Heuristics, Rules of Thumb, are combinations or programs which we can develop and employ to help us make decisions and solve problems.

Systems Engineering is the study of multi-faceted problems and the complex interrelations that make them up. Systems analysis and control is applied to fields as diverse as biomechanics, telecommunications, finance, and transportation. Despite the variety of applications of the systems technology, the basic methodology is similar in most cases.

The process begins with the analysis of a complex problem to gain an understanding of its current state. Relationships are calculated along with the dependent and independent variables. This information is then charted to provide a physical representation of the state of the system.

The next step in systems analysis involves determining the state the system will be in if left unchanged. To calculate what will happen in the future, the systems analyst must simplify the system and apply mathematical models to the result. These simulations will play a large role in this step of the systems process. The final step of systems analysis is determining how the final results should be changed to improve the system and furthermore calculating what changes today will bring about the desired end results.

Simulation is both the most time consuming and the most important part of the systems analysis process; it is therefore is the brunt of systems methodology. By making a scaled version of the subject they are working on, the engineers are able to become more familiar with the workings of the subject before working on the actual version. Furthermore, it is easier and cheaper to test a simulation of a device than it is to test the real thing. Imagine designing an aircraft without using blueprints, air flow tests over a piece of the wing, or a scaled down model of the entire craft.

The challenges of systems simulation are three-fold. First of all, perfect accuracy cannot be obtained without infinite effort. A simulation will always approximate the actual behavior of the system, and will be incorrect. The more time and effort put into a simulation the more accurate it becomes. A tradeoff must be reached between the cost of the simulation and its accuracy.

The second challenge of system simulation is that systems often consist of varieties of elements each operating with different sets of criteria. For example, in looking at an automobile as a system, it is easy to notice that the drive chain and tires operate with rotational properties, the brake system uses hydraulics, the control system for the brakes is electrical, and the brakes themselves use transitional motion. Yet, all of these systems operate together in harmony. Therefore, it is necessary to have a common language for all of the various principals to relate.

The final challenge offered by simulation is where to begin simulating a large, complex system. Large systems, such as those analyzed with modern systems engineering, will exhibit different initial properties depending on where you are focused. It is important for the systems analyst to understand this and either eliminate irrelevant variables or determine what part of the system should be focused on first to increase the efficiency of the analysis.

Analyzing a chess match is a similar challenge because chess is also a complex problem, and in that similarity, the methods used in its analysis may prove to be useful to systems engineers. In chess, rules of thumb are important and powerful, and yet are easy to learn. It is for these three reasons that such nuggets of advice are often given out to novice chess players to improve their game; they are told to avoid double pawns, gain control of the center and do not move the same piece many times in a row.

Each of these statements can be analyzed and explained by chess grandmasters. Great chess players will even qualify and add exceptions to such rules to show that they are semi-conditional. For novice players, however, rules of thumb are treated as if they are unconditional, as if they are immutable because they apply a large percentage of the time. Using these rules of thumb, novices can simplify the complex chess problem into a manageable game.

Systems simulation can be improved through the use of these rules of thumb. By incorporating concepts that apply to a large enough range of situations, system simplification can be improved. Different categories of systems will have different sets of rules of thumb, but for similar fields, such as manufacturing or robotics, similar rules of thumb will apply. Some examples of rules of thumb currently being used in systems engineering follow:

1. In bode simplification of the frequencies inherent in systems, factors more than 10 times less than the overriding frequency and therefore 10 times greater from the axis of the plot can be ignored.
2. To further simplify bode plots add lead and lag compensation in factors of 10.
3. In manufacturing systems, such as assembly line design and analysis, focus first on bottlenecks to improve performance.
4. In computer and robotics systems, when something fails, check the hardware setup and wire connections first.
5. When searching for the optimal point, begin with the outside corner that has the greatest change between it and its neighbors.
6. The following table describes the rule of thumb for interrelating various elements of a couple different types of systems as shown in Table 1.

As is shown above, rules of thumb aid the three-fold challenges presented by simulations in systems engineering design. First of all, by simplifying the simulation procedures, rules of thumb make it easier and less expensive to create simulations, and therefore improve its cost-benefit ratio. Secondly, rules of thumb can be used to relate various different types of system nodes to capacitors, resistors, and masses allowing simplified interrelation between various types of systems. Finally, rules of thumb provide suggestions as to where to begin analyzing your simulation as well as how to illuminate parts that affect the system minimally.

When approaching a systems design problem it definitely beneficial to learn about the rules of thumb that apply to that system. Using these helpful tools will make simulation and analysis an easier process and will help checkmate the system at hand.

Heuristics 2

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