A deterministic algorithm for two-stage cutting layout with same-shape block was proposed to solve the large-scale two-dimensional cutting problems. The algorithm used dynamic programming to generate the optimal same-shape block, solved the knapsack problem to determine the optimal layout of the same-shape block in the same-shape section and the same-shape section in the same-shape segment, and optimally selected the two segments to compose the optimal same-shape block layout. The algorithm was tested through 43 benchmark problems, and compared with the classic two-stage and the three-block algorithms. The experimental results indicate that the algorithm is superior in both computation time and material usage rate, it not only meets the requirements of shearing and punching process, but also achieves good results within reasonable time.

Bayesian network is one of the most important theoretical models for the representation and reasoning of uncertainty. At present, its structure learning has become a focus of study. In this paper, a Bayesian network structure learning algorithm was developed, which was based on topological order and quantum genetic algorithm. With the richness of the quantum information and the parallelism of quantum computation, this paper designed generator strategy of topological order based on a quantum chromosome to improve not only the efficiency of search, but also the quality of Bayesian network structure. And then by using self-adaptive quantum mutation strategy with upper-lower limit, the diversity of the population was increased, so that the search performance of the new algorithm was improved. Compared to some existing algorithms, the experimental results show that the new algorithm not only searches higher quality Bayesian structure, but also has a quicker convergence rate.

This paper presented an algorithm for generating the homogenous strip T-shape layouts for large-scale rectangular piece packing. The algorithm not only achieved good results within reasonable time, but also met the shearing and punching process need. The algorithm generated optimal homogenous strips, solved knapsack problems to obtain the strip layouts on the sections and the section layouts on the segments, and optimally selected two segments to compose the layouts. The algorithm was tested on 63 benchmark problems, and compared with five famous algorithms (the classic two-stage, the general T shape, the two-stage homogenous block, the general cutting pattern and the heuristic algorithm TABU500). The computational results indicate that the algorithm is efficient in both the computation time and the material utilization rate.

For air-raid target the surface warship threat assessment is the basic foundation of firepower organization. To solve the shortcomings of traditional approximation method, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), for order preference by similarity to solution in calculating the weights, an improved TOPSIS based on entropy was applied to the multi-target threat problems with weights unknown. This algorithm used entropy theory to process air-raid target objective information, determined each attribute weights of the goal. Then, the weights were applied to the TOPSIS model, and the evaluating model and algorithm of multi-target threat which based on modified TOPSIS model were given. The method is proven to the reasonable and valid by the practical example.