To attain the goal of cost minimization, a vehicle routing model for cargo transport O2O platforms were established in this study. In consideration of differences in vehicle origin in the traditional vehicle routing problem as well as the one-to-one corresponding relationship between cargo owners' pickup and delivery points, constraint conditions such as half-open, multiple depot, multiple vehicle type, origindestination pair, loading limit, and soft time window constraints were introduced into the proposed model. Given the model characteristics, an improved genetic algorithm, which is commonly used in vehicle routing problem, was used as the solving tool. The nearest matching method currently used by cargo transport O2O platforms was simulated using the simulation software AnyLogic. Moreover, vehicle-cargo orders on a platform within a certain time period were selected and allocated, and a matching scheme was obtained. Then, the optimized matching scheme for the same order was calculated using the improved genetic algorithm. Results show the comprehensive cost obtained by the improved genetic algorithm is 21.14 % lower than that of obtained by the nearest matching method.

This paper makes a detailed introduction of the software system which is used to detect the partial discharge in GIS online and describe its superiority with open and robust features in distributed network acquisition as well as its great real-time performance. The introduction can be divided into two parts: design and implementation. Based on multi-sensors adopted and the distributed framework, the signal collection and control, feature extraction, feature storage, analysis and evaluation of the diagnostic function of PD can be realized in the system. The stability and reliability of the system has been verified by the field operation experience, which can satisfy the need of GIS online monitoring.