4.5 __Optimization of System
Effectiveness__

The optimization of system effectiveness is important throughout
the system life cycle, from concept through the operation. Optimization is the
balancing of available resources (time, money, personnel, etc.) against
resulting effectiveness parameters (performance, operational readiness, etc.),
until a combination is found that provides the most effectiveness for the
desired expenditure of resources. Thus, the optimum system might be one
that:

(1) Meets or exceeds a particular level of effectiveness for
minimum cost, and/or

(2) Provides a maximum effectiveness for a given total
cost

Optimization is illustrated by the flow diagram of
Figure 4.5-1 which shows the optimization process as a feedback loop
consisting of the following three steps:

(1) Designing many systems that satisfy the
operational requirements and constraints

(2) Computing resultant values for effectiveness and
resources used

(3) Evaluating these results and making
generalizations concerning appropriate combinations of design and support
factors, which are then fed back into the model through the feedback
loops

Optimization also can be illustrated by the purchase of
a new car or, more specifically, by putting into precise, quantifiable terms
the rule, or criteria, that will be followed in the automobile selection
process. Although automobiles do have quantifiable characteristics, such as
horsepower, cost, and seating capacity, they are basically similar in most
cars of a particular class (low-price sedans, sports models, etc.). Thus the
selection criteria essentially reduces to esthetic appeal, prior experience
with particular models, and similar intangibles. In the same sense, the choice
of best design for the weapon system is greatly influenced by experience with
good engineering practices, knowledge assimilated from similar systems, and
economics. Despite this fuzziness, the selection criteria must be adjusted so
that:

(1) The problem size can be reduced to ease the choice
of approaches

(2) All possible alternatives can be examined more
readily and objectively for adaptation to mathematical representation and
analysis

(3) Ideas and experiences from other disciplines can
be more easily incorporated into the solution

(4) The final choice of design approaches can be based
on more precise, quantifiable terms, permitting more effective review and
revision, and better inputs for future optimization problems

The choice of parameters in
the optimization model also is influenced by system definition. The automobile
purchaser, for example, may not consider the manufacturer’s and dealer’s
service policies. If these policies are considered, the system becomes the
automobile plus the service policies. If service policies are not considered,
the system consists only of the automobile.

The optimization of system
effectiveness is a highly complex problem; there is a degree of interaction
among the factors which enter into consideration of this problem. The actual
techniques used to optimize system effectiveness will be described in greater
detail in Section
10 of this handbook. Table 4.5-1, for example, lists only some of the more
commonly-used techniques. These techniques are not peculiar to system
effectiveness optimization, nor are they limited to system
engineering.

This section is an
introduction to the Handbook from a top level, or system, viewpoint. The
remaining sections of this Handbook will expand upon the concepts introduced
in this chapter. They will cover: (1) the basic
reliability/maintainability/availability theory, (2) practical application of
the theory in terms of the design methodology and procedures of reliability
engineering at the equipment and system level, (3) procedures for insuring
that inherent reliability is not degraded during production and field
deployment of systems, and (4) steps that management must take to insure the
acquisition and deployment of reliable systems at minimum life cycle
cost.