Optimal Scheduling For A Bell-type Batch Annealing Based On Differential Evolution Algorithm

The production scheduling problem in complicated process industry and its related high effective algorithm are always the hot topics not only in academic research but also in application field at present. Differential Evolution algorithm is a kind of search algorithm based on colony intelligence, which has great global search ability and the characteristics of easily implementation. In this paper, considering the merit of the colony intelligent algorithm, a modified Differential Evolution algorithm is proposed to solve the production scheduling problem in industry, in which the adaptive crossover probability is presented. Other optimization algorithms were compared with modified Differential Evolution algorithm on the classic TSP question; such approach can improve the global search ability in initial phase and the local search ability in the later phase of this algorithm.In this thesis, an existing modeling method is used for the bell-type annealing process. The modified Differential Evolution algorithm is applied to the simulation model in order to optimize the scheduling model. Several other algorithms are employed to compare for the analysis and research. Finally, such algorithm can be applied to the specific production scheduling problem, and solve complex combinatorial optimization problems better.Finally, the object-oriented method is used to design and develop an application system. A simulation model is build for actual on-site production using the encapsulation and inheritance characteristics. The .NET platform is adopted in the software developing and the B/S (Browser/Server) structure is implemented as well. The SQL server2000 database is employed to be treated as the background database instance, and the ADO.NET technology is adopted in this system.