Research On Computer Aided Workshop Production Planning

As the most important part of ERP(Enterprise Resource Planning ),the Workshop Production Planning System directly affects efficiency ,operation and management in production .Effective optimized algorithms can improve benefit and reduce cost , thereby enhance the competitive advantage of the enterprise .The research on the workshop Production Planning is of great value both in theory and in practice . Job Shop scheduling Problem (JSP) commonly exists in manufacturing factory , it is a NP-hard combinational optimization problem .Some intelligent algorithms have been used for it ,such as genetic algorithm (GA) and simulate annealing (SA) algorithm etc. The genetic algorithm is a random neighbor search algorithm based on the mechanics of natural selection and natural genetics .It is widely used in many kinds of fields because of its less-dependency global optimization,robustness and implicit parallelism . While applied to Job Shop scheduling Problems,it has some limitations to be solved. The Paper is dedicated to the application of improved GA to JSP. Some research has been made in the following aspects: 1. JSP mathematical model, optimized aim and its heuristic algorithm are described .The paper summary the genetic algorithm comparative advantages in contrast to the traditional algorithm after expounding its general idea , basic components and its theoretical base . 2. A new simple genetic algorithm (SGA) is proposed to solve JSP in the paper. On the base of the analysis of the causes and characters of the infeasible plan in JSP, a new encoding method of chromosome and the corresponding operators are constructed. The encoding method have the virtues of Job-Based representation and Operation-Based representation . The new SGA avoids infeasible plan completely, and it combines operation matrix, machine matrix and time matrix together, greatly simplifies the algorithm procedure. 3. A improved hybrid genetic algorithm (HGA) is proposed to overcome premature convergence and increase the optimize pace of SGA . In the HGA , heuristic algorithm is used to produced initial population , self-adaptive parameter are used in copy operator, crossover operator and mutation operator .Simulated annealing algorithm is also cited ,and only some excellent chromosomes are operated by SA . In this way, HGA could improve its search ability at cost of a little more run time. 4. Visual procedures of the heuristic algorithm, SGA and HGA are programmed . The benchmarks of different scale are carried to test three kind algorithms. Test result indicates SGA could find out much better solution than heuristic algorithm. Compared with SGA , HGA's search ability has being enhanced , and its optimize speed is faster .