Application of ant colony, genetic algorithm and data mining-based techniques for scheduling

In this paper, we have proposed a novel use of data mining algorithms for the extraction of knowledge from a large set of flow shop schedules. The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an ant colony algorithm performing a scheduling operation and to develop a rule set scheduler which approximates the ant colony algorithm's scheduler. Ant colony optimization (ACO) is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. The natural metaphor on which ant algorithms are based is that of ant colonies. Fascinated by the ability of the almost blind ants to establish the shortest route from their nests to the food source and back, researchers found out that these ants secrete a substance called ‘pheromone’ and use its trails as a medium for communicating information among each other. The ant algorithm is simple to implement and results of the case studies show its ability to provide speedy and accurate solutions. Further, we employed the genetic algorithm operators such as crossover and mutation to generate the new regions of solution. The data mining tool we have used is Decision Tree, which is produced by the See5 software after the instances are classified. The data mining is for mining the knowledge of job scheduling about the objective of minimization of makespan in a flow shop environment. Data mining systems typically uses conditional relationships represented by IF-THEN rules and allowing the production managers to easily take the decisions regarding the flow shop scheduling based on various objective functions and the constraints.