The process is comparing the inputs to the collected information in our system. There are 532 different vehicles in our system, each with their own ratings. Once the inputs are compared, the final output is three different vehicles that most closely match the preferences of the user. .
The environment of our system is composed of society and changes in tastes. If advertising and brand name are important to our user, these societal pressures will have an affect on how effective our system is. The suggested vehicles may not match what the user thinks he/she needs, making the decision support system ineffective for that user. Our system is also bounded by time. New vehicle models come out every year, but as we cannot predict future car models, we have taken the information for this past year, 2003, as the basis for our system. So when we claim that our system will pick out a new vehicle, we mean new in terms of 2003 only. This can be modified, however, as new models can easily be added to the database and old ones removed. .
Our system uses a mathematical model in Excel to come up with results. The model has three basic components: the decision variables are the preferences of the user. For example, the cost of the car is a decision variable. The uncontrollable parameters in our model include the number of doors on the vehicles and the class of the vehicle. The result variables are the outputs. This normative model gives results that are the best possible given all possible alternatives. So out of the 532 car listed, our outputs are the optimal choices. The process of optimization is used to get the smallest difference between user variables and the uncontrollable parameters of our model to get the result. .
Given this model, different scenarios can be played with. Decision variables can be changed to aim for the worst or best possible car given the cost constraint. Other scenarios similar to this one can also be played out.
