The one-ladle technology requires an efficient ironmaking and steelmaking interface. The scheduling of the hot metal ladle in the steel plant determines the overall operational efficiency of the interface. Considering...The one-ladle technology requires an efficient ironmaking and steelmaking interface. The scheduling of the hot metal ladle in the steel plant determines the overall operational efficiency of the interface. Considering the strong uncertainties of real-world production environments, this work studies the dynamic scheduling problem of hot metal ladles and develops a data-driven three-layer approach to solve this problem. A dynamic scheduling optimization model of the hot metal ladle operation with a minimum average turnover time as the optimization objective is also constructed. Furthermore, the intelligent perception of industrial scenes and autonomous identification of disturbances, adaptive configuration of dynamic scheduling strategies, and real-time adjustment of schedules can be realized. The upper layer generates a demand-oriented prescheduling scheme for hot metal ladles. The middle layer adaptively adjusts this scheme to obtain an executable schedule according to the actual supply–demand relationship. In the lower layer, three types of dynamic scheduling strategies are designed according to the characteristics of the dynamic disturbance in the model:real-time flexible fine-tuning, local machine adjustment, and global rescheduling. Case test using 24 h production data on a certain day during the system operation of a steel plant shows that the method and system can effectively reduce the fluctuation and operation time of the hot metal ladle and improve the stability of the ironmaking and steelmaking interface production rhythm. The data-driven dynamic scheduling strategy is feasible and effective, and the proposed method can improve the operation efficiency of hot metal ladles.展开更多
Cloud computing distributes task-parallel among the various resources.Applications with self-service supported and on-demand service have rapid growth.For these applications,cloud computing allocates the resources dyn...Cloud computing distributes task-parallel among the various resources.Applications with self-service supported and on-demand service have rapid growth.For these applications,cloud computing allocates the resources dynami-cally via the internet according to user requirements.Proper resource allocation is vital for fulfilling user requirements.In contrast,improper resource allocations result to load imbalance,which leads to severe service issues.The cloud resources implement internet-connected devices using the protocols for storing,communi-cating,and computations.The extensive needs and lack of optimal resource allo-cating scheme make cloud computing more complex.This paper proposes an NMDS(Network Manager based Dynamic Scheduling)for achieving a prominent resource allocation scheme for the users.The proposed system mainly focuses on dimensionality problems,where the conventional methods fail to address them.The proposed system introduced three–threshold mode of task based on its size STT,MTT,LTT(small,medium,large task thresholding).Along with it,task mer-ging enables minimum energy consumption and response time.The proposed NMDS is compared with the existing Energy-efficient Dynamic Scheduling scheme(EDS)and Decentralized Virtual Machine Migration(DVM).With a Network Manager-based Dynamic Scheduling,the proposed model achieves excellence in resource allocation compared to the other existing models.The obtained results shows the proposed system effectively allocate the resources and achieves about 94%of energy efficient than the other models.The evaluation metrics taken for comparison are energy consumption,mean response time,percentage of resource utilization,and migration.展开更多
The uninterrupted operation of the quay crane(QC)ensures that the large container ship can depart port within laytime,which effectively reduces the handling cost for the container terminal and ship owners.The QC waiti...The uninterrupted operation of the quay crane(QC)ensures that the large container ship can depart port within laytime,which effectively reduces the handling cost for the container terminal and ship owners.The QC waiting caused by automated guided vehicles(AGVs)delay in the uncertain environment can be alleviated by dynamic scheduling optimization.A dynamic scheduling process is introduced in this paper to solve the AGV scheduling and path planning problems,in which the scheduling scheme determines the starting and ending nodes of paths,and the choice of paths between nodes affects the scheduling of subsequent AGVs.This work proposes a two-stage mixed integer optimization model to minimize the transportation cost of AGVs under the constraint of laytime.A dynamic optimization algorithm,including the improved rule-based heuristic algorithm and the integration of the Dijkstra algorithm and the Q-Learning algorithm,is designed to solve the optimal AGV scheduling and path schemes.A new conflict avoidance strategy based on graph theory is also proposed to reduce the probability of path conflicts between AGVs.Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm over existing methods.展开更多
A dynamic database based dynamic scheduling system is proposed.As the schedule is being preformed, the scheduling task data in the dynamic database is updated timely.Genetic algorithm (GA) is employed for generating o...A dynamic database based dynamic scheduling system is proposed.As the schedule is being preformed, the scheduling task data in the dynamic database is updated timely.Genetic algorithm (GA) is employed for generating optimised production plan quickly and easily in response to changes on the shop floor. The current status of the shop is considered while rescheduling, and new plan is used in conjunction with the existing schedule to improve the effeciency of flexble manufacturing systems. Simulation results demonstrate the effectiveness of the proposed system.展开更多
Currently, the article analyzes the CAN bus's rule of priority's arbitration bit by bit without destroy. It elicits the conclusion that if static priority based on the affirmatory system model is used, the lower pri...Currently, the article analyzes the CAN bus's rule of priority's arbitration bit by bit without destroy. It elicits the conclusion that if static priority based on the affirmatory system model is used, the lower priority's messages will be delayed considerably more, even some data will be lost when the bus's bandwidth is widely used. The scheduling cannot be modified neither during the system when static priority is used. The dynamic priority promoting method and the math model of SQSA and SQMA are presented; it analyzes the model's rate of taking in and sending out in large quantities, the largest delay, the problems and solutions when using SQMA. In the end, it is confirmed that the method of improving dynamic priority has good performances on the network rate of taking in and sending out in large quantities, the average delay, and the rate of network usage by emulational experiments.展开更多
Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to...Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network system.Finally, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.展开更多
During the war,equipment is constantly being damaged with limited battlefield rush-repair time and power.Therefore,some military problems are presented in this paper.In order to get more fighting time for damaged equi...During the war,equipment is constantly being damaged with limited battlefield rush-repair time and power.Therefore,some military problems are presented in this paper.In order to get more fighting time for damaged equipment to participate in operation again as much as possible,three problems should be considered properly.The first problem is how to dynamically choose the most suitable damaged equipment for each repair group.The second one is how to divide tasks between different groups.The third one is how to determine execution sequence in the same group.A mathematical model is established to solve the dynamic battlefield rushrepair task scheduling problem(DBRTSP) in wartime.A variant genetic algorithm is designed to dynamically track the change of the optimal solution.A scheduling example is solved through Matlab.Results show that the proposed model is not only scientific and reasonable,but also convenient and efficient.展开更多
Based on the analysis of collective activities of ant colonies, the typicalexample of swarm intelligence, a new approach to construct swarm intelligence basedmulti-agent-system (SMAS) for dynamic real-time scheduling ...Based on the analysis of collective activities of ant colonies, the typicalexample of swarm intelligence, a new approach to construct swarm intelligence basedmulti-agent-system (SMAS) for dynamic real-time scheduling for semiconductor wafer fab is proposed.The relevant algorithm, pheromone-based dynamic real-time scheduling algorithm (PBDR), is given.MIMAC test bed data set mini-fab is used to compare PBDR with FIFO (first in first out),SRPT(shortest remaining processing time) and CR(critical ratio) under three different release rules,i.e. deterministic rule, Poisson rule and CONWIP (constant WIP). It is shown that PBDR is prior toFIFO, SRPT and CR with better performance of cycle time, throughput, and on-time delivery,especially for on-time delivery performance.展开更多
A dynamic advanced planning and scheduling (DAPS) problem is addressed where new orders arrive on a continuous basis. A periodic policy with frozen interval is adopted to increase stability on the shop floor. A gene...A dynamic advanced planning and scheduling (DAPS) problem is addressed where new orders arrive on a continuous basis. A periodic policy with frozen interval is adopted to increase stability on the shop floor. A genetic algorithm is developed to find a schedule at each rescheduling point for both original orders and new orders that both production idle time and penalties on tardiness and earliness of orders are minimized. The proposed methodology is tested on a small example to illustrate the effect of the frozen interval. The results indicate that the suggested approach can improve the schedule stability while retaining efficiency.展开更多
Nowadays,emergency accidents could happen at any time.The accidents occur unpredictably and the accidents requirements are diversely.The accidents happen in a dynamic environment and the resource should be cooperative...Nowadays,emergency accidents could happen at any time.The accidents occur unpredictably and the accidents requirements are diversely.The accidents happen in a dynamic environment and the resource should be cooperative to solve the accidents.Most methods are focusing on minimizing the casualties and property losses in a static environment.However,they are lack in considering the dynamic and unpredictable event handling.In this paper,we propose a representative environmental model in representation of emergency and dynamic resource allocation model,and an adaptive mathematical model based on Genetic Algorithm(GA)to generate an optimal set of solution domain.The experimental results show that the proposed algorithm can get a set of better candidate solutions.展开更多
Project scheduling is a key objective of many models and is the proposed method for project planning and management.Project scheduling problems depend on precedence relationships and resource constraints,in addition t...Project scheduling is a key objective of many models and is the proposed method for project planning and management.Project scheduling problems depend on precedence relationships and resource constraints,in addition to some other limitations for achieving a subset of goals.Project scheduling problems are dependent on many limitations,including limitations of precedence relationships,resource constraints,and some other limitations for achieving a subset of goals.Deterministic project scheduling models consider all information about the scheduling problem such as activity durations and precedence relationships information resources available and required,which are known and stable during the implementation process.The concept of deterministic project scheduling conflicts with real situations,in which in many cases,some data on the activity’s durations of the project and the degree of availability of resources change or may have different modes and strategies during the process of project implementation for dealing with multi-mode conditions surrounded by projects and their activity durations.Scheduling the multi-mode resource-constrained project problem is an optimization problem whose minimum project duration subject to the availability of resources is of particular interest to us.We use the multi-mode resource allocation and schedulingmodel that takes into account the dynamicity features of all parameters,that is,the scheduling process must be flexible to dynamic environment features.In this paper,we propose five priority heuristic rules for scheduling multi-mode resource-constrained projects under dynamicity features for more realistic situations,in which we apply the proposed heuristic rules(PHR)for scheduling multi-mode resource-constrained projects.Five projects are considered test problems for the PHR.The obtained results rendered by these priority rules for the test problems are compared by the results obtained from 10 well-known heuristics rules rendered for the same test problems.The results in many cases of the proposed priority rules are very promising,where they achieve better scheduling dates in many test case problems and the same results for the others.The proposed model is based on the dynamic features for project topography.展开更多
Based on the concept of operation flexibility, we study the relationship among multiple operation sequences and provide a flexibility measure for operation sequences. A criterion is proposed to prioritize each operati...Based on the concept of operation flexibility, we study the relationship among multiple operation sequences and provide a flexibility measure for operation sequences. A criterion is proposed to prioritize each operation (rather than sequence). Under the multi-agent architecture the criterion can be used to guide the decision-making procedure during production scheduling so that there is an adequate flexibility at each decision point. Experimental results demonstrate the efficiency of the criterion when it is used as a scheduling heuristic. It can increase flexibility of manufacturing systems, and consequently improve the performance of the systems.展开更多
In commercial unmanned aerial vehicle(UAV)applications,one of the main restrictions is UAVs’limited battery endurance when executing persistent tasks.With the mature of wireless power transfer(WPT)technologies,by lev...In commercial unmanned aerial vehicle(UAV)applications,one of the main restrictions is UAVs’limited battery endurance when executing persistent tasks.With the mature of wireless power transfer(WPT)technologies,by leveraging ground vehicles mounted with WPT facilities on their proofs,we propose a mobile and collaborative recharging scheme for UAVs in an on-demand manner.Specifically,we first present a novel air-ground cooperative UAV recharging framework,where ground vehicles cooperatively share their idle wireless chargers to UAVs and a swarm of UAVs in the task area compete to get recharging services.Considering the mobility dynamics and energy competitions,we formulate an energy scheduling problem for UAVs and vehicles under practical constraints.A fair online auction-based solution with low complexity is also devised to allocate and price idle wireless chargers on vehicular proofs in real time.We rigorously prove that the proposed scheme is strategy-proof,envy-free,and produces stable allocation outcomes.The first property enforces that truthful bidding is the dominant strategy for participants,the second ensures that no user is better off by exchanging his allocation with another user when the auction ends,while the third guarantees the matching stability between UAVs and UGVs.Extensive simulations validate that the proposed scheme outperforms benchmarks in terms of energy allocation efficiency and UAV’s utility.展开更多
This paper compares the quality and execution times of several algorithms for scheduling service based workflow applications with changeable service availability and parameters. A workflow is defined as an acyclic dir...This paper compares the quality and execution times of several algorithms for scheduling service based workflow applications with changeable service availability and parameters. A workflow is defined as an acyclic directed graph with nodes corresponding to tasks and edges to dependencies between tasks. For each task, one out of several available services needs to be chosen and scheduled to minimize the workflow execution time and keep the cost of service within the budget. During the execution of a workflow, some services may become unavailable, new ones may appear, and costs and execution times may change with a certain probability. Rescheduling is needed to obtain a better schedule. A solution is proposed on how integer linear programming can be used to solve this problem to obtain optimal solutions for smaller problems or suboptimal solutions for larger ones. It is compared side-by-side with GAIN, divide-and-conquer, and genetic algorithms for various probabilities of service unavailability or change in service parameters. The algorithms are implemented and subsequently tested in a real BeesyCluster environment.展开更多
Mitigating the adverse effects of uncertainty in appointment systems,arising from heterogeneous patient needs and preferences,is critical to the effective use of scarce medical resources and patient satisfaction.This ...Mitigating the adverse effects of uncertainty in appointment systems,arising from heterogeneous patient needs and preferences,is critical to the effective use of scarce medical resources and patient satisfaction.This study addresses an online scheduling problem with multiple servers and consideration of patient preference for physicians and their appointment times.The receptionist immediately determines whether a request should be accommodated and offers an appointment time slot for each accepted patient request.The patient may reject an undesirable appointment time slot with a certain probability,or may accept it,but the no-show probability will be higher.A stochastic overbooking model is formulated to maximize the expected profit,which is defined as the revenue generated from accepted requests minus the cost incurred by patients waiting and physicians’overtime.A myopic scheduling policy is developed based on certain structural properties of the objective function.This study advances the study of appointment systems by generating a non-unimodal profit evolution.Moreover,both the decision of accommodating more requests for certain slots and the scheduling of appointments depend on the patient choice rather than the patient type.Further,computational experiments and analysis offer valuable insights into performance improvement in outpatient clinics.展开更多
One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines as well as the integrated features of hosting physical machines into consider...One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines as well as the integrated features of hosting physical machines into consideration. We introduce a Dynamic and Integrated Resource Scheduling algorithm (DAIRS) for Cloud data centers. Unlike traditional load-balance scheduling algorithms which often consider only one factor such as the CPU load in physical servers, DAIRS treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop integrated measurement for the total imbalance level of a Cloud datacenter as well as the average imbalance level of each server. Simulation results show that DAIRS has good performance with regard to total imbalance level, average imbalance level of each server, as well as overall running time.展开更多
Two Relative-Residual-based Dynamic Schedules(RRDS) for Belief Propagation(BP) decoding of Low-Density Parity-Check(LDPC) codes are proposed,in which the Variable code-RRDS(VN-RRDS) is a greediness-reduced version of ...Two Relative-Residual-based Dynamic Schedules(RRDS) for Belief Propagation(BP) decoding of Low-Density Parity-Check(LDPC) codes are proposed,in which the Variable code-RRDS(VN-RRDS) is a greediness-reduced version of the Check code-RRDS(CN-RRDS).The RRDS only processes the variable(or check) node,which has the maximum relative residual among all the variable(or check) nodes in each decoding iteration,thus keeping less greediness and decreased complexity in comparison with the edge-based Variable-to-Check Residual Belief Propagation(VC-RBP) algorithm.Moreover,VN-RRDS propagates first the message which has the largest residual based on all check equations.For different types of LDPC codes,simulation results show that the convergence rate of RRDS is higher than that of VC-RBP while keeping very low computational complexity.Furthermore,VN-RRDS achieves faster convergence as well as better performance than CN-RRDS.展开更多
The architecture of project management of distributed concurrent product design in a virtual enterprise is put forward. T he process of project management and its functions are presented. Product design process coo...The architecture of project management of distributed concurrent product design in a virtual enterprise is put forward. T he process of project management and its functions are presented. Product design process coordination is also discussed. First, based on the analysis of traditi onal project management, project management and coordination of distributed coop erative product design in the virtual enterprise is put forward. Then, aiming at the characteristics of a distributed concurrent product design process, the inh erent rules and complex interrelations in product development are studied. Accor dingly, the architecture of project management of distributed cooperative produc t design in a virtual enterprise is presented to adapt to distributed concurrent development of complex products. The main advantages of the architecture are al so discussed. Finally, the emphasis is placed on the project management process. Its main functions are set forth, such as project definition, task decompositio n and distribution, resource constraints and dynamic resource scheduling, proces s fusion, task scheduling and monitoring, project plan, cost and quality evaluat ion, etc.展开更多
Knowledge plays an active role in job-shop scheduling, especially in dynamic environments. A novel case-based immune framework was developed for static and dynamic job-shop problems, using the associative memory and k...Knowledge plays an active role in job-shop scheduling, especially in dynamic environments. A novel case-based immune framework was developed for static and dynamic job-shop problems, using the associative memory and knowledge reuse from case-based reasoning (CBR) and immune response mechanisms. A 2-level similarity index which combines both job routing and problem solution characteristics based on DNA matching ideas was defined for both the CBR and immune algorithms. A CBR-embedded immune algorithms (CBR-IAs) framework was then developed focusing on case retrieval and adaptation methods. In static environments, the CBR-IAs have excellent population diversity and fast convergence which are necessary for dynamic problems with jobs arriving and leaving continually. The results with dy-namic scheduling problems further confirm the CBR-IAs effectiveness as a problem solving method with knowledge reuse.展开更多
An algorithm is proposed for scheduling dependent tasks in time-varying heterogeneous multiprocessor systems, in which computational power and links between processors are allowed to change over time. Link contention ...An algorithm is proposed for scheduling dependent tasks in time-varying heterogeneous multiprocessor systems, in which computational power and links between processors are allowed to change over time. Link contention is considered in the multiprocessor scheduling problem. A linear switching-state space-modeling paradigm is introduced to enable theoretical analysis from a system engineering perspective. Theoretical analysis of this model shows its robustness against changes in processing power and link failure. The proposed algorithm uses a fuzzy decision-making procedure to handle changes in the multiprocessor system. The efficiency of the proposed algorithm is illustrated by several random experiments and comparison against a recent benchmark approach. The results show up to 18% average improvement in makespan, especially for larger scale systems.展开更多
基金financially supported by the National Natural Science Foundation of China (No.51734004)the Key Program of the National Key R&D Program of China(No.2017YFB0304002)。
文摘The one-ladle technology requires an efficient ironmaking and steelmaking interface. The scheduling of the hot metal ladle in the steel plant determines the overall operational efficiency of the interface. Considering the strong uncertainties of real-world production environments, this work studies the dynamic scheduling problem of hot metal ladles and develops a data-driven three-layer approach to solve this problem. A dynamic scheduling optimization model of the hot metal ladle operation with a minimum average turnover time as the optimization objective is also constructed. Furthermore, the intelligent perception of industrial scenes and autonomous identification of disturbances, adaptive configuration of dynamic scheduling strategies, and real-time adjustment of schedules can be realized. The upper layer generates a demand-oriented prescheduling scheme for hot metal ladles. The middle layer adaptively adjusts this scheme to obtain an executable schedule according to the actual supply–demand relationship. In the lower layer, three types of dynamic scheduling strategies are designed according to the characteristics of the dynamic disturbance in the model:real-time flexible fine-tuning, local machine adjustment, and global rescheduling. Case test using 24 h production data on a certain day during the system operation of a steel plant shows that the method and system can effectively reduce the fluctuation and operation time of the hot metal ladle and improve the stability of the ironmaking and steelmaking interface production rhythm. The data-driven dynamic scheduling strategy is feasible and effective, and the proposed method can improve the operation efficiency of hot metal ladles.
文摘Cloud computing distributes task-parallel among the various resources.Applications with self-service supported and on-demand service have rapid growth.For these applications,cloud computing allocates the resources dynami-cally via the internet according to user requirements.Proper resource allocation is vital for fulfilling user requirements.In contrast,improper resource allocations result to load imbalance,which leads to severe service issues.The cloud resources implement internet-connected devices using the protocols for storing,communi-cating,and computations.The extensive needs and lack of optimal resource allo-cating scheme make cloud computing more complex.This paper proposes an NMDS(Network Manager based Dynamic Scheduling)for achieving a prominent resource allocation scheme for the users.The proposed system mainly focuses on dimensionality problems,where the conventional methods fail to address them.The proposed system introduced three–threshold mode of task based on its size STT,MTT,LTT(small,medium,large task thresholding).Along with it,task mer-ging enables minimum energy consumption and response time.The proposed NMDS is compared with the existing Energy-efficient Dynamic Scheduling scheme(EDS)and Decentralized Virtual Machine Migration(DVM).With a Network Manager-based Dynamic Scheduling,the proposed model achieves excellence in resource allocation compared to the other existing models.The obtained results shows the proposed system effectively allocate the resources and achieves about 94%of energy efficient than the other models.The evaluation metrics taken for comparison are energy consumption,mean response time,percentage of resource utilization,and migration.
基金supported in part by the National Natural Science Foundation of China(61473053)the Science and Technology Innovation Foundation of Dalian,China(2020JJ26GX033)。
文摘The uninterrupted operation of the quay crane(QC)ensures that the large container ship can depart port within laytime,which effectively reduces the handling cost for the container terminal and ship owners.The QC waiting caused by automated guided vehicles(AGVs)delay in the uncertain environment can be alleviated by dynamic scheduling optimization.A dynamic scheduling process is introduced in this paper to solve the AGV scheduling and path planning problems,in which the scheduling scheme determines the starting and ending nodes of paths,and the choice of paths between nodes affects the scheduling of subsequent AGVs.This work proposes a two-stage mixed integer optimization model to minimize the transportation cost of AGVs under the constraint of laytime.A dynamic optimization algorithm,including the improved rule-based heuristic algorithm and the integration of the Dijkstra algorithm and the Q-Learning algorithm,is designed to solve the optimal AGV scheduling and path schemes.A new conflict avoidance strategy based on graph theory is also proposed to reduce the probability of path conflicts between AGVs.Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm over existing methods.
基金This project is supported by National Natural ScienceFoundation of China (No.70071017,59889505)
文摘A dynamic database based dynamic scheduling system is proposed.As the schedule is being preformed, the scheduling task data in the dynamic database is updated timely.Genetic algorithm (GA) is employed for generating optimised production plan quickly and easily in response to changes on the shop floor. The current status of the shop is considered while rescheduling, and new plan is used in conjunction with the existing schedule to improve the effeciency of flexble manufacturing systems. Simulation results demonstrate the effectiveness of the proposed system.
基金supported by the National Natural Science Foundation of China (50421703)the National Key Laboratory of Electrical Engineering of Naval Engineering University
文摘Currently, the article analyzes the CAN bus's rule of priority's arbitration bit by bit without destroy. It elicits the conclusion that if static priority based on the affirmatory system model is used, the lower priority's messages will be delayed considerably more, even some data will be lost when the bus's bandwidth is widely used. The scheduling cannot be modified neither during the system when static priority is used. The dynamic priority promoting method and the math model of SQSA and SQMA are presented; it analyzes the model's rate of taking in and sending out in large quantities, the largest delay, the problems and solutions when using SQMA. In the end, it is confirmed that the method of improving dynamic priority has good performances on the network rate of taking in and sending out in large quantities, the average delay, and the rate of network usage by emulational experiments.
基金the State Grid Liaoning Electric Power Supply Co.,Ltd.(Research on Scheduling Decision Technology Based on Interactive Reinforcement Learning for Adapting High Proportion of New Energy,No.2023YF-49).
文摘Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network system.Finally, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.
文摘During the war,equipment is constantly being damaged with limited battlefield rush-repair time and power.Therefore,some military problems are presented in this paper.In order to get more fighting time for damaged equipment to participate in operation again as much as possible,three problems should be considered properly.The first problem is how to dynamically choose the most suitable damaged equipment for each repair group.The second one is how to divide tasks between different groups.The third one is how to determine execution sequence in the same group.A mathematical model is established to solve the dynamic battlefield rushrepair task scheduling problem(DBRTSP) in wartime.A variant genetic algorithm is designed to dynamically track the change of the optimal solution.A scheduling example is solved through Matlab.Results show that the proposed model is not only scientific and reasonable,but also convenient and efficient.
基金This project is supported by National 973 Project of China (No.2002-CB312202) National Natural Science Foundation of China (No.60374005, No.60104004) Chinese Postdoctoral Fellowship Foundation.
文摘Based on the analysis of collective activities of ant colonies, the typicalexample of swarm intelligence, a new approach to construct swarm intelligence basedmulti-agent-system (SMAS) for dynamic real-time scheduling for semiconductor wafer fab is proposed.The relevant algorithm, pheromone-based dynamic real-time scheduling algorithm (PBDR), is given.MIMAC test bed data set mini-fab is used to compare PBDR with FIFO (first in first out),SRPT(shortest remaining processing time) and CR(critical ratio) under three different release rules,i.e. deterministic rule, Poisson rule and CONWIP (constant WIP). It is shown that PBDR is prior toFIFO, SRPT and CR with better performance of cycle time, throughput, and on-time delivery,especially for on-time delivery performance.
基金This project is supported by the Hong Kong Polytechnic University,China(No,G-RGF9).
文摘A dynamic advanced planning and scheduling (DAPS) problem is addressed where new orders arrive on a continuous basis. A periodic policy with frozen interval is adopted to increase stability on the shop floor. A genetic algorithm is developed to find a schedule at each rescheduling point for both original orders and new orders that both production idle time and penalties on tardiness and earliness of orders are minimized. The proposed methodology is tested on a small example to illustrate the effect of the frozen interval. The results indicate that the suggested approach can improve the schedule stability while retaining efficiency.
基金This work is supported by the National Science Foundation of China under Grant No.F020803,and No.61602254the National Science Foundation of Jiangsu Province,China,under Grant No.BK20160968the Project through the Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions,the China-USA Computer Science Research Center.
文摘Nowadays,emergency accidents could happen at any time.The accidents occur unpredictably and the accidents requirements are diversely.The accidents happen in a dynamic environment and the resource should be cooperative to solve the accidents.Most methods are focusing on minimizing the casualties and property losses in a static environment.However,they are lack in considering the dynamic and unpredictable event handling.In this paper,we propose a representative environmental model in representation of emergency and dynamic resource allocation model,and an adaptive mathematical model based on Genetic Algorithm(GA)to generate an optimal set of solution domain.The experimental results show that the proposed algorithm can get a set of better candidate solutions.
文摘Project scheduling is a key objective of many models and is the proposed method for project planning and management.Project scheduling problems depend on precedence relationships and resource constraints,in addition to some other limitations for achieving a subset of goals.Project scheduling problems are dependent on many limitations,including limitations of precedence relationships,resource constraints,and some other limitations for achieving a subset of goals.Deterministic project scheduling models consider all information about the scheduling problem such as activity durations and precedence relationships information resources available and required,which are known and stable during the implementation process.The concept of deterministic project scheduling conflicts with real situations,in which in many cases,some data on the activity’s durations of the project and the degree of availability of resources change or may have different modes and strategies during the process of project implementation for dealing with multi-mode conditions surrounded by projects and their activity durations.Scheduling the multi-mode resource-constrained project problem is an optimization problem whose minimum project duration subject to the availability of resources is of particular interest to us.We use the multi-mode resource allocation and schedulingmodel that takes into account the dynamicity features of all parameters,that is,the scheduling process must be flexible to dynamic environment features.In this paper,we propose five priority heuristic rules for scheduling multi-mode resource-constrained projects under dynamicity features for more realistic situations,in which we apply the proposed heuristic rules(PHR)for scheduling multi-mode resource-constrained projects.Five projects are considered test problems for the PHR.The obtained results rendered by these priority rules for the test problems are compared by the results obtained from 10 well-known heuristics rules rendered for the same test problems.The results in many cases of the proposed priority rules are very promising,where they achieve better scheduling dates in many test case problems and the same results for the others.The proposed model is based on the dynamic features for project topography.
基金This work was supported by the National Natural Science Foundation of China(Grant No.59990470) the Outstanding Youth Foundation of China(Grant No.59725514)
文摘Based on the concept of operation flexibility, we study the relationship among multiple operation sequences and provide a flexibility measure for operation sequences. A criterion is proposed to prioritize each operation (rather than sequence). Under the multi-agent architecture the criterion can be used to guide the decision-making procedure during production scheduling so that there is an adequate flexibility at each decision point. Experimental results demonstrate the efficiency of the criterion when it is used as a scheduling heuristic. It can increase flexibility of manufacturing systems, and consequently improve the performance of the systems.
基金supported in part by National Key R&D Program of China(no.2022YFB3104500)NSFC(nos.U22A2029,U20A20175)the Fundamental Research Funds for the Central Universities.
文摘In commercial unmanned aerial vehicle(UAV)applications,one of the main restrictions is UAVs’limited battery endurance when executing persistent tasks.With the mature of wireless power transfer(WPT)technologies,by leveraging ground vehicles mounted with WPT facilities on their proofs,we propose a mobile and collaborative recharging scheme for UAVs in an on-demand manner.Specifically,we first present a novel air-ground cooperative UAV recharging framework,where ground vehicles cooperatively share their idle wireless chargers to UAVs and a swarm of UAVs in the task area compete to get recharging services.Considering the mobility dynamics and energy competitions,we formulate an energy scheduling problem for UAVs and vehicles under practical constraints.A fair online auction-based solution with low complexity is also devised to allocate and price idle wireless chargers on vehicular proofs in real time.We rigorously prove that the proposed scheme is strategy-proof,envy-free,and produces stable allocation outcomes.The first property enforces that truthful bidding is the dominant strategy for participants,the second ensures that no user is better off by exchanging his allocation with another user when the auction ends,while the third guarantees the matching stability between UAVs and UGVs.Extensive simulations validate that the proposed scheme outperforms benchmarks in terms of energy allocation efficiency and UAV’s utility.
基金Project partially supported by the Polish National Science Center(No.DEC-2012/07/B/ST6/01516)
文摘This paper compares the quality and execution times of several algorithms for scheduling service based workflow applications with changeable service availability and parameters. A workflow is defined as an acyclic directed graph with nodes corresponding to tasks and edges to dependencies between tasks. For each task, one out of several available services needs to be chosen and scheduled to minimize the workflow execution time and keep the cost of service within the budget. During the execution of a workflow, some services may become unavailable, new ones may appear, and costs and execution times may change with a certain probability. Rescheduling is needed to obtain a better schedule. A solution is proposed on how integer linear programming can be used to solve this problem to obtain optimal solutions for smaller problems or suboptimal solutions for larger ones. It is compared side-by-side with GAIN, divide-and-conquer, and genetic algorithms for various probabilities of service unavailability or change in service parameters. The algorithms are implemented and subsequently tested in a real BeesyCluster environment.
基金This work was supported by the National Natural Science Foundation of China[Grant number 71501027]China Postdoctoral Science Foundation[Grant number 2015M581342].
文摘Mitigating the adverse effects of uncertainty in appointment systems,arising from heterogeneous patient needs and preferences,is critical to the effective use of scarce medical resources and patient satisfaction.This study addresses an online scheduling problem with multiple servers and consideration of patient preference for physicians and their appointment times.The receptionist immediately determines whether a request should be accommodated and offers an appointment time slot for each accepted patient request.The patient may reject an undesirable appointment time slot with a certain probability,or may accept it,but the no-show probability will be higher.A stochastic overbooking model is formulated to maximize the expected profit,which is defined as the revenue generated from accepted requests minus the cost incurred by patients waiting and physicians’overtime.A myopic scheduling policy is developed based on certain structural properties of the objective function.This study advances the study of appointment systems by generating a non-unimodal profit evolution.Moreover,both the decision of accommodating more requests for certain slots and the scheduling of appointments depend on the patient choice rather than the patient type.Further,computational experiments and analysis offer valuable insights into performance improvement in outpatient clinics.
基金supported by Scientific Research Foundation for the Returned Overseas Chinese ScholarsState Education Ministry under Grant No.2010-2011 and Chinese Post-doctoral Research Foundation
文摘One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines as well as the integrated features of hosting physical machines into consideration. We introduce a Dynamic and Integrated Resource Scheduling algorithm (DAIRS) for Cloud data centers. Unlike traditional load-balance scheduling algorithms which often consider only one factor such as the CPU load in physical servers, DAIRS treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop integrated measurement for the total imbalance level of a Cloud datacenter as well as the average imbalance level of each server. Simulation results show that DAIRS has good performance with regard to total imbalance level, average imbalance level of each server, as well as overall running time.
基金supported by the Fundamental Research Funds for the Central Universities
文摘Two Relative-Residual-based Dynamic Schedules(RRDS) for Belief Propagation(BP) decoding of Low-Density Parity-Check(LDPC) codes are proposed,in which the Variable code-RRDS(VN-RRDS) is a greediness-reduced version of the Check code-RRDS(CN-RRDS).The RRDS only processes the variable(or check) node,which has the maximum relative residual among all the variable(or check) nodes in each decoding iteration,thus keeping less greediness and decreased complexity in comparison with the edge-based Variable-to-Check Residual Belief Propagation(VC-RBP) algorithm.Moreover,VN-RRDS propagates first the message which has the largest residual based on all check equations.For different types of LDPC codes,simulation results show that the convergence rate of RRDS is higher than that of VC-RBP while keeping very low computational complexity.Furthermore,VN-RRDS achieves faster convergence as well as better performance than CN-RRDS.
文摘The architecture of project management of distributed concurrent product design in a virtual enterprise is put forward. T he process of project management and its functions are presented. Product design process coordination is also discussed. First, based on the analysis of traditi onal project management, project management and coordination of distributed coop erative product design in the virtual enterprise is put forward. Then, aiming at the characteristics of a distributed concurrent product design process, the inh erent rules and complex interrelations in product development are studied. Accor dingly, the architecture of project management of distributed cooperative produc t design in a virtual enterprise is presented to adapt to distributed concurrent development of complex products. The main advantages of the architecture are al so discussed. Finally, the emphasis is placed on the project management process. Its main functions are set forth, such as project definition, task decompositio n and distribution, resource constraints and dynamic resource scheduling, proces s fusion, task scheduling and monitoring, project plan, cost and quality evaluat ion, etc.
基金the National Natural Science Foundation of China (No. 60004010) and the National High-Tech Research and Development (863) Program of China (No. 2001AA411020)
文摘Knowledge plays an active role in job-shop scheduling, especially in dynamic environments. A novel case-based immune framework was developed for static and dynamic job-shop problems, using the associative memory and knowledge reuse from case-based reasoning (CBR) and immune response mechanisms. A 2-level similarity index which combines both job routing and problem solution characteristics based on DNA matching ideas was defined for both the CBR and immune algorithms. A CBR-embedded immune algorithms (CBR-IAs) framework was then developed focusing on case retrieval and adaptation methods. In static environments, the CBR-IAs have excellent population diversity and fast convergence which are necessary for dynamic problems with jobs arriving and leaving continually. The results with dy-namic scheduling problems further confirm the CBR-IAs effectiveness as a problem solving method with knowledge reuse.
文摘An algorithm is proposed for scheduling dependent tasks in time-varying heterogeneous multiprocessor systems, in which computational power and links between processors are allowed to change over time. Link contention is considered in the multiprocessor scheduling problem. A linear switching-state space-modeling paradigm is introduced to enable theoretical analysis from a system engineering perspective. Theoretical analysis of this model shows its robustness against changes in processing power and link failure. The proposed algorithm uses a fuzzy decision-making procedure to handle changes in the multiprocessor system. The efficiency of the proposed algorithm is illustrated by several random experiments and comparison against a recent benchmark approach. The results show up to 18% average improvement in makespan, especially for larger scale systems.