Considering the characteristics of dyeing vat scheduling problem, such as complex constraints, large task scales, high efficiency request, an incremental dyeing vat scheduling model was established and the Slide Time Window Scheduling heuristic (STWS) algorithm was proposed to improve the applicability of the problem model and the algorithm in real scenario. In order to meet the optimization target of minimizing delay cost, washing cost and the switching cost of dyeing vat, the heuristic scheduling rules were applied to schedule the products according to the priority order. For each product scheduling, the dynamic combination batch algorithm and the batch split algorithm were used to divide batches, and then the batch optimal sorting algorithm was used to schedule the batches. The simulated scheduling results on actual production data provided by a dyeing enterprise show that the algorithm can complete the scheduling for monthly plan within 10 s. Compared with the manual scheduling, the proposed algorithm improves the scheduling efficiency and significantly optimizes three objectives. Additionally, experiments on incremental scheduling show obvious optimization of the algorithm on reducing the washing cost and the switching cost of dyeing vat. All the results indicate that the proposed algorithm has excellent scheduling ability.

Variable neighborhood search algorithm is effective for the nurse rostering, and the perturbation method used in it has significant effect on its performance. In order to improve the satisfaction of nurses in the nurse rostering problem, an Improved Variable Neighborhood Search (IVNS) algorithm was proposed. The algorithm used three neighborhood structures, when using any neighborhood could not improve the current solution further, a method for perturbing the current optimal solution was designed: firstly, two days in the rostering period were randomly selected, then a group of nurses were selected and their shifts in these two days were exchanged under the restriction of hard constraints. Comparison experiments with a Hybrid Variable Neighborhood Search (HVNS) algorithm were carried out on the benchmarks provided by the first international nurse rostering competition in 2010, and the results in the Sprint-early, Medium-early and Long-early instance groups show that, the optimal value of the IVNS algorithm is not inferior to HVNS at least, and its average value is superior to HVNS; the maximum variance of IVNS algorithm is 0.72, which means the fluctuation range is small, and the solution performance is stable. The IVNS disturbance program makes small disturbance to the existing project, and the current local optimal value can effectively jump out, enhancing the optimization ability of variable neighborhood search algorithm. Compared with HVNS algorithm, its performance is better.