Dual-resource-oriented Production Scheduling Problem Of Improving The Adaptive Immune Genetic Algorithm

As the market's rapid economic development, manufacturing scale of production is increasing. Actual production, the various process constraints, resource constraints, capacity constraints and other parallel existence, which requires a reasonable production operations in the scheduling. And a good scheduling arrangements need to be a good optimal scheduling algorithm. In addition, the vast majority of existing research work or studies or research assembly scheduling process scheduling, results of their research in a mixed production patterns, types of manufacturing industries in the application of the situation is not optimistic about the reason is that the actual production situation of the enterprise complex only the processed form is rarely or only assembled form, also need to consider the real-time inventory information, in-progress information, plant capacity information, and other information, dynamically generated scheduling plan, its complexity far exceeds the current use of the Institute scheduling problem of international standards and some of the existing production patterns in a single scheduling system.In this paper, dual-resource constraints, scheduling difficulties in the process of modeling, computational complexity, there are dynamic, random, multi-target binding, as well as such difficult to conduct in-depth theoretical research. And adaptive immune algorithm combining the advantages of genetic algorithm presents a new adaptive immune genetic algorithm. The new algorithm based on the search for historical information, adaptive adjustment of the genetic process of genetic algorithm parameters in order to enhance the stability and efficiency of local search algorithm to improve the ability of reconciliation quality; dynamic extraction and vaccination methods, improve the immune memory , so that the vaccine can be stored location information, thereby injecting more reasonable position to improve the algorithm convergence speed.By the algorithm applied to solve the dual problem of resources, the shorter the shortest production time, increase machine utilization and worker efficiency, reducing the number of iterations the optimal solution. The results show that the new algorithm has better optimization results, is an improved optimization strategy.Experimental Study: In this paper, Job-Shop scheduling problem was designed and implemented a multi-agent shop floor scheduling platform system, and application of the improved algorithm to solve these problems, the results obtained shows improved algorithm is feasible and effective.