This paper introduces a multi-agent system which i nt egrates process planning and production scheduling, in order to increase the fle xibility of manufacturing systems in coping with rapid changes in dynamic market a...This paper introduces a multi-agent system which i nt egrates process planning and production scheduling, in order to increase the fle xibility of manufacturing systems in coping with rapid changes in dynamic market and dealing with internal uncertainties such as machine breakdown or resources shortage. This system consists of various autonomous agents, each of which has t he capability of communicating with one another and making decisions based on it s knowledge and if necessary on information provided by other agents. Machine ag ents which represent the machines play an important role in the system in that t hey negotiate with each other to bid for jobs. An iterative bidding mechanism is proposed to facilitate the process of job assignment to machines and handle the negotiation between agents. This mechanism enables near optimal process plans a nd production schedules to be produced concurrently, so that dynamic changes in the market can be coped with at a minimum cost, and the utilisation of manufactu ring resources can be optimised. In addition, a currency scheme with currency-l ike metrics is proposed to encourage or prohibit machine agents to put forward t heir bids for the jobs announced. The values of the metrics are adjusted iterati vely so as to obtain an integrated plan and schedule which result in the minimum total production cost while satisfying products due dates. To deal with the optimisation problem, i.e. to what degree and how the currencies should be adj usted in each iteration, a genetic algorithm (GA) is developed. Comparisons are made between GA approach and simulated annealing (SA) optimisation technique.展开更多
In order to guarantee the overall production performance of the multiple departments in an air-condition production industry, multidisciplinary design optimization model for production system is established based on t...In order to guarantee the overall production performance of the multiple departments in an air-condition production industry, multidisciplinary design optimization model for production system is established based on the multi-agent technology. Local operation models for departments of plan, marketing, sales, purchasing, as well as production and warehouse are formulated into individual agents, and their respective local objectives are collectively formulated into a multi-objective optimization problem. Considering the coupling effects among the correlated agents, the optimization process is carried out based on self-adaptive chaos immune optimization algorithm with mutative scale. The numerical results indicate that the proposed multi-agent optimization model truly reflects the actual situations of the air-condition production system. The proposed multi-agent based multidisciplinary design optimization method can help companies enhance their income ratio and profit by about 33% and 36%, respectively, and reduce the total cost by about 1.8%.展开更多
A distributed coordinated consensus problem for multiple networked Euler-Lagrange systems is studied. The communication between agents is subject to time delays, unknown parameters and nonlinear inputs, but only with ...A distributed coordinated consensus problem for multiple networked Euler-Lagrange systems is studied. The communication between agents is subject to time delays, unknown parameters and nonlinear inputs, but only with their states available for measurement. When the communication topology of the system is connected, an adaptive control algorithm with selfdelays and uncertainties is suggested to guarantee global full-state synchro-nization that the difference between the agent＇s positions and ve-locities asymptotically converges to zero. Moreover, the distributed sliding-mode law is given for chaotic systems with nonlinear inputs to compensate for the effects of nonlinearity. Finally, simulation results show the effectiveness of the proposed control algorithm.展开更多
Evolutionary algorithms(EAs)were shown to be effective for complex constrained optimization problems.However,inflexible exploration in general EAs would lead to losing the global optimum nearby the ill-convergence reg...Evolutionary algorithms(EAs)were shown to be effective for complex constrained optimization problems.However,inflexible exploration in general EAs would lead to losing the global optimum nearby the ill-convergence regions.In this paper,we propose an iterative dynamic diversity evolutionary algorithm(IDDEA)with contractive subregions guiding exploitation through local extrema to the global optimum in suitable steps.In IDDEA,a novel optimum estimation strategy with multi-agents evolving diversely is suggested to e?ciently compute dominance trend and establish a subregion.In addition,a subregion converging iteration is designed to redistrict a smaller subregion in current subregion for next iteration,which is based on a special dominance estimation scheme.Meanwhile,an infimum penalty function is embedded into IDDEA to judge agents and penalize adaptively the unfeasible agents with the lowest fitness of feasible agents.Furthermore,several engineering design optimization problems taken from the specialized literature are successfully solved by the present algorithm with high reliable solutions.展开更多
针对点融合系统中航班调度问题,构建航班进场排序的二维元胞自动机模型,模拟先来先服务(first come first service, FCFS)和滑动窗口(sliding window, SDW)策略下的航班排序过程,对比不同策略下的终端区运行效率。实验结果表明:若都采用...针对点融合系统中航班调度问题,构建航班进场排序的二维元胞自动机模型,模拟先来先服务(first come first service, FCFS)和滑动窗口(sliding window, SDW)策略下的航班排序过程,对比不同策略下的终端区运行效率。实验结果表明:若都采用FCFS,使用中国民航航空器尾流重新分类标准(RECAT-CN)代替现行尾流间隔,进场航班流在点融合系统中的总运行时间减少了98 s,运行效率提升了4.3%;若都使用RECAT-CN间隔标准,采用SDW优化后的航班进场序列,较FCFS的总运行时间减少了193 s,运行效率提升了8.5%;点融合技术和RECAT-CN间隔标准可以实现终端区运行安全和效率的同步提升。展开更多
Collaborative coverage path planning(CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle(UAV) collaborative CCPP algorithm ...Collaborative coverage path planning(CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle(UAV) collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment.Due to flexible control of small UAVs, it can be considered that all UAVs fly at the same altitude, that is, they perform search tasks on a two-dimensional plane. Based on the agents’ motion characteristics and environmental information, a mathematical model of CCPP problem is established. The minimum time for UAVs to complete the CCPP is the objective function, and complete coverage constraint, no-fly constraint, collision avoidance constraint, and communication constraint are considered. Four motion strategies and two communication strategies are designed. Then a distributed CCPP algorithm is designed based on hybrid strategies. Simulation results compared with patternbased genetic algorithm(PBGA) and random search method show that the proposed method has stronger real-time performance and better scalability and can complete the complete CCPP task more efficiently and stably.展开更多
蜂窝网络下的同时同频全双工(CCFD)设备到设备(D2D)组网可以进一步提升网络频谱效率,然而由此引入的残余自干扰(RSI)及蜂窝用户(CU)与D2D用户(DU)之间共享频谱的干扰会严重影响到蜂窝用户的体验。因此,该文为蜂窝网络下同时同频全双工...蜂窝网络下的同时同频全双工(CCFD)设备到设备(D2D)组网可以进一步提升网络频谱效率,然而由此引入的残余自干扰(RSI)及蜂窝用户(CU)与D2D用户(DU)之间共享频谱的干扰会严重影响到蜂窝用户的体验。因此,该文为蜂窝网络下同时同频全双工组网设计了两种干扰协调算法,即CU和速率最大化算法(MaxSumCU)与CU最小速率最大化算法(MaxMinCU),在小区频谱效率得到提升的同时尽可能地保证CU的体验。对于MaxSumCU算法,该文以CU和速率为优化目标建立混合整数非线性规划问题(MINLP),其在数学上为非确定性多项式(NP-hard)问题。算法将其分解为功率控制与频谱资源分配两个子问题,并用图形规划找到最优功率解后,使用二向图最大权值匹配算法决定频谱共享的CU与DU。为了保证每一个蜂窝用户体验的公平性,该文设计了Max Min CU算法用以最大化所有CU速率中的最小值,该算法基于二分查找与二向图最小权值匹配算法来完成用户的资源分配。数值结果表明,与小区和速率最大化(MaxSumCell)设计相比,该文所提的两种算法在提升小区和速率的同时均有效地提升了蜂窝用户的体验。展开更多
文摘This paper introduces a multi-agent system which i nt egrates process planning and production scheduling, in order to increase the fle xibility of manufacturing systems in coping with rapid changes in dynamic market and dealing with internal uncertainties such as machine breakdown or resources shortage. This system consists of various autonomous agents, each of which has t he capability of communicating with one another and making decisions based on it s knowledge and if necessary on information provided by other agents. Machine ag ents which represent the machines play an important role in the system in that t hey negotiate with each other to bid for jobs. An iterative bidding mechanism is proposed to facilitate the process of job assignment to machines and handle the negotiation between agents. This mechanism enables near optimal process plans a nd production schedules to be produced concurrently, so that dynamic changes in the market can be coped with at a minimum cost, and the utilisation of manufactu ring resources can be optimised. In addition, a currency scheme with currency-l ike metrics is proposed to encourage or prohibit machine agents to put forward t heir bids for the jobs announced. The values of the metrics are adjusted iterati vely so as to obtain an integrated plan and schedule which result in the minimum total production cost while satisfying products due dates. To deal with the optimisation problem, i.e. to what degree and how the currencies should be adj usted in each iteration, a genetic algorithm (GA) is developed. Comparisons are made between GA approach and simulated annealing (SA) optimisation technique.
基金Project(60973132)supported by the National Natural Science Foundation of ChinaProject(2010B050400005)supported by the Science and Research Program of Guangdong Province,China
文摘In order to guarantee the overall production performance of the multiple departments in an air-condition production industry, multidisciplinary design optimization model for production system is established based on the multi-agent technology. Local operation models for departments of plan, marketing, sales, purchasing, as well as production and warehouse are formulated into individual agents, and their respective local objectives are collectively formulated into a multi-objective optimization problem. Considering the coupling effects among the correlated agents, the optimization process is carried out based on self-adaptive chaos immune optimization algorithm with mutative scale. The numerical results indicate that the proposed multi-agent optimization model truly reflects the actual situations of the air-condition production system. The proposed multi-agent based multidisciplinary design optimization method can help companies enhance their income ratio and profit by about 33% and 36%, respectively, and reduce the total cost by about 1.8%.
基金supported by the National Natural Sciences Foundation of China (60974146)
文摘A distributed coordinated consensus problem for multiple networked Euler-Lagrange systems is studied. The communication between agents is subject to time delays, unknown parameters and nonlinear inputs, but only with their states available for measurement. When the communication topology of the system is connected, an adaptive control algorithm with selfdelays and uncertainties is suggested to guarantee global full-state synchro-nization that the difference between the agent＇s positions and ve-locities asymptotically converges to zero. Moreover, the distributed sliding-mode law is given for chaotic systems with nonlinear inputs to compensate for the effects of nonlinearity. Finally, simulation results show the effectiveness of the proposed control algorithm.
基金Supported by National Natural Science Foundation of China(61074020)
文摘Evolutionary algorithms(EAs)were shown to be effective for complex constrained optimization problems.However,inflexible exploration in general EAs would lead to losing the global optimum nearby the ill-convergence regions.In this paper,we propose an iterative dynamic diversity evolutionary algorithm(IDDEA)with contractive subregions guiding exploitation through local extrema to the global optimum in suitable steps.In IDDEA,a novel optimum estimation strategy with multi-agents evolving diversely is suggested to e?ciently compute dominance trend and establish a subregion.In addition,a subregion converging iteration is designed to redistrict a smaller subregion in current subregion for next iteration,which is based on a special dominance estimation scheme.Meanwhile,an infimum penalty function is embedded into IDDEA to judge agents and penalize adaptively the unfeasible agents with the lowest fitness of feasible agents.Furthermore,several engineering design optimization problems taken from the specialized literature are successfully solved by the present algorithm with high reliable solutions.
文摘针对点融合系统中航班调度问题,构建航班进场排序的二维元胞自动机模型,模拟先来先服务(first come first service, FCFS)和滑动窗口(sliding window, SDW)策略下的航班排序过程,对比不同策略下的终端区运行效率。实验结果表明:若都采用FCFS,使用中国民航航空器尾流重新分类标准(RECAT-CN)代替现行尾流间隔,进场航班流在点融合系统中的总运行时间减少了98 s,运行效率提升了4.3%;若都使用RECAT-CN间隔标准,采用SDW优化后的航班进场序列,较FCFS的总运行时间减少了193 s,运行效率提升了8.5%;点融合技术和RECAT-CN间隔标准可以实现终端区运行安全和效率的同步提升。
基金supported by the National Natural Science Foundation of China (61903036, 61822304)Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100)。
文摘Collaborative coverage path planning(CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle(UAV) collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment.Due to flexible control of small UAVs, it can be considered that all UAVs fly at the same altitude, that is, they perform search tasks on a two-dimensional plane. Based on the agents’ motion characteristics and environmental information, a mathematical model of CCPP problem is established. The minimum time for UAVs to complete the CCPP is the objective function, and complete coverage constraint, no-fly constraint, collision avoidance constraint, and communication constraint are considered. Four motion strategies and two communication strategies are designed. Then a distributed CCPP algorithm is designed based on hybrid strategies. Simulation results compared with patternbased genetic algorithm(PBGA) and random search method show that the proposed method has stronger real-time performance and better scalability and can complete the complete CCPP task more efficiently and stably.
文摘蜂窝网络下的同时同频全双工(CCFD)设备到设备(D2D)组网可以进一步提升网络频谱效率,然而由此引入的残余自干扰(RSI)及蜂窝用户(CU)与D2D用户(DU)之间共享频谱的干扰会严重影响到蜂窝用户的体验。因此,该文为蜂窝网络下同时同频全双工组网设计了两种干扰协调算法,即CU和速率最大化算法(MaxSumCU)与CU最小速率最大化算法(MaxMinCU),在小区频谱效率得到提升的同时尽可能地保证CU的体验。对于MaxSumCU算法,该文以CU和速率为优化目标建立混合整数非线性规划问题(MINLP),其在数学上为非确定性多项式(NP-hard)问题。算法将其分解为功率控制与频谱资源分配两个子问题,并用图形规划找到最优功率解后,使用二向图最大权值匹配算法决定频谱共享的CU与DU。为了保证每一个蜂窝用户体验的公平性,该文设计了Max Min CU算法用以最大化所有CU速率中的最小值,该算法基于二分查找与二向图最小权值匹配算法来完成用户的资源分配。数值结果表明,与小区和速率最大化(MaxSumCell)设计相比,该文所提的两种算法在提升小区和速率的同时均有效地提升了蜂窝用户的体验。
