An efficient priority service model with two-level-polling scheme

This paper considers an efficient priority service model with two-level-polling scheme which the message packets conform to the discrete-time Geom/G/1 queue with multiple vacations and bulk arrival. By the embedded Markov chain theory and the probability generating function method, we set up the mathematics functions and give closed form expressions for obtaining the mean cyclic period （MCP）, the mean queue length （MQL） and the mean waiting time （MWT） characteristics, the analytical results are also verified through extensive computer simulations. The performance analysis reveals that this priority polling scheme can gives better efficiency as well as impartiality in terms of system characteristics, and it can be used for differentiating priority service to guarantee better QoS and system stability in design and improvement of MAC protocol.

Improved ant colony optimization for multi-depot heterogeneous vehicle routing problem with soft time windows

Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.

Strong Approximations for a Kumar-Seidman Network under a Priority Service Discipline

In this paper,we derive the strong approximations for a four-class two station multi-class queuing network（Kumar-Seidman network） under a priority service discipline.Within a group,jobs are served in the order of arrival,that is,a first-in-first-out disciple,and among groups,jobs are served under a pre-emptiveresume priority disciple.We show that if the input data（i.e.,the arrival and service processe） satisfy an approximation（such as the functional law-of-iterated logarithm approximation or the strong approximation）,the output data（the departure processes） and the performance measures（such as the queue length,the work load and the sojourn time process） satisfy a similar approximation.