AGVs dispatching, one of the hot problems in FMS, has attracted widespread interest in recent years. It is hard to dynamically schedule AGVs with pre designed rule because of the uncertainty and dynamic nature of AGVs...AGVs dispatching, one of the hot problems in FMS, has attracted widespread interest in recent years. It is hard to dynamically schedule AGVs with pre designed rule because of the uncertainty and dynamic nature of AGVs dispatching progress, so the AGVs system in this paper is treated as a cooperative learning multiagent system, in which each agent adopts multilevel decision method, which includes two level decisions: the option level and the action level. On the option level, an agent learns a policy to execute a subtask with the best response to the other AGVs’ current options. On the action level, an agent learns an optimal policy of actions for achieving his planned option. The method is applied to a AGVs’ dispatching simulation, and the performance of the AGVs system based on this method is verified.展开更多
A new mobile multicast scheme called mobility prediction based mobile multicast(MPBMM) was proposed. In MPBMM, when a mobile node (MN) roams among subnets during a multicast session, MN predicts the next subnet, to wh...A new mobile multicast scheme called mobility prediction based mobile multicast(MPBMM) was proposed. In MPBMM, when a mobile node (MN) roams among subnets during a multicast session, MN predicts the next subnet, to which MN will attach, by the information of its position and mobility speed, consequently speeds up the handoff procedure. Simulation results show that the proposed scheme can minimize the loss of multicast packets, reduce the delay of subnet handoff, decrease the frequency of multicast tree reconfiguration, and optimize the delivery path of multicast packets. When MN moves among subnets at different speeds (from 5 to 25 ms), the maximum loss ratio of multicast packets is less than0.2%, the maximum inter-arrival time of multicast packets is 117 ms, so the proposed scheme can meet the QoS requirements of real-time services. In addition, MPBMM can support the mobility of multicast source.展开更多
文摘AGVs dispatching, one of the hot problems in FMS, has attracted widespread interest in recent years. It is hard to dynamically schedule AGVs with pre designed rule because of the uncertainty and dynamic nature of AGVs dispatching progress, so the AGVs system in this paper is treated as a cooperative learning multiagent system, in which each agent adopts multilevel decision method, which includes two level decisions: the option level and the action level. On the option level, an agent learns a policy to execute a subtask with the best response to the other AGVs’ current options. On the action level, an agent learns an optimal policy of actions for achieving his planned option. The method is applied to a AGVs’ dispatching simulation, and the performance of the AGVs system based on this method is verified.
基金Project (60573127) supported by the National Natural Science Foundation of ChinaProject (20040533036) supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China+1 种基金Project (05JJ40131) supported by the Natural Science Foundation of Hunan Province, ChinaProject(03C326) supported by the Natural Science Foundation of Education Department of Hunan Province, China
文摘A new mobile multicast scheme called mobility prediction based mobile multicast(MPBMM) was proposed. In MPBMM, when a mobile node (MN) roams among subnets during a multicast session, MN predicts the next subnet, to which MN will attach, by the information of its position and mobility speed, consequently speeds up the handoff procedure. Simulation results show that the proposed scheme can minimize the loss of multicast packets, reduce the delay of subnet handoff, decrease the frequency of multicast tree reconfiguration, and optimize the delivery path of multicast packets. When MN moves among subnets at different speeds (from 5 to 25 ms), the maximum loss ratio of multicast packets is less than0.2%, the maximum inter-arrival time of multicast packets is 117 ms, so the proposed scheme can meet the QoS requirements of real-time services. In addition, MPBMM can support the mobility of multicast source.
