, does it predict adequately the behavior 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 behavior.
Also, because the model is usually based on careful examination of past data,
the proposed comparison is usually favorable. If the proposed model rep-resents
a new (nonexisting) system, no historical data would be available. In such
cases, we may use simulation as an independent tool for verifying the output of
the mathematical model.

**Implementation **of the
solution of a validated model involves the translation of** **the results into understandable operating instructions to be
issued to the people who will administer the recommended system. The burden of
this task lies primarily with the OR team.

Study Material, Lecturing Notes, Assignment, Reference, Wiki description explanation, brief detail

**Related Topics **

Copyright © 2018-2020 BrainKart.com; All Rights Reserved. Developed by Therithal info, Chennai.