The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because o...The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because of its straightforward,single-solution evolution framework.However,a potential draw-back of IGA is the lack of utilization of historical information,which could lead to an imbalance between exploration and exploitation,especially in large-scale DPFSPs.As a consequence,this paper develops an IGA with memory and learning mechanisms(MLIGA)to efficiently solve the DPFSP targeted at the mini-malmakespan.InMLIGA,we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search,by extending,reconstructing,and reinforcing the information from previous solutions.In addition,we design a twolayer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory mechanism.Meanwhile,to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA,a probability curve-based acceptance criterion is proposed by combining a cube root function with custom rules.At last,a discrete adaptive learning rate is employed to enhance the stability of the memory and learningmechanisms.Complete ablation experiments are utilized to verify the effectiveness of the memory mechanism,and the results show that this mechanism is capable of improving the performance of IGA to a large extent.Furthermore,through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks,we have discovered that MLI-GA demonstrates significant potential for solving large-scale DPFSPs.This indicates that MLIGA is well-suited for real-world distributed flow shop scheduling.展开更多
This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved pr...This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved proximal policy optimization(IPPO)method to make real-time decisions for the DHHBFSP.A multi-objective Markov decision process is modeled for the DHHBFSP,where the reward function is represented by a vector with dynamic weights instead of the common objectiverelated scalar value.A factory agent(FA)is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision quality.Multiple FAs work asynchronously to allocate jobs that arrive randomly at the shop.A two-stage training strategy is introduced in the IPPO,which learns from both single-and dual-policy data for better data utilization.The proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization(PPO),dispatch rules,multi-objective metaheuristics,and multi-agent reinforcement learning methods.Extensive experimental results suggest that the proposed strategies offer significant improvements to the basic PPO,and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.展开更多
The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worke...The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.展开更多
The flow shop scheduling problem is important for the manufacturing industry.Effective flow shop scheduling can bring great benefits to the industry.However,there are few types of research on Distributed Hybrid Flow S...The flow shop scheduling problem is important for the manufacturing industry.Effective flow shop scheduling can bring great benefits to the industry.However,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted meta-heuristics.This work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned DHFSP.Second,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are proposed.According to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local space.Instead of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during iterations.Finally,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed algorithms.The experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random strategy.To verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving DHFSP.The Friedman test is executed on the results by five algorithms.It is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness.展开更多
In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-in...In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.展开更多
Dear Editor,The distributed constraint optimization problems(DCOPs) [1]-[3]provide an efficient model for solving the cooperative problems of multi-agent systems, which has been successfully applied to model the real-...Dear Editor,The distributed constraint optimization problems(DCOPs) [1]-[3]provide an efficient model for solving the cooperative problems of multi-agent systems, which has been successfully applied to model the real-world problems like the distributed scheduling [4], sensor network management [5], [6], multi-robot coordination [7], and smart grid [8]. However, DCOPs were not well suited to solve the problems with continuous variables and constraint cost in functional form, such as the target tracking sensor orientation [9], the air and ground cooperative surveillance [10], and the sensor network coverage [11].展开更多
The energy Internet operation platform provides market entities such as energy users,energy enterprises,suppliers,and governments with the ability to interact,transact,and manage various operations.Owing to the large ...The energy Internet operation platform provides market entities such as energy users,energy enterprises,suppliers,and governments with the ability to interact,transact,and manage various operations.Owing to the large number of platform users,complex businesses,and large amounts of data-mining tasks,it is necessary to solve the problems afflicting platform task scheduling and the provision of simultaneous access to a large number of users.This study examines the two core technologies of platform task scheduling and multiuser concurrent processing,proposing a distributed task-scheduling method and a technical implementation scheme based on the particle swarm optimization algorithm,and presents a systematic solution in concurrent processing for massive user numbers.Based on the results of this study,the energy internet operation platform can effectively deal with the concurrent access of tens of millions of users and complex task-scheduling problems.展开更多
Furrow irrigation is a traditional widely-used irrigation method in the world. Understanding the dynamics of soil water distribution is essential to developing effective furrow irrigation strategies, especially in wat...Furrow irrigation is a traditional widely-used irrigation method in the world. Understanding the dynamics of soil water distribution is essential to developing effective furrow irrigation strategies, especially in water-limited regions. The objectives of this study are to analyze root length density distribution and to explore soil water dynamics by simulating soil water content using a HYDRUS-2D model with consideration of root water uptake for furrow irrigated tomato plants in a solar greenhouse in Northwest China. Soil water contents were also in-situ observed by the ECH_2O sensors from 4 June to 19 June and from 21 June to 4 July, 2012. Results showed that the root length density of tomato plants was concentrated in the 0–50 cm soil layers, and radiated 0–18 cm toward the furrow and 0–30 cm along the bed axis. Soil water content values simulated by the HYDRUS-2D model agreed well with those observed by the ECH_2O sensors, with regression coefficient of 0.988, coefficient of determination of 0.89, and index of agreement of 0.97. The HYDRUS-2D model with the calibrated parameters was then applied to explore the optimal irrigation scheduling. Infrequent irrigation with a large amount of water for each irrigation event could result in 10%–18% of the irrigation water losses. Thus we recommend high irrigation frequency with a low amount of water for each irrigation event in greenhouses for arid region. The maximum high irrigation amount and the suitable irrigation interval required to avoid plant water stress and drainage water were 34 mm and 6 days, respectively, for given daily average transpiration rate of 4.0 mm/d. To sum up, the HYDRUS-2D model with consideration of root water uptake can be used to improve irrigation scheduling for furrow irrigated tomato plants in greenhouses in arid regions.展开更多
Current MSM switching fabric has poor performance under unbalanced traffic. This paper presents an alternative, novel Central-stage Buffered Three-stage Clos switching (CB-3Clos) fabric and proves that this fabric can...Current MSM switching fabric has poor performance under unbalanced traffic. This paper presents an alternative, novel Central-stage Buffered Three-stage Clos switching (CB-3Clos) fabric and proves that this fabric can emulate output queuing switch without any speedup. By analyzing the condition to satisfy the central-stage load-balance, this paper also proposes a Central-stage Load-balanced-based Distributed Scheduling algorithm (CLDS) for CB-3Clos. The results show that, compared with Concurrent Round-Robin based Dispatching (CRRD) algorithm based on MSM, CLDS algorithm has high throughput irrespective with the traffic model and better performance in mean packet delay.展开更多
This paper proposes a distributed fair queuing algorithm which is based on compensation coordi- nation scheduling in wireless mesh networks, considering such problems as the location-dependent competition and unfair c...This paper proposes a distributed fair queuing algorithm which is based on compensation coordi- nation scheduling in wireless mesh networks, considering such problems as the location-dependent competition and unfair channel bandwidth allocation among nodes. The data communication process requiring the establishment of compensation coordination scheduling model is divided into three periods： the sending period, the compensation period and the dormancy period. According to model parameters, time constraint functions are designed to limit the execution length of each period. The algorithms guarantee that the nodes complete fair transmission of network packets together in accordance with the fixed coordination scheduling rule of the model. Simulations and analysis demonstrate the effectiveness of the proposed algorithm in network throughput and fairness.展开更多
To further increase the throughput of wireless multi-hop networks,a distributed scheduling method is proposed,which takes physical interference model into account.It is assumed that nodes in the network can perform ph...To further increase the throughput of wireless multi-hop networks,a distributed scheduling method is proposed,which takes physical interference model into account.It is assumed that nodes in the network can perform physical carrier sensing,and the carrier sensing range can be set to different values.In the traditional carrier sensing mechanism,the carrier sensing range is computed under the protocol interference model,which is not accurate.Here the optimal carrier sensing range with physical interference model is achieved.Each sending node implements the distributed approach in three phases at each time slot,and all the concurrent transmissions are interference free.Good performance can be achieved under this scheduling approach.The approximation ratio of the distributed method to the optimal one is also proved.展开更多
This work aims to resolve the distributed heterogeneous permutation flow shop scheduling problem(DHPFSP)with minimizing makespan and total energy consumption(TEC).To solve this NP-hard problem,this work proposed a com...This work aims to resolve the distributed heterogeneous permutation flow shop scheduling problem(DHPFSP)with minimizing makespan and total energy consumption(TEC).To solve this NP-hard problem,this work proposed a competitive and cooperative-based strength Pareto evolutionary algorithm(CCSPEA)which contains the following features:1)An initialization based on three heuristic rules is developed to generate a population with great diversity and convergence.2)A comprehensive metric combining convergence and diversity metrics is used to better represent the heuristic information of a solution.3)A competitive selection is designed which divides the population into a winner and a loser swarms based on the comprehensive metric.4)A cooperative evolutionary schema is proposed for winner and loser swarms to accelerate the convergence of global search.5)Five local search strategies based on problem knowledge are designed to improve convergence.6)Aproblem-based energy-saving strategy is presented to reduce TEC.Finally,to evaluate the performance of CCSPEA,it is compared to four state-of-art and run on 22 instances based on the Taillard benchmark.The numerical experiment results demonstrate that 1)the proposed comprehensive metric can efficiently represent the heuristic information of each solution to help the later step divide the population.2)The global search based on the competitive and cooperative schema can accelerate loser solutions convergence and further improve the winner’s exploration.3)The problembased initialization,local search,and energy-saving strategies can efficiently reduce the makespan and TEC.4)The proposed CCSPEA is superior to the state-of-art for solving DHPFSP.展开更多
This paper proposes an agent-based distributed scheduling system against the background of the deregulation of electric utility and the smart grid for the renewable energy, and then focuses on a maintenance scheduling...This paper proposes an agent-based distributed scheduling system against the background of the deregulation of electric utility and the smart grid for the renewable energy, and then focuses on a maintenance scheduling in the context of real problems. A synchronous backtrack algorithm, a welD-known method for distributed scheduling problems, has difficulties handling （A） rapid schedule adjustments and （B） impartial assignment. Thus, this paper proposes two kinds of heuristics： （1） parallel assignment and （2） multiple priority strategies, and developed the distributed scheduling system which makes use of the heuristics. It consists of schedulers for each power station and mediation agents which have cloning and merging functions to support the implementation of the heuristics. Finally, the result of experiment shows an improvement when handling the rapid adjustment and the impartiality issues with reasonable computational overhead.展开更多
A distributionally robust scheduling strategy is proposed to address the complex benefit allocation problem in regional integrated energy systems(RIESs)with multiple stakeholders.A two-level Stackelberg game model is ...A distributionally robust scheduling strategy is proposed to address the complex benefit allocation problem in regional integrated energy systems(RIESs)with multiple stakeholders.A two-level Stackelberg game model is established,with the RIES operator as the leader and the users as the followers.It considers the interests of the RIES operator and demand response users in energy trading.The leader optimizes time-of-use(TOU)energy prices to minimize costs while users formulate response plans based on prices.A two-stage distributionally robust game model with comprehensive norm constraints,which encompasses the two-level Stackelberg game model in the day-ahead scheduling stage,is constructed to manage wind power uncertainty.Karush-Kuhn-Tucker(KKT)conditions transform the two-level Stackelberg game model into a single-level robust optimization model,which is then solved using column and constraint generation(C&CG).Numerical results demonstrate the effectiveness of the proposed strategy in balancing stakeholders'interests and mitigating wind power risks.展开更多
This paper presents a holistic pricing and distributed scheduling framework for multi-microgrid system(MMGS)that considers the supply‒demand relationships of the coupled electricity‒carbon market to promote collaborat...This paper presents a holistic pricing and distributed scheduling framework for multi-microgrid system(MMGS)that considers the supply‒demand relationships of the coupled electricity‒carbon market to promote collaborative market trading within the MMGS for economic and environmental benefit improvement.Initially,an operation model of each microgrid is developed by synthetically considering electricity-carbon operational constraints related to generation units and energy storage units.Then,a collaborative optimization strategy of the MMGS is established according to the Nash bargaining game(NBG)model with the objective of maximizing overall operational revenue.To determine the trading schedule,an accelerated prediction-correction-based alternating direction method of multipliers(PCB-ADMM)algorithm is employed to derive the optimal scheduling strategy of MMGS in a distributed manner,ensuring the privacy preservation of individual microgrids.For electricity-carbon pricing,a supply-demand ratio(SDR)based pricing strategy is proposed to dynamically update electricity and carbon allowance prices,which fundamentally guides and incentivizes each microgrid to trade within the MMGS preferentially rather than with an upstream distribution network.Finally,a study case verifies the effectiveness of the proposed framework in enhancing the operation economy and environmental friendliness of the entire MMGS.展开更多
In this research,a novel dynamic and heterogeneous identity based cooperative co-evolutionary algorithm(DHICCA)is proposed for addressing the distributed lot-streaming flowshop scheduling problem(DLSFSP)with the objec...In this research,a novel dynamic and heterogeneous identity based cooperative co-evolutionary algorithm(DHICCA)is proposed for addressing the distributed lot-streaming flowshop scheduling problem(DLSFSP)with the objective to minimize the makespan.A two-layer-vector representation is devised to bridge the solution space of DLSFSP and the search space of DHICCA.In the evolution of DHICCA,population individuals are endowed with heterogeneous identities according to their quality,including superior individuals,ordinary individuals,and inferior individuals,which serve local exploitation,global exploration,and diversified restart,respectively.Because individuals with different identities require different evolutionary mechanisms to fully unleash their respective potentials,identity-specific evolutionary operators are devised to evolve them in a cooperative co-evolutionary way.This is important to use limited population resources to solve complex optimization problems.Specifically,exploitation is carried out on superior individuals by devising three exploitative operators with different intensities based on techniques of variable neighborhood,destruction-construction,and gene targeting.Exploration is executed on ordinary individuals by a newly constructed discrete Jaya algorithm and a probability crossover strategy.In addition,restart is performed on inferior individuals to introduce new evolutionary individuals to the population.After the cooperative co-evolution,all individuals with different identities are merged as a population again,and their identities are dynamically adjusted by new evaluation.The influence of parameters on the algorithm is investigated based on design-of-experiment and comprehensive computational experiments are used to evaluate the performance of all algorithms.The results validate the effectiveness of special designs and show that DHICCA performs more efficient than the existing state-of-the-art algorithms in solving the DLSFSP.展开更多
The distributed permutation flowshop scheduling problem(DPFSP)has received increasing attention in recent years,which always assumes that the machine can process without restrictions.However,in practical production,ma...The distributed permutation flowshop scheduling problem(DPFSP)has received increasing attention in recent years,which always assumes that the machine can process without restrictions.However,in practical production,machine preventive maintenance is required to prevent machine breakdowns.Therefore,this paper studies the DPFSP with preventive maintenance(PM/DPFSP)aiming at minimizing the total flowtime.For solving the problem,a discrete gray wolf optimization algorithm with restart mechanism(DGWO_RM)is proposed.In the initialization phase,a heuristic algorithm that takes into consideration preventive maintenance and idle time is employed to elevate the quality of the initial solution.Next,four local search strategies are proposed for further enhancing the exploitation capability.Furthermore,a restart mechanism is integrated into algorithm to avert the risk of converging prematurely to a suboptimal solution,thereby ensuring a broader exploration of potential solutions.Finally,comprehensive experiments studies are carried out to illustrate the effectiveness of the proposed strategy and to verify the performance of DGWO_RM.The obtained results show that the proposed DGWO_RM significantly outperforms the four state-of-the-art algorithms in solving PM/DPFSP.展开更多
As transmission power among interconnected re-gional grids is increasing rapidly,formulating the power distri-bution and maintenance schedules of multiple paralleled trans-mission channels is critical to ensure the se...As transmission power among interconnected re-gional grids is increasing rapidly,formulating the power distri-bution and maintenance schedules of multiple paralleled trans-mission channels is critical to ensure the secure and economic operation in an AC/DC power system.A coordinated optimiza-tion for power distribution and maintenance schedules(COPD-MS)of multiple paralleled transmission channels is proposed,and the active power losses of the resistances of earth line in the high-voltage direct current(HVDC)transmission lines are taken into account when one pole is under maintenance while the other pole is operating under monopolar ground circuit.To solve the proposed COPD-MS model efficiently,the generalized Benders decomposition(GBD)algorithm is used to decompose the proposed COPD-MS model into master problem of mainte-nance scheduling and sub-problems of power distribution sched-uling,and the optimal solution to the original model is obtained by the alternative iteration between them.Moreover,a recur-sive acceleration(RA)algorithm is proposed to solve the master problem,which can directly obtain its solution in the new itera-tion by using the solution in the last iteration and the newly added Benders cut.Convex relaxation techniques are applied to the nonlinear constraints in the sub-problem to ensure the reli-able convergence.Additionally,since there is no coupling among the power distributions during each time interval in the sub-problem,parallel computing technology is used to improve the computational efficiency.Finally,case studies on the modi-fied IEEE 39-bus system and an actual 1524-bus large-scale AC/DC hybrid power system demonstrate the effectiveness of the proposed COPD-MS model.展开更多
Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study...Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.展开更多
With the new power system growth,cooperative scheduling among multiple microgrids(MMG)is emerging.Complex energy coupling relationship within the MMG system poses challenges for achieving energy complementarity.Tradit...With the new power system growth,cooperative scheduling among multiple microgrids(MMG)is emerging.Complex energy coupling relationship within the MMG system poses challenges for achieving energy complementarity.Tradition-alMMGscheduling faces limitations in achieving economic scheduling and privacy protection due to the extensive need for energy data.To address the MMG scheduling issue,this paper proposes a novel distributed intelligent cooperative scheduling model,named E-Hive,for optimal economic operation.We employ two techniques into E-Hive:1)We develop a multi-agent deep reinforcement learning module to achieve cooperative scheduling in MMG.2)We employ a distributed architecture for inter-microgrid communication while ensuring privacy protection of energy data.Evaluation results show that the E-Hive model enables cooperative scheduling relying solely on local microgrid data,preserving the privacy of each microgrid.Furthermore,the operating cost is reduced by up to 16.4%compared to state-of-the-art methods,enhancing the economic benefit.展开更多
基金supported in part by the National Key Research and Development Program of China under Grant No.2021YFF0901300in part by the National Natural Science Foundation of China under Grant Nos.62173076 and 72271048.
文摘The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because of its straightforward,single-solution evolution framework.However,a potential draw-back of IGA is the lack of utilization of historical information,which could lead to an imbalance between exploration and exploitation,especially in large-scale DPFSPs.As a consequence,this paper develops an IGA with memory and learning mechanisms(MLIGA)to efficiently solve the DPFSP targeted at the mini-malmakespan.InMLIGA,we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search,by extending,reconstructing,and reinforcing the information from previous solutions.In addition,we design a twolayer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory mechanism.Meanwhile,to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA,a probability curve-based acceptance criterion is proposed by combining a cube root function with custom rules.At last,a discrete adaptive learning rate is employed to enhance the stability of the memory and learningmechanisms.Complete ablation experiments are utilized to verify the effectiveness of the memory mechanism,and the results show that this mechanism is capable of improving the performance of IGA to a large extent.Furthermore,through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks,we have discovered that MLI-GA demonstrates significant potential for solving large-scale DPFSPs.This indicates that MLIGA is well-suited for real-world distributed flow shop scheduling.
基金partially supported by the National Key Research and Development Program of the Ministry of Science and Technology of China(2022YFE0114200)the National Natural Science Foundation of China(U20A6004).
文摘This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved proximal policy optimization(IPPO)method to make real-time decisions for the DHHBFSP.A multi-objective Markov decision process is modeled for the DHHBFSP,where the reward function is represented by a vector with dynamic weights instead of the common objectiverelated scalar value.A factory agent(FA)is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision quality.Multiple FAs work asynchronously to allocate jobs that arrive randomly at the shop.A two-stage training strategy is introduced in the IPPO,which learns from both single-and dual-policy data for better data utilization.The proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization(PPO),dispatch rules,multi-objective metaheuristics,and multi-agent reinforcement learning methods.Extensive experimental results suggest that the proposed strategies offer significant improvements to the basic PPO,and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.
基金supported by the Natural Science Foundation of Anhui Province(Grant Number 2208085MG181)the Science Research Project of Higher Education Institutions in Anhui Province,Philosophy and Social Sciences(Grant Number 2023AH051063)the Open Fund of Key Laboratory of Anhui Higher Education Institutes(Grant Number CS2021-ZD01).
文摘The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.
基金partially supported by the Guangdong Basic and Applied Basic Research Foundation(2023A1515011531)the National Natural Science Foundation of China under Grant 62173356+2 种基金the Science and Technology Development Fund(FDCT),Macao SAR,under Grant 0019/2021/AZhuhai Industry-University-Research Project with Hongkong and Macao under Grant ZH22017002210014PWCthe Key Technologies for Scheduling and Optimization of Complex Distributed Manufacturing Systems(22JR10KA007).
文摘The flow shop scheduling problem is important for the manufacturing industry.Effective flow shop scheduling can bring great benefits to the industry.However,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted meta-heuristics.This work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned DHFSP.Second,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are proposed.According to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local space.Instead of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during iterations.Finally,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed algorithms.The experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random strategy.To verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving DHFSP.The Friedman test is executed on the results by five algorithms.It is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness.
基金supported by the Sichuan Science and Technology Program(grant number 2022YFG0123).
文摘In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.
基金supported by the National Nature Science Foundation of China(62272078)
文摘Dear Editor,The distributed constraint optimization problems(DCOPs) [1]-[3]provide an efficient model for solving the cooperative problems of multi-agent systems, which has been successfully applied to model the real-world problems like the distributed scheduling [4], sensor network management [5], [6], multi-robot coordination [7], and smart grid [8]. However, DCOPs were not well suited to solve the problems with continuous variables and constraint cost in functional form, such as the target tracking sensor orientation [9], the air and ground cooperative surveillance [10], and the sensor network coverage [11].
基金supported by the Science and Technology Project of State Grid Corporation“Research and Application of Internet Operation Platform for Ubiquitous Power Internet of Things”(5700-201955462A-0-0-00).
文摘The energy Internet operation platform provides market entities such as energy users,energy enterprises,suppliers,and governments with the ability to interact,transact,and manage various operations.Owing to the large number of platform users,complex businesses,and large amounts of data-mining tasks,it is necessary to solve the problems afflicting platform task scheduling and the provision of simultaneous access to a large number of users.This study examines the two core technologies of platform task scheduling and multiuser concurrent processing,proposing a distributed task-scheduling method and a technical implementation scheme based on the particle swarm optimization algorithm,and presents a systematic solution in concurrent processing for massive user numbers.Based on the results of this study,the energy internet operation platform can effectively deal with the concurrent access of tens of millions of users and complex task-scheduling problems.
基金supported by the National Key Research and Development Program of China (2016YFC0400207)the National Natural Science Foundation of China (51222905, 51621061, 51509130)+2 种基金the Natural Science Foundation of Jiangsu Province, China (BK20150908)the Discipline Innovative Engineering Plan (111 Program, B14002)the Jiangsu Key Laboratory of Agricultural Meteorology Foundation (JKLAM1601)
文摘Furrow irrigation is a traditional widely-used irrigation method in the world. Understanding the dynamics of soil water distribution is essential to developing effective furrow irrigation strategies, especially in water-limited regions. The objectives of this study are to analyze root length density distribution and to explore soil water dynamics by simulating soil water content using a HYDRUS-2D model with consideration of root water uptake for furrow irrigated tomato plants in a solar greenhouse in Northwest China. Soil water contents were also in-situ observed by the ECH_2O sensors from 4 June to 19 June and from 21 June to 4 July, 2012. Results showed that the root length density of tomato plants was concentrated in the 0–50 cm soil layers, and radiated 0–18 cm toward the furrow and 0–30 cm along the bed axis. Soil water content values simulated by the HYDRUS-2D model agreed well with those observed by the ECH_2O sensors, with regression coefficient of 0.988, coefficient of determination of 0.89, and index of agreement of 0.97. The HYDRUS-2D model with the calibrated parameters was then applied to explore the optimal irrigation scheduling. Infrequent irrigation with a large amount of water for each irrigation event could result in 10%–18% of the irrigation water losses. Thus we recommend high irrigation frequency with a low amount of water for each irrigation event in greenhouses for arid region. The maximum high irrigation amount and the suitable irrigation interval required to avoid plant water stress and drainage water were 34 mm and 6 days, respectively, for given daily average transpiration rate of 4.0 mm/d. To sum up, the HYDRUS-2D model with consideration of root water uptake can be used to improve irrigation scheduling for furrow irrigated tomato plants in greenhouses in arid regions.
基金Funded by the National Basic Research Program of China (No.2007CB307102)National High Tech Research and Development Program of China (No.2005AA121210)National Natural Science Foundation of China (No. 60572042)
文摘Current MSM switching fabric has poor performance under unbalanced traffic. This paper presents an alternative, novel Central-stage Buffered Three-stage Clos switching (CB-3Clos) fabric and proves that this fabric can emulate output queuing switch without any speedup. By analyzing the condition to satisfy the central-stage load-balance, this paper also proposes a Central-stage Load-balanced-based Distributed Scheduling algorithm (CLDS) for CB-3Clos. The results show that, compared with Concurrent Round-Robin based Dispatching (CRRD) algorithm based on MSM, CLDS algorithm has high throughput irrespective with the traffic model and better performance in mean packet delay.
基金Supported by the National Natural Science Foundation of China （61071096, 61003233, 61073103 ） and the Research Fund for the Doctoral Program of Higher Education （20100162110012）.
文摘This paper proposes a distributed fair queuing algorithm which is based on compensation coordi- nation scheduling in wireless mesh networks, considering such problems as the location-dependent competition and unfair channel bandwidth allocation among nodes. The data communication process requiring the establishment of compensation coordination scheduling model is divided into three periods： the sending period, the compensation period and the dormancy period. According to model parameters, time constraint functions are designed to limit the execution length of each period. The algorithms guarantee that the nodes complete fair transmission of network packets together in accordance with the fixed coordination scheduling rule of the model. Simulations and analysis demonstrate the effectiveness of the proposed algorithm in network throughput and fairness.
基金Supported by the National Basic Research Program of China(No.2007CB307105)the National Natural Science Foundation of China(No.60932005)
文摘To further increase the throughput of wireless multi-hop networks,a distributed scheduling method is proposed,which takes physical interference model into account.It is assumed that nodes in the network can perform physical carrier sensing,and the carrier sensing range can be set to different values.In the traditional carrier sensing mechanism,the carrier sensing range is computed under the protocol interference model,which is not accurate.Here the optimal carrier sensing range with physical interference model is achieved.Each sending node implements the distributed approach in three phases at each time slot,and all the concurrent transmissions are interference free.Good performance can be achieved under this scheduling approach.The approximation ratio of the distributed method to the optimal one is also proved.
基金supported by the National Natural Science Foundation of China under Grant Nos.62076225 and 62122093the Open Project of Xiangjiang Laboratory under Grant No 22XJ02003.
文摘This work aims to resolve the distributed heterogeneous permutation flow shop scheduling problem(DHPFSP)with minimizing makespan and total energy consumption(TEC).To solve this NP-hard problem,this work proposed a competitive and cooperative-based strength Pareto evolutionary algorithm(CCSPEA)which contains the following features:1)An initialization based on three heuristic rules is developed to generate a population with great diversity and convergence.2)A comprehensive metric combining convergence and diversity metrics is used to better represent the heuristic information of a solution.3)A competitive selection is designed which divides the population into a winner and a loser swarms based on the comprehensive metric.4)A cooperative evolutionary schema is proposed for winner and loser swarms to accelerate the convergence of global search.5)Five local search strategies based on problem knowledge are designed to improve convergence.6)Aproblem-based energy-saving strategy is presented to reduce TEC.Finally,to evaluate the performance of CCSPEA,it is compared to four state-of-art and run on 22 instances based on the Taillard benchmark.The numerical experiment results demonstrate that 1)the proposed comprehensive metric can efficiently represent the heuristic information of each solution to help the later step divide the population.2)The global search based on the competitive and cooperative schema can accelerate loser solutions convergence and further improve the winner’s exploration.3)The problembased initialization,local search,and energy-saving strategies can efficiently reduce the makespan and TEC.4)The proposed CCSPEA is superior to the state-of-art for solving DHPFSP.
文摘This paper proposes an agent-based distributed scheduling system against the background of the deregulation of electric utility and the smart grid for the renewable energy, and then focuses on a maintenance scheduling in the context of real problems. A synchronous backtrack algorithm, a welD-known method for distributed scheduling problems, has difficulties handling （A） rapid schedule adjustments and （B） impartial assignment. Thus, this paper proposes two kinds of heuristics： （1） parallel assignment and （2） multiple priority strategies, and developed the distributed scheduling system which makes use of the heuristics. It consists of schedulers for each power station and mediation agents which have cloning and merging functions to support the implementation of the heuristics. Finally, the result of experiment shows an improvement when handling the rapid adjustment and the impartiality issues with reasonable computational overhead.
基金supported by National Natural Science Foundation of China(No.52207133)Science and Technology Project of State Grid Corporation of China(No.5400-202112571A-0-5-SF)。
文摘A distributionally robust scheduling strategy is proposed to address the complex benefit allocation problem in regional integrated energy systems(RIESs)with multiple stakeholders.A two-level Stackelberg game model is established,with the RIES operator as the leader and the users as the followers.It considers the interests of the RIES operator and demand response users in energy trading.The leader optimizes time-of-use(TOU)energy prices to minimize costs while users formulate response plans based on prices.A two-stage distributionally robust game model with comprehensive norm constraints,which encompasses the two-level Stackelberg game model in the day-ahead scheduling stage,is constructed to manage wind power uncertainty.Karush-Kuhn-Tucker(KKT)conditions transform the two-level Stackelberg game model into a single-level robust optimization model,which is then solved using column and constraint generation(C&CG).Numerical results demonstrate the effectiveness of the proposed strategy in balancing stakeholders'interests and mitigating wind power risks.
基金supported in part by the National Natural Science Foundation of China(No.52377075)Fundamental Research Funds for the Central Universities(No.2024CDJXY006).
文摘This paper presents a holistic pricing and distributed scheduling framework for multi-microgrid system(MMGS)that considers the supply‒demand relationships of the coupled electricity‒carbon market to promote collaborative market trading within the MMGS for economic and environmental benefit improvement.Initially,an operation model of each microgrid is developed by synthetically considering electricity-carbon operational constraints related to generation units and energy storage units.Then,a collaborative optimization strategy of the MMGS is established according to the Nash bargaining game(NBG)model with the objective of maximizing overall operational revenue.To determine the trading schedule,an accelerated prediction-correction-based alternating direction method of multipliers(PCB-ADMM)algorithm is employed to derive the optimal scheduling strategy of MMGS in a distributed manner,ensuring the privacy preservation of individual microgrids.For electricity-carbon pricing,a supply-demand ratio(SDR)based pricing strategy is proposed to dynamically update electricity and carbon allowance prices,which fundamentally guides and incentivizes each microgrid to trade within the MMGS preferentially rather than with an upstream distribution network.Finally,a study case verifies the effectiveness of the proposed framework in enhancing the operation economy and environmental friendliness of the entire MMGS.
基金supported by the National Natural Science Foundation of China(No.62003258)Natural Science Foundation of Hebei Province(No.F2024204007)Projection of State Key Laboratory for Manufacturing Systems Engineering of Xi’an Jiaotong University(No.sklms 2023002).
文摘In this research,a novel dynamic and heterogeneous identity based cooperative co-evolutionary algorithm(DHICCA)is proposed for addressing the distributed lot-streaming flowshop scheduling problem(DLSFSP)with the objective to minimize the makespan.A two-layer-vector representation is devised to bridge the solution space of DLSFSP and the search space of DHICCA.In the evolution of DHICCA,population individuals are endowed with heterogeneous identities according to their quality,including superior individuals,ordinary individuals,and inferior individuals,which serve local exploitation,global exploration,and diversified restart,respectively.Because individuals with different identities require different evolutionary mechanisms to fully unleash their respective potentials,identity-specific evolutionary operators are devised to evolve them in a cooperative co-evolutionary way.This is important to use limited population resources to solve complex optimization problems.Specifically,exploitation is carried out on superior individuals by devising three exploitative operators with different intensities based on techniques of variable neighborhood,destruction-construction,and gene targeting.Exploration is executed on ordinary individuals by a newly constructed discrete Jaya algorithm and a probability crossover strategy.In addition,restart is performed on inferior individuals to introduce new evolutionary individuals to the population.After the cooperative co-evolution,all individuals with different identities are merged as a population again,and their identities are dynamically adjusted by new evaluation.The influence of parameters on the algorithm is investigated based on design-of-experiment and comprehensive computational experiments are used to evaluate the performance of all algorithms.The results validate the effectiveness of special designs and show that DHICCA performs more efficient than the existing state-of-the-art algorithms in solving the DLSFSP.
基金supported by the National Natural Science Foundation of China(Nos.62473186 and 62273221)Natural Science Foundation of Shandong Province(No.ZR2024MF017)Discipline with Strong Characteristics of Liaocheng University Intelligent Science and Technology(No.319462208).
文摘The distributed permutation flowshop scheduling problem(DPFSP)has received increasing attention in recent years,which always assumes that the machine can process without restrictions.However,in practical production,machine preventive maintenance is required to prevent machine breakdowns.Therefore,this paper studies the DPFSP with preventive maintenance(PM/DPFSP)aiming at minimizing the total flowtime.For solving the problem,a discrete gray wolf optimization algorithm with restart mechanism(DGWO_RM)is proposed.In the initialization phase,a heuristic algorithm that takes into consideration preventive maintenance and idle time is employed to elevate the quality of the initial solution.Next,four local search strategies are proposed for further enhancing the exploitation capability.Furthermore,a restart mechanism is integrated into algorithm to avert the risk of converging prematurely to a suboptimal solution,thereby ensuring a broader exploration of potential solutions.Finally,comprehensive experiments studies are carried out to illustrate the effectiveness of the proposed strategy and to verify the performance of DGWO_RM.The obtained results show that the proposed DGWO_RM significantly outperforms the four state-of-the-art algorithms in solving PM/DPFSP.
基金This work was supported by the National Natural Science Foundation of China(No.51977080)the Natural Science Foundation of Guangdong Province(No.2022A1515010332).
文摘As transmission power among interconnected re-gional grids is increasing rapidly,formulating the power distri-bution and maintenance schedules of multiple paralleled trans-mission channels is critical to ensure the secure and economic operation in an AC/DC power system.A coordinated optimiza-tion for power distribution and maintenance schedules(COPD-MS)of multiple paralleled transmission channels is proposed,and the active power losses of the resistances of earth line in the high-voltage direct current(HVDC)transmission lines are taken into account when one pole is under maintenance while the other pole is operating under monopolar ground circuit.To solve the proposed COPD-MS model efficiently,the generalized Benders decomposition(GBD)algorithm is used to decompose the proposed COPD-MS model into master problem of mainte-nance scheduling and sub-problems of power distribution sched-uling,and the optimal solution to the original model is obtained by the alternative iteration between them.Moreover,a recur-sive acceleration(RA)algorithm is proposed to solve the master problem,which can directly obtain its solution in the new itera-tion by using the solution in the last iteration and the newly added Benders cut.Convex relaxation techniques are applied to the nonlinear constraints in the sub-problem to ensure the reli-able convergence.Additionally,since there is no coupling among the power distributions during each time interval in the sub-problem,parallel computing technology is used to improve the computational efficiency.Finally,case studies on the modi-fied IEEE 39-bus system and an actual 1524-bus large-scale AC/DC hybrid power system demonstrate the effectiveness of the proposed COPD-MS model.
基金This work was supported by Shandong medical and health science and technology development plan project(No.202012070393).
文摘Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.
文摘With the new power system growth,cooperative scheduling among multiple microgrids(MMG)is emerging.Complex energy coupling relationship within the MMG system poses challenges for achieving energy complementarity.Tradition-alMMGscheduling faces limitations in achieving economic scheduling and privacy protection due to the extensive need for energy data.To address the MMG scheduling issue,this paper proposes a novel distributed intelligent cooperative scheduling model,named E-Hive,for optimal economic operation.We employ two techniques into E-Hive:1)We develop a multi-agent deep reinforcement learning module to achieve cooperative scheduling in MMG.2)We employ a distributed architecture for inter-microgrid communication while ensuring privacy protection of energy data.Evaluation results show that the E-Hive model enables cooperative scheduling relying solely on local microgrid data,preserving the privacy of each microgrid.Furthermore,the operating cost is reduced by up to 16.4%compared to state-of-the-art methods,enhancing the economic benefit.
