The Research And Application Based On Optimization Ant Colony Algorithms For Production Scheduling Of SUNTEN Electrical Factory

Bionics optimization algorithm is an important embranchment of artificial intelligence (AI). Ant colony algorithms (ACA) is a novel bio-inspired optimization algorithm, which simulates the foraging behavior of ants for solving various complex combinatorial optimization problems. ACA has become the research focus because of its great ability of finding new solutions, robustness and essential parallelism.As the most pivotal part of ERP (Enterprise Resource Planning), the production planning and scheduling system directly affect the output efficiency and product cost of enterprise. Effective planning and scheduling algorithms can benefit enterprise to the maximal extent. But scheduling problem is combinatorial optimization problem, which belongs to NP problems and is difficult to solve in the regular way. In recent years, some intelligent algorithms have been used for this point such as GA (generic algorithm) and AS (Ant System).etc.In this thesis, AS is applied to solve the complex production scheduling problem. The author has made some research in the following aspects:Introduce the development and theory of ACA. The Job shop scheduling model is described. The thesis constructs a graph model and designs an ant colony algorithm to solve the Job shop scheduling problem in the aspect of engineering.We realize both based ACA and new ACA with C++ program. Then we validates the algorithm with Eil51 and Berlin52 problems. At last, after compiling the result, we test the performance of new ACA and get some important conclusions.