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The optimization of logistics distribution can be defined as the multiple traveling salesman problem (MTSP). The purpose of existing heuristic algorithms, such as Genetic Algorithm (GA), Ant Colony Algorithm (ACO), etc., is to find the optimal path in a short time. However, two important factors of logistics distribution optimization, including work time window and the carrying capacity of the vehicle in distribution system, have been ignored. In this paper, we consider the influences of time limitation of modern commercial logistics and carrying capacity of the vehicle on the logistics optimization, and then propose a MTSP with constraints of time window and capacity of each salesman. We design a novel hybrid algorithm by combining the minimum spanning 1-tree with ACO to find the optimal solution. In addition, we improve the pheromone update rules to increase the search efficiency of ACO algorithm. The experiments show that the novel hybrid algorithm achieves a shorter path than the other algorithms.

Energy consumption has overtaken equipment costs given the growth of computing power, thereby becoming the dominant cost to data centers. Constrained by ensuring that peak resource requirements of VMs are met, the effective use of resource fragments is an effective means of energy conservation. We apply resource reservation to the resource-utilization-aware energy efficient server consolidation algorithm (RUAEE), and propose a server consolidation energy-saving algorithm (SCES). We adjust the allocations of VMs by computing the resource reservation and resource allocation ratio dynamically. This technique maintains the host resource utilization to within a reasonable range, reduces resource fragmentation and reserves resources to satisfy the peak resource requests simultaneously. Compared with the RUAEE algorithm, our algorithm can reduce the risk of system overload and improve the stability of the system. The experimental results based on the actual workload of the Google cluster show that the algorithm is effective at reducing resource waste, improving system stability, and achieving energy savings.