Collaborative multicenter logistics delivery network optimization with resource sharing.

Collaboration among logistics facilities in a multicenter logistics delivery network can significantly improve the utilization of logistics resources through resource sharing including logistics facilities, vehicles, and customer services. This study proposes and tests different resource sharing schemes to solve the optimization problem of a collaborative multicenter logistics delivery network based on resource sharing (CMCLDN-RS). The CMCLDN-RS problem aims to establish a collaborative mechanism of allocating logistics resources in a manner that improves the operational efficiency of a logistics network. A bi-objective optimization model is proposed with consideration of various resource sharing schemes in multiple service periods to minimize the total cost and number of vehicles. An adaptive grid particle swarm optimization (AGPSO) algorithm based on customer clustering is devised to solve the CMCLDN-RS problem and find Pareto optimal solutions. An effective elite iteration and selective endowment mechanism is designed for the algorithm to combine global and local search to improve search capabilities. The solution of CMCLDN-RS guarantees that cost savings are fairly allocated to the collaborative participants through a suitable profit allocation model. Compared with the computation performance of the existing nondominated sorting genetic algorithm-II and multi-objective evolutionary algorithm, AGPSO is more computationally efficient. An empirical case study in Chengdu, China suggests that the proposed collaborative mechanism with resource sharing can effectively reduce total operational costs and number of vehicles, thereby enhancing the operational efficiency of the logistics network.

Adaptive vehicle extraction in real-time traffic video monitoring based on the fusion of multi-objective particle swarm optimization algorithm.

In view of the problems in the real-time traffic video monitoring that the adaptive vehicle extraction is greatly affected by the environmental factors such as the illumination, noise, and so on; the missed detection and error detection rate is high; and it is difficult to meet the robustness and the real-time performance at the same time, a kind of method for the adaptive vehicle extraction in real-time traffic video monitoring based on the fusion of multi-objective particle swarm optimization algorithm is put forward. In this method, based on the multi-objective particle swarm optimization algorithm, adaptive binarization processing is carried out on the image first, and the interference points are removed by filtration through the erosion and expansion method. Simple and effective method is used to carry out the merger of the shadow line and the extraction of the real-time traffic video. In the algorithm, the information entropy in the target area and the symmetry characteristics of the vehicle tail are used to screen and identify the region of interest, which has reduced the missed detection and error detection rate of the algorithm. The multi-objective particle swarm optimization algorithm is used to extract the vehicle boundaries and has achieved relatively good effect. The results show that the detection accuracy is 89% and the average operating speed is 17.6 frames/s during the processing of the real-time traffic video with the resolution of 640 × 480.