Sesiunea de Comunicări Ştiinţifice Studenţeşti, SCCS 2014, Secţiunea 12.1

The increasing complexity of the systems designed in the recent decades imposed the exclusive use of computers in their design. A great number of numerical methods have been developed and implemented in this domain, methods that today cover all the design stages, from the requirements study to the technological arranging. Obviously, all these activities are under the maximize performance sign and their subordination to the optimization process is inevitable, leading to the appearance of design algorithms that are more sophisticated and more powerful.

Optimization is that part of the mathematical field concerned with the retrieval of the maximum or minimum of a function. Optimization includes a large area of techniques from different fields such as research, artificial intelligence or information technology and it is used to improve various processed regardless of the domain they belong to (health, business, financial, industrial, etc.).

Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics stated by Darwin (“It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is most adaptable to change.”).

The mechanism is similar to the biological process of evolution. This process has as main feature the fact that only the species that adapt better to the environment are able to survive and to evolve , while those less able to adapt fail to survive and disappear as a result of natural selection. The probability that the species survives and evolves over generations is direct proportional with the degree of adaptation, which in terms of optimizing means approaching the optimum solution.

A genetic algorithm is a model of machine learning which derives its behavior from a metaphor of the processes of evolution in nature to solve optimization problems.

The basic idea of a genetic algorithm is to start with a population of solutions, each more powerful than the previous. A Genetic Algorithms operates through a simple cycle of stages:

1. Creating a population of "members" (solutions candidate to solve a problem); 2. Selecting the members who adapted better to the needs of the problem to be solved; 3. Reproduction (using the genetic operators of crossover and mutation, in order to obtain new member); 4. Evaluating the degree to which the new members respond better to problem solving; 5. Replacing the old population with new population from the new generation.

Sesiunea de Comunicări Ştiinţifice Studenţeşti, SCCS 2014, Secţiunea 12.1

As one of a variety of new tools coming out of modern computing and data modeling, decision support system (DSS) is getting a lot of attention from many businesses as a way to promote better projections, management and analysis within a company or business. DSS comes in many forms, and the term basically refers to a computer-aided system that helps managers and planners make decisions. DSS combines the power of human thought with the power of modeling systems to get optimal, informed decision making.

That said, there are a lot of different ways managers can use decision support software to their advantage if they are open to exploring decision support systems applications and uses.One is the clinical decision support system (CDSS) - an interactive Expert system Computer Software, which is designed to assist physicians and other health professionals with decision making tasks, such as determining diagnosis of patient data. Another specific example concerns American Airlines that produced a DSS that helps decide how much to overbook and how to set prices for each seat so that a plane is filled and profits are maximized.

Selection

Reproduction

Evaluation

Population

Replacement

Sesiunea de Comunicări Ştiinţifice Studenţeşti, SCCS 2014, Secţiunea 12.1

Develop a decision support application for optimal distribution of equipment by intervention teams. The equipment represent the scope of the intervention. Knowing the details of the equipment and team, calculate the cost of the intervention in time and distance. The type of road and the traffic are taken into account for a realistic assessment. Using this cost function, and having the restriction that a team has a maximum load, calculate the optimal distribution.

The application that uses a Model – View - Controller architecture is developed in Netbeans, and for the design, implementation and administration of the database uses MySQL Workbench.