The Method To Resolve Job-shop Scheduling Problem Based On Improved Ant Colony Algorithm

As the most pivotal part of ERP (Enterprise Resource Planning), Effective scheduling algorithms can benefit enterprise to the maximal extent. But scheduling problem is combinatorial optimization problem, which belongs to NP hard problems. In recent years, some intelligent algorithms have been used for this point. The ant system, a new intelligent algorithm has become the research focus because of its great ability of finding new solutions, robustness and essential parallelism. In this thesis, AS is applied to solve the complex production scheduling problem. The author has made some research in the following aspects:Aim at basic ant colony algorithm has deficiencies such as easy to enter part convergence, this paper provide a improved ant colony algorithm, this algorithm can judge whether search get into part convergence, once algorithm get into part convergence then algorithm will adjust pheromone updating strategy, dynamic adjust pheromone volatility and pheromone intensity according to the degree of part convergence.In reality the Job Shop scheduling is dynamic and flexible, this paper advanced a method to resolve dynamic and flexible Job Shop scheduling problem. Use event-driven rescheduling strategies based on last scheduling result.Aim at the fuzzy scheduling problem, this paper provide a model of Job Shop fuzzy scheduling problem, use triangle fuzzy date express fuzzy processing time, use trapezia fuzzy date express fuzzy due-date, average due-date satisfaction as scheduling goal. A new state transfer rule advanced based on adaptive ant colony algorithm.Integrate actual manufacture, use adaptive ant colony algorithm to solve actual schedule problem, developed production scheduling system, combined theory and fact.