The platform scheduling problem in battlefield is one of the important problems in military operational research.It needs to minimize mission completing time and meanwhile maximize the mission completing accuracy with...The platform scheduling problem in battlefield is one of the important problems in military operational research.It needs to minimize mission completing time and meanwhile maximize the mission completing accuracy with a limited number of platforms.Though the traditional certain models obtain some good results,uncertain model is still needed to be introduced since the battlefield environment is complex and unstable.An uncertain model is prposed for the platform scheduling problem.Related parameters in this model are set to be fuzzy or stochastic.Due to the inherent disadvantage of the solving methods for traditional models,a new method is proposed to solve the uncertain model.Finally,the practicability and availability of the proposed method are demonstrated with a case of joint campaign.展开更多
The fact that outburst traffic in industrial Ethemet was focused on that would bring self-similar phenomenon leading to the delay increase of the cyclical data, and a hybrid priority queue schedule model was proposed ...The fact that outburst traffic in industrial Ethemet was focused on that would bring self-similar phenomenon leading to the delay increase of the cyclical data, and a hybrid priority queue schedule model was proposed in which the outburst data was given the highest priority. Some properties of the self-similar outburst data were proved by network calculus, and its service curve scheduled by the switch was gained. And then the performance of the scheduling algorithm was obtained. The simulation results are close to those calculated by using network calculus model. Some results are of actual significance to the construction of switched industrial Ethernet.展开更多
Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance...Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.展开更多
Reduction of conservatism is one of the key and difficult problems in missile robust gain scheduling autopilot design based on multipliers.This article presents a scheme of adopting linear parameter-varying（LPV） con...Reduction of conservatism is one of the key and difficult problems in missile robust gain scheduling autopilot design based on multipliers.This article presents a scheme of adopting linear parameter-varying（LPV） control approach with full block multipliers to design a missile robust gain scheduling autopilot in order to eliminate conservatism.A model matching design structure with a high demand on matching precision is constructed based on the missile linear fractional transformation（LFT） model.By applying full block S-procedure and elimination lemma,a convex feasibility problem with an infinite number of constraints is formulated to satisfy robust quadratic performance specifications.Then a grid method is adopted to transform the infinite-dimensional convex feasibility problem into a solvable finite-dimensional convex feasibility problem,based on which a gain scheduling controller with linear fractional dependence on the flight Mach number and altitude is derived.Static and dynamic simulation results show the effectiveness and feasibility of the proposed scheme.展开更多
Prior research on the resilience of critical infrastructure usually utilizes the network model to characterize the structure of the components so that a quantitative representation of resilience can be obtained. Parti...Prior research on the resilience of critical infrastructure usually utilizes the network model to characterize the structure of the components so that a quantitative representation of resilience can be obtained. Particularly, network component importance is addressed to express its significance in shaping the resilience performance of the whole system. Due to the intrinsic complexity of the problem, some idealized assumptions are exerted on the resilience-optimization problem to find partial solutions. This paper seeks to exploit the dynamic aspect of system resilience, i.e., the scheduling problem of link recovery in the post-disruption phase.The aim is to analyze the recovery strategy of the system with more practical assumptions, especially inhomogeneous time cost among links. In view of this, the presented work translates the resilience-maximization recovery plan into the dynamic decisionmaking of runtime recovery option. A heuristic scheme is devised to treat the core problem of link selection in an ongoing style.Through Monte Carlo simulation, the link recovery order rendered by the proposed scheme demonstrates excellent resilience performance as well as accommodation with uncertainty caused by epistemic knowledge.展开更多
To improve the productivity of cluster tools in semiconductor fabrications,on the basis of stating scheduling problems,a try and error-based scheduling algorithm was proposed with residency time constraints and an obj...To improve the productivity of cluster tools in semiconductor fabrications,on the basis of stating scheduling problems,a try and error-based scheduling algorithm was proposed with residency time constraints and an objective of minimizing Makespan for the wafer jobs in cluster tools.Firstly,mathematical formulations of scheduling problems were presented by using assumptions and definitions of a scheduling domain.Resource conflicts were analyzed in the built scheduling model,and policies to solve resource conflicts were built.A scheduling algorithm was developed.Finally,the performances of the proposed algorithm were evaluated and compared with those of other methods by simulations.Experiment results indicate that the proposed algorithm is effective and practical in solving the scheduling problem of the cluster tools.展开更多
A reservation-based feedback scheduling （FS-CBS） of a set of model predictive control （MPC） tasks is presented to optimize the global control performance subject to limited computational resource. Implemented as a...A reservation-based feedback scheduling （FS-CBS） of a set of model predictive control （MPC） tasks is presented to optimize the global control performance subject to limited computational resource. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance. Each MPC task is assigned with a constant bandwidth server （CBS）, whose reserved processor time is adjusted dynamically. The FS-CBS is shown robust against the varying of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.展开更多
Due to the effectiveness, simple deployment and low cost, radio frequency identification （RFID） systems are used in a variety of applications to uniquely identify physical objects. The operation of RFID systems ofte...Due to the effectiveness, simple deployment and low cost, radio frequency identification （RFID） systems are used in a variety of applications to uniquely identify physical objects. The operation of RFID systems often involves a situation in which multiple readers physically located near one another may interfere with one another＇s operation. Such reader collision must be minimized to avoid the faulty or miss reads. Specifically, scheduling the colliding RFID readers to reduce the total system transaction time or response time is the challenging problem for large-scale RFID network deployment. Therefore, the aim of this work is to use a successful multi-swarm cooperative optimizer called pseo to minimize both the reader-to-reader interference and total system transaction time in RFID reader networks. The main idea of pS20 is to extend the single population PSO to the interacting multi-swarm model by constructing hierarchical interaction topology and enhanced dynamical update equations. As the RFID network scheduling model formulated in this work is a discrete problem, a binary version of PS20 algorithm is proposed. With seven discrete benchmark functions, PS20 is proved to have significantly better performance than the original PSO and a binary genetic algorithm, pS20 is then used for solving the real-world RFID network scheduling problem. Numerical results for four test cases with different scales, ranging from 30 to 200 readers, demonstrate the performance of the proposed methodology.展开更多
基金supported by the National Natural Science Foundation of China(61573017)
文摘The platform scheduling problem in battlefield is one of the important problems in military operational research.It needs to minimize mission completing time and meanwhile maximize the mission completing accuracy with a limited number of platforms.Though the traditional certain models obtain some good results,uncertain model is still needed to be introduced since the battlefield environment is complex and unstable.An uncertain model is prposed for the platform scheduling problem.Related parameters in this model are set to be fuzzy or stochastic.Due to the inherent disadvantage of the solving methods for traditional models,a new method is proposed to solve the uncertain model.Finally,the practicability and availability of the proposed method are demonstrated with a case of joint campaign.
基金Project( 60425310) supported by the National Science Fund for Distinguished Young Scholars of ChinaProject(05JJ40118) supported by the Natural Science Foundation of Hunan Province, China
文摘The fact that outburst traffic in industrial Ethemet was focused on that would bring self-similar phenomenon leading to the delay increase of the cyclical data, and a hybrid priority queue schedule model was proposed in which the outburst data was given the highest priority. Some properties of the self-similar outburst data were proved by network calculus, and its service curve scheduled by the switch was gained. And then the performance of the scheduling algorithm was obtained. The simulation results are close to those calculated by using network calculus model. Some results are of actual significance to the construction of switched industrial Ethernet.
基金supported by the National Natural Science Foundation of China (61104180)the National Basic Research Program of China(973 Program) (97361361)
文摘Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.
文摘Reduction of conservatism is one of the key and difficult problems in missile robust gain scheduling autopilot design based on multipliers.This article presents a scheme of adopting linear parameter-varying（LPV） control approach with full block multipliers to design a missile robust gain scheduling autopilot in order to eliminate conservatism.A model matching design structure with a high demand on matching precision is constructed based on the missile linear fractional transformation（LFT） model.By applying full block S-procedure and elimination lemma,a convex feasibility problem with an infinite number of constraints is formulated to satisfy robust quadratic performance specifications.Then a grid method is adopted to transform the infinite-dimensional convex feasibility problem into a solvable finite-dimensional convex feasibility problem,based on which a gain scheduling controller with linear fractional dependence on the flight Mach number and altitude is derived.Static and dynamic simulation results show the effectiveness and feasibility of the proposed scheme.
基金supported by the National Natural Science Foundation of China(51479158)the Fundamental Research Funds for the Central Universities(WUT:2018III061GX)
文摘Prior research on the resilience of critical infrastructure usually utilizes the network model to characterize the structure of the components so that a quantitative representation of resilience can be obtained. Particularly, network component importance is addressed to express its significance in shaping the resilience performance of the whole system. Due to the intrinsic complexity of the problem, some idealized assumptions are exerted on the resilience-optimization problem to find partial solutions. This paper seeks to exploit the dynamic aspect of system resilience, i.e., the scheduling problem of link recovery in the post-disruption phase.The aim is to analyze the recovery strategy of the system with more practical assumptions, especially inhomogeneous time cost among links. In view of this, the presented work translates the resilience-maximization recovery plan into the dynamic decisionmaking of runtime recovery option. A heuristic scheme is devised to treat the core problem of link selection in an ongoing style.Through Monte Carlo simulation, the link recovery order rendered by the proposed scheme demonstrates excellent resilience performance as well as accommodation with uncertainty caused by epistemic knowledge.
基金Projects(71071115,60574054) supported by the National Natural Science Foundation of China
文摘To improve the productivity of cluster tools in semiconductor fabrications,on the basis of stating scheduling problems,a try and error-based scheduling algorithm was proposed with residency time constraints and an objective of minimizing Makespan for the wafer jobs in cluster tools.Firstly,mathematical formulations of scheduling problems were presented by using assumptions and definitions of a scheduling domain.Resource conflicts were analyzed in the built scheduling model,and policies to solve resource conflicts were built.A scheduling algorithm was developed.Finally,the performances of the proposed algorithm were evaluated and compared with those of other methods by simulations.Experiment results indicate that the proposed algorithm is effective and practical in solving the scheduling problem of the cluster tools.
文摘A reservation-based feedback scheduling （FS-CBS） of a set of model predictive control （MPC） tasks is presented to optimize the global control performance subject to limited computational resource. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance. Each MPC task is assigned with a constant bandwidth server （CBS）, whose reserved processor time is adjusted dynamically. The FS-CBS is shown robust against the varying of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.
基金Projects(61105067,61174164)supported by the National Natural Science Foundation of ChinaProjects(012BAF10B11,2012BAF10B06)supported by the National Key Technologies R&D Program of China+1 种基金Project(F11-264-1-08)supported by the Shenyang Science and Technology Project,ChinaProject(2011BY100383)supported by the Cooperation Project of Foshan and Chinese Academy of Sciences
文摘Due to the effectiveness, simple deployment and low cost, radio frequency identification （RFID） systems are used in a variety of applications to uniquely identify physical objects. The operation of RFID systems often involves a situation in which multiple readers physically located near one another may interfere with one another＇s operation. Such reader collision must be minimized to avoid the faulty or miss reads. Specifically, scheduling the colliding RFID readers to reduce the total system transaction time or response time is the challenging problem for large-scale RFID network deployment. Therefore, the aim of this work is to use a successful multi-swarm cooperative optimizer called pseo to minimize both the reader-to-reader interference and total system transaction time in RFID reader networks. The main idea of pS20 is to extend the single population PSO to the interacting multi-swarm model by constructing hierarchical interaction topology and enhanced dynamical update equations. As the RFID network scheduling model formulated in this work is a discrete problem, a binary version of PS20 algorithm is proposed. With seven discrete benchmark functions, PS20 is proved to have significantly better performance than the original PSO and a binary genetic algorithm, pS20 is then used for solving the real-world RFID network scheduling problem. Numerical results for four test cases with different scales, ranging from 30 to 200 readers, demonstrate the performance of the proposed methodology.
