Research On Shop Scheduling Problem Based On Improved Hybrid Immune Algorithm

With the intensification of global economic competition and complication of enterprise production mode, the workshop production scheduling plays an important role in the manufacturing industry. Hybrid flow shop is generalized from the basic flow shop which increases the number of devices in parallel at each stage of scheduling to achieve parallel production. It generally exists in the petrochemical, pharmaceutical, steel and other manufacturing processes. There is a variety of the uncertain factors in the flexible job shop, developing more flexible scheduling scheme for the complex combinatorial optimization problem. Solving the production workshop scheduling problem is that satisfy the premise of constraint conditions to achieve the maximization of corporate interests and according to the requirements of the work piece processing at the same time. Thus, studying the workshop scheduling problem of flow shop and job shop has important representative and applied significance which satisfies the demand of production developments.Shop scheduling problem is a kind of combinatorial optimization problem, but the application of the traditional algorithm for large-scale complex problems often exist various restrictions although many algorithms for this. Intelligent optimization algorithm presents the incomparable superiority in solving complex NP-hard problem. Immune algorithm is a new kind of intelligent optimization algorithm which inspired by biological immune system and based on the immunology theory. But the research of immune algorithm is still in its infancy and there are some shortcomings in solving the specific optimization problem. After a set number of iterations, the algorithm is easy to fall into local optimum and be a search degradation phenomenon. Simulated annealing algorithm with a complete theoretical foundation which showing a strong competitive advantage when it conducting global optimization problems. It has been clear about the advantages of the algorithm combining through the analysis on the performance of these two algorithms. Designing an improved hybrid immune algorithm for hybrid flow shop scheduling problem and flexible job shop scheduling problem that to solve the shop scheduling problem.This paper combines the above two algorithms and designed the overall structure of improved algorithm. In the study of the immune operator and the diversity evaluation of antibodies, formulating a reasonable encoding rules, researching the adaptive annealing operation. Selecting the flow shop and job shop problem of two types of instance to test the effectiveness of the improved algorithm and evaluating the performance of the improved algorithm. Finally, the algorithm is applied to the workshop scheduling problem of simulation system. The results show that the improved algorithms can very good convergence to a feasible solution and the improved algorithm can very good convergence to a feasible solution.