To solve the problems of secondary injury, muscle dysfunction, stiffness of the affected limb, damage to the blood supply, and poor dynamic performance of postoperative correction in traditional reduction methods (such as manual reduction, traction reduction, and surgical reduction) of fracture treatment, a Cyber-Physical and Human System (CPHS) was proposed to guide the reduction movement of the robot. First of all, the composition of the cyber-physical system of the parallel robot was illustrated from the aspects of CPHS system such as digital twin, information perception, system integration, surgical procedure, and simulated reduction. The high positioning accuracy and repeatability of the robot were combined with minimally invasive methods effectively to guide doctors to complete a series of operations such as simulation planning and intraoperative monitoring. Secondly, according to the clinical fracture reduction process, the reduction experiments were performed on simulated fracture cases of 5 groups of different fracture postures under robot operation. Finally, the remaining placements and angle errors in each experimental group were calculated after reduction operation and were compared with the corresponding data of the traditional reduction methods. Experimental results show that the CPHS fracture reduction robot has obvious advantages in fracture reduction and patient postoperative rehabilitation compared with the traditional reduction methods.

The Vehicle Routing Problem with Hard Time Window (VRPHTW) is a kind of Vehicle Routing Problem (VRP) which has a lot of applications. The existing heuristics of this problem hold shortcomings such as complex structure, lots of parameters and low efficiency. Therefore, Time Difference Insertion Heuristics (TDIH) for VRPHTW was proposed. The algorithm introduced the concept of Time Difference (TD), and took TD as a heuristic rule evaluation indicator. Compared to other classic heuristics, the algorithm was characterized with fewer parameters and simpler structure. The computational results on the benchmark problems show that the algorithm is better than the Solomons insertion heuristics and Potvins parallel insertion heuristics.

Considering the uncertainty of logistics demand network, the interval number was used to measure uncertain variables and parameters. The bilevel programming model of logistics network under interval demand mode was established and a hierarchical interval optimization genetic algorithm with interval variables and parameters was designed. The risk coefficient and the maximum decision-making deviation were defined to solve the problem, and the rules for logistics network structure were given to transform the model with certainty. The initial population was defined by interval slack variables and 0-1 decision variable, with two-stage genetic operation to solve interval optimal solution and node decision-making scheme of bilevel programming objects under different scenarios. The results of tested example show that the operability of the algorithm is much stronger and the solution result has superiority in interval optimal solution and scenario decision.