A system and method for automatically assigning multiple tasks with multiple objectives to multiple teams (group of technician on field) is provided. This invention provides a means to minimize total operation cost and business process through the optimization consisting of the best assignment of resources to available task. In the tasks assignment and resource allocation method disclosed herein, new genetically adapted search agent (Genetic algorithm) are employed to improve the population, to provide the most optimum match matrix among jobs and teams, and calculate the best score of the fittest chromosome. The assignments generated by this method satisfy all constraints.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for assigning tasks to groups of workers, comprising: retrieving from a database, by a data retrieval module, information relating to a set of activities to be executed, a set resources to be utilized during execution of the activities, a set of constraints to be satisfied, and a set of objectives to be accomplished; assigning, by a weight assigning module, a weight value according to the set of constraints for each activity of the set of activities; sorting, by a task sorting module, the activities of the set of activities according to each of their respective weight values; assigning, by the task sorting module, at least one resource to each activities of the set of activities; generating, by a match matrix generator, a matrix including a list of the set of activities and the at least one resource assigned to each of the activities of the set of activities; and applying, by a genetic algorithm module, a genetic algorithm process on the generated matrix to produce an optimum solution for assignment of the at least one resource to each activity of the set of activities. 2. The method according to claim 1, wherein applying the genetic algorithm process with the genetic algorithm module includes generating a plurality rows of initial population, wherein each row of the initial population corresponds to one chromosome in a genetic algorithm, and each activity in the row of the initial population corresponds to a gene in the genetic algorithm. 3. The method according to claim 1, comprising sorting, by the task sorting module, the activities of the set of activities according to each of their respective weight value in a descending order. 4. The method according to claim 9, wherein the chromosome crossover process comprises: defining a crossover rate; randomly assigning a chromosome value for the chromosome crossover process; defining a crossover point; executing a recombination process of genes based on defined parameters; and updating a recombined chromosome to the initial population. 5. The method according to claim 9, wherein the mutation process comprises the steps of: defining a mutation rate; randomly generating a value for the gene; defining a mutation point; executing a replacement of the generated value according to a defined parameter; and updating a mutated chromosome to the initial population. 6. The method according to claim 10, wherein the output process includes: generating a task schedule chart which illustrates timeline, resources, and priority for each activity of the set of activities; and computing a distance score, a priority serving score, a balance score, and a total fitness value. 7. An automated system for assigning tasks to groups of workers, comprising: a service server connected to a communication network, and coupled with at least one database; and a data mining engine connected to the service server via the communication network, and configured to: retrieve data from the at least one database, assign a weight value to the retrieved data, sort the retrieved data according to each of their respective weight values, process the sorted data via a genetic algorithm to produce an optimum solution for task assignment, and transmit the optimum solution to at least one electronic device via the communication network. 8. The system according to claim 7, wherein the data mining engine includes a data retrieval module configured to retrieve the data from the at least one database of the service server. 9. The method according to claim 2, wherein applying the genetic algorithm process with the genetic algorithm module includes: forming a fitness function based on the initial population to acquire fitness values of the chromosomes; selecting an optimum fitness value by using roulette wheel selection; and performing a chromosome crossover process and a mutation process to generate new and mutated sets of chromosomes. 10. The method according to claim 9, wherein applying the genetic algorithm process with the genetic algorithm module includes: executing an iteration process on the chromosome crossover process and the mutation process until a predetermined maximum repetition value is reached so as to obtain an optimum chromosome; and outputting an outcomes of the genetic algorithm process to a display unit. 11. The system according to claim 7, wherein the data mining engine includes a weight assigning module configured to assign the weight value to the retrieved data. 12. The system according to claim 7, wherein the data mining engine includes a task sorting module configured to sort the retrieved data according to each of assigned weight values. 13. The system according to claim 7, wherein the data mining engine includes a match matrix generator configured to generate a match matrix according to the sorted data. 14. The system according to claim 13, wherein the data mining engine includes a genetic algorithm module configured to apply a genetic algorithm process on the generated match matrix to produce the optimum solution for the task assignment. 15. A non-transitory computer-readable medium storing instructions for assigning tasks to groups of workers that are executable by a processor, wherein execution of the instructions by the processor causes the processor to: retrieve from a database, by a data retrieval module, information relating to a set of activities to be executed, a set resources to be utilized during execution of the activities, a set of constraints to be satisfied, and a set of objectives to be accomplished; assign, by a weight assigning module, a weight value according to the set of constraints for each activity of the set of activities; sort, by a task sorting module, the activities of the set of activities according to each of their respective weight values; assign, by the task sorting module, at least one resource to each activities of the set of activities; generate, by a match matrix generator, a matrix including a list of the set of activities and the at least one resource assigned to each of the activities of the set of activities; and apply, by a genetic algorithm module, a genetic algorithm process on the generated matrix to produce an optimum solution for assignment of the at least one resource to each activity of the set of activities.