The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of...The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.展开更多
Coordinated scheduling of multimode plays a pivotal role in the rapid gathering and dissipating of passengers in transport hubs. Based on the survey data, the whole-day reaching time distribution at transfer points of...Coordinated scheduling of multimode plays a pivotal role in the rapid gathering and dissipating of passengers in transport hubs. Based on the survey data, the whole-day reaching time distribution at transfer points of passengers from the dominant mode to the connecting mode was achieved. A GI/M K/1 bulk service queuing system was constituted by putting the passengers' reaching time distribution as the input and the connecting mode as the service institution. Through queuing theory, the relationship between average queuing length under steady-state and headway of the connecting mode was achieved. By putting the minimum total cost of system as optimization objective, the headway as decision variable, a coordinated scheduling model of multimode in intermodal transit hubs was established. At last, a dynamic scheduling strategy was generated to cope with the unexpected changes of the dominant mode. The instance analysis indicates that this model can significantly reduce passengers' queuing time by approximately 17% with no apparently increase in departure frequency, which provides a useful solution for the coordinated scheduling of different transport modes in hubs.展开更多
A two-agent production and transportation coordinated scheduling problem in a single-machine environment is suggested to compete for one machine from different downstream production links or various consumers.The jobs...A two-agent production and transportation coordinated scheduling problem in a single-machine environment is suggested to compete for one machine from different downstream production links or various consumers.The jobs of two agents compete for the processing position on a machine,and after the pro-cessed,they compete for the transport position on a transport vehicle to be trans-ported to two agents.The two agents have different objective functions.The objective function of the first agent is the sum of the makespan and the total trans-portation time,whereas the objective function of the second agent is the sum of the total completion time and the total transportation time.Given the competition between two agents for machine resources and transportation resources,a non-cooperative game model with agents as game players is established.The job pro-cessing position and transportation position corresponding to the two agents are mapped as strategies,and the corresponding objective function is the utility func-tion.To solve the game model,an approximate Nash equilibrium solution algo-rithm based on an improved genetic algorithm(NE-IGA)is proposed.The genetic operation based on processing sequence and transportation sequence,as well as the fitness function based on Nash equilibrium definition,are designed based on the features of the two-agent production and transportation coordination scheduling problem.The effectiveness of the proposed algorithm is demonstrated through numerical experiments of various sizes.When compared to heuristic rules such as the Longest Processing Time first(LPT)and the Shortest Processing Time first(SPT),the objective function values of the two agents are reduced by 4.3%and 2.6% on average.展开更多
Electromagnetic detection satellite（EDS） is a type of Earth observation satellite（EOS）. Satellites observation and data down-link scheduling plays a significant role in improving the efficiency of satellite observ...Electromagnetic detection satellite（EDS） is a type of Earth observation satellite（EOS）. Satellites observation and data down-link scheduling plays a significant role in improving the efficiency of satellite observation systems. However, the current works mainly focus on the scheduling of imaging satellites, little work focuses on the scheduling of EDSes for its specific requirements.And current works mainly schedule satellite resources and data down-link resources separately, not considering them in a globally optimal perspective. The EDSes and data down-link resources are scheduled in an integrated process and the scheduling result is searched globally. Considering the specific constraints of EDS, a coordinate scheduling model for EDS observation tasks and data transmission jobs is established and an algorithm based on the genetic algorithm is proposed. Furthermore, the convergence of our algorithm is proved. To deal with some specific constraints, a solution repairing algorithm of polynomial computing time is designed. Finally, some experiments are conducted to validate the correctness and practicability of our scheduling algorithms.展开更多
There are many flow shop problems of throughput (denoted by FSPT) with constraints of due date in real production planning and scheduling. In this paper, a decomposition and coordination algorithm is proposed based on...There are many flow shop problems of throughput (denoted by FSPT) with constraints of due date in real production planning and scheduling. In this paper, a decomposition and coordination algorithm is proposed based on the analysis of FSPT and under the support of TOC (theory of constraint). A flow shop is at first decomposed into two subsystems named PULL and PUSH by means of bottleneck. Then the subsystem is decomposed into single machine scheduling problems,so the original NP-HARD problem can be transferred into a serial of single machine optimization problems finally. This method reduces the computational complexity, and has been used in a real project successfully.展开更多
Due to the stubborn nature of dynamic job shop scheduling problem,a novel ant colony coordination mechanism is proposed in this paper to search for an optimal schedule in dynamic environment.In ant colony coordination...Due to the stubborn nature of dynamic job shop scheduling problem,a novel ant colony coordination mechanism is proposed in this paper to search for an optimal schedule in dynamic environment.In ant colony coordination mechanism,the dynamic job shop is composed of several autonomous ants.These ants coordinate with each other by simulating the ant foraging behavior of spreading pheromone on the trails,by which they can make information available globally,and further more guide ants make optimal decisions.The proposed mechanism is tested by several instances and the results confirm the validity of it.展开更多
The emergent task is a kind of uncertain event that satellite systems often encounter in the application process.In this paper,the multi-satellite distributed coordinating and scheduling problem considering emergent t...The emergent task is a kind of uncertain event that satellite systems often encounter in the application process.In this paper,the multi-satellite distributed coordinating and scheduling problem considering emergent tasks is studied.Due to the limitation of onboard computational resources and time,common online onboard rescheduling methods for such problems usually adopt simple greedy methods,sacrificing the solution quality to deliver timely solutions.To better solve the problem,a new multi-satellite onboard scheduling and coordinating framework based on multi-solution integration is proposed.This method uses high computational power on the ground and generates multiple solutions,changing the complex onboard rescheduling problem to a solution selection problem.With this method,it is possible that little time is used to generate a solution that is as good as the solutions on the ground.We further propose several multi-satellite coordination methods based on the multi-agent Markov decision process(MMDP)and mixed-integer programming(MIP).These methods enable the satellite to make independent decisions and produce high-quality solutions.Compared with the traditional centralized scheduling method,the proposed distributed method reduces the cost of satellite communication and increases the response speed for emergent tasks.Extensive experiments show that the proposed multi-solution integration framework and the distributed coordinating strategies are efficient and effective for onboard scheduling considering emergent tasks.展开更多
This paper studies the coordinated planning of transmission tasks in the heterogeneous space networks to enable efficient sharing of ground stations cross satellite systems.Specifically,we first formulate the coordina...This paper studies the coordinated planning of transmission tasks in the heterogeneous space networks to enable efficient sharing of ground stations cross satellite systems.Specifically,we first formulate the coordinated planning problem into a mixed integer liner programming(MILP)problem based on time expanded graph.Then,the problem is transferred and reformulated into a consensus optimization framework which can be solved by satellite systems parallelly.With alternating direction method of multipliers(ADMM),a semi-distributed coordinated transmission task planning algorithm is proposed,in which each satellite system plans its own tasks based on local information and limited communication with the coordination center.Simulation results demonstrate that compared with the centralized and fully-distributed methods,the proposed semi-distributed coordinated method can strike a better balance among task complete rate,complexity,and the amount of information required to be exchanged.展开更多
Aiming at multi-agent coordinated scheduling problems in power systems under uncertainty,a generic projection and decomposition(P&D)approach is proposed in this letter.The canonical min-max-min two-stage robust op...Aiming at multi-agent coordinated scheduling problems in power systems under uncertainty,a generic projection and decomposition(P&D)approach is proposed in this letter.The canonical min-max-min two-stage robust optimization(TSRO)model with coupling constraints is equivalent to a concise robust optimization(RO)model in the version of mixed-integer linear programming(MILP)via feasible region projection.The decentralized decoupling of the non-convex MILP problem is realized through a dual decomposition algorithm,which ensures the fast convergence to a high-quality solution in the distributed optimization.Numerical tests verify the superior performance of the proposed P&D approach over the existing distributed TSRO method.展开更多
基金funded by the National Key Research and Development Program of China(2024YFE0106800)Natural Science Foundation of Shandong Province(ZR2021ME199).
文摘The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.
基金Projects(51278221,51378076)supported by the National Natural Science Foundation of China
文摘Coordinated scheduling of multimode plays a pivotal role in the rapid gathering and dissipating of passengers in transport hubs. Based on the survey data, the whole-day reaching time distribution at transfer points of passengers from the dominant mode to the connecting mode was achieved. A GI/M K/1 bulk service queuing system was constituted by putting the passengers' reaching time distribution as the input and the connecting mode as the service institution. Through queuing theory, the relationship between average queuing length under steady-state and headway of the connecting mode was achieved. By putting the minimum total cost of system as optimization objective, the headway as decision variable, a coordinated scheduling model of multimode in intermodal transit hubs was established. At last, a dynamic scheduling strategy was generated to cope with the unexpected changes of the dominant mode. The instance analysis indicates that this model can significantly reduce passengers' queuing time by approximately 17% with no apparently increase in departure frequency, which provides a useful solution for the coordinated scheduling of different transport modes in hubs.
基金This work was supported in part by the Project of Liaoning BaiQianWan Talents Program under Grand No.2021921089the Science Research Foundation of Educational Department of Liaoning Province under Grand No.LJKQZ2021057 and WJGD2020001+2 种基金the Key Program of Social Science Planning Foundation of Liaoning Province under Grant L21AGL017the special project of SUT on serving local economic and social development decision-making under Grant FWDFGD2021019the“Double First-Class”Construction Project in Liaoning Province under Grant ZDZRGD2020037.
文摘A two-agent production and transportation coordinated scheduling problem in a single-machine environment is suggested to compete for one machine from different downstream production links or various consumers.The jobs of two agents compete for the processing position on a machine,and after the pro-cessed,they compete for the transport position on a transport vehicle to be trans-ported to two agents.The two agents have different objective functions.The objective function of the first agent is the sum of the makespan and the total trans-portation time,whereas the objective function of the second agent is the sum of the total completion time and the total transportation time.Given the competition between two agents for machine resources and transportation resources,a non-cooperative game model with agents as game players is established.The job pro-cessing position and transportation position corresponding to the two agents are mapped as strategies,and the corresponding objective function is the utility func-tion.To solve the game model,an approximate Nash equilibrium solution algo-rithm based on an improved genetic algorithm(NE-IGA)is proposed.The genetic operation based on processing sequence and transportation sequence,as well as the fitness function based on Nash equilibrium definition,are designed based on the features of the two-agent production and transportation coordination scheduling problem.The effectiveness of the proposed algorithm is demonstrated through numerical experiments of various sizes.When compared to heuristic rules such as the Longest Processing Time first(LPT)and the Shortest Processing Time first(SPT),the objective function values of the two agents are reduced by 4.3%and 2.6% on average.
基金supported by the National Natural Science Foundation of China(6110118461174159)
文摘Electromagnetic detection satellite（EDS） is a type of Earth observation satellite（EOS）. Satellites observation and data down-link scheduling plays a significant role in improving the efficiency of satellite observation systems. However, the current works mainly focus on the scheduling of imaging satellites, little work focuses on the scheduling of EDSes for its specific requirements.And current works mainly schedule satellite resources and data down-link resources separately, not considering them in a globally optimal perspective. The EDSes and data down-link resources are scheduled in an integrated process and the scheduling result is searched globally. Considering the specific constraints of EDS, a coordinate scheduling model for EDS observation tasks and data transmission jobs is established and an algorithm based on the genetic algorithm is proposed. Furthermore, the convergence of our algorithm is proved. To deal with some specific constraints, a solution repairing algorithm of polynomial computing time is designed. Finally, some experiments are conducted to validate the correctness and practicability of our scheduling algorithms.
基金Supported by National Natural Science Foundation of P. R. China (60274013)
文摘There are many flow shop problems of throughput (denoted by FSPT) with constraints of due date in real production planning and scheduling. In this paper, a decomposition and coordination algorithm is proposed based on the analysis of FSPT and under the support of TOC (theory of constraint). A flow shop is at first decomposed into two subsystems named PULL and PUSH by means of bottleneck. Then the subsystem is decomposed into single machine scheduling problems,so the original NP-HARD problem can be transferred into a serial of single machine optimization problems finally. This method reduces the computational complexity, and has been used in a real project successfully.
基金National Natural Science Foundation of China(No.50575137)National Science and Technology Support Project(No.2006BAF01A44)National High Technology Research and Development Program of China(863 Program,No.2007AA04Z109)
文摘Due to the stubborn nature of dynamic job shop scheduling problem,a novel ant colony coordination mechanism is proposed in this paper to search for an optimal schedule in dynamic environment.In ant colony coordination mechanism,the dynamic job shop is composed of several autonomous ants.These ants coordinate with each other by simulating the ant foraging behavior of spreading pheromone on the trails,by which they can make information available globally,and further more guide ants make optimal decisions.The proposed mechanism is tested by several instances and the results confirm the validity of it.
基金supported by the National Natural Science Foundation of China(72001212,71701204,71801218)the China Hunan Postgraduate Research Innovating Project(CX2018B020)。
文摘The emergent task is a kind of uncertain event that satellite systems often encounter in the application process.In this paper,the multi-satellite distributed coordinating and scheduling problem considering emergent tasks is studied.Due to the limitation of onboard computational resources and time,common online onboard rescheduling methods for such problems usually adopt simple greedy methods,sacrificing the solution quality to deliver timely solutions.To better solve the problem,a new multi-satellite onboard scheduling and coordinating framework based on multi-solution integration is proposed.This method uses high computational power on the ground and generates multiple solutions,changing the complex onboard rescheduling problem to a solution selection problem.With this method,it is possible that little time is used to generate a solution that is as good as the solutions on the ground.We further propose several multi-satellite coordination methods based on the multi-agent Markov decision process(MMDP)and mixed-integer programming(MIP).These methods enable the satellite to make independent decisions and produce high-quality solutions.Compared with the traditional centralized scheduling method,the proposed distributed method reduces the cost of satellite communication and increases the response speed for emergent tasks.Extensive experiments show that the proposed multi-solution integration framework and the distributed coordinating strategies are efficient and effective for onboard scheduling considering emergent tasks.
基金supported in part by the NSF China under Grant(61701365,61801365,62001347)in part by Natural Science Foundation of Shaanxi Province(2020JQ-686)+4 种基金in part by the China Postdoctoral Science Foundation under Grant(2018M643581,2019TQ0210,2019TQ0241,2020M673344)in part by Young Talent fund of University Association for Science and Technology in Shaanxi,China(20200112)in part by Key Research and Development Program in Shaanxi Province of China(2021GY066)in part by Postdoctoral Foundation in Shaanxi Province of China(2018BSHEDZZ47)the Fundamental Research Funds for the Central Universities。
文摘This paper studies the coordinated planning of transmission tasks in the heterogeneous space networks to enable efficient sharing of ground stations cross satellite systems.Specifically,we first formulate the coordinated planning problem into a mixed integer liner programming(MILP)problem based on time expanded graph.Then,the problem is transferred and reformulated into a consensus optimization framework which can be solved by satellite systems parallelly.With alternating direction method of multipliers(ADMM),a semi-distributed coordinated transmission task planning algorithm is proposed,in which each satellite system plans its own tasks based on local information and limited communication with the coordination center.Simulation results demonstrate that compared with the centralized and fully-distributed methods,the proposed semi-distributed coordinated method can strike a better balance among task complete rate,complexity,and the amount of information required to be exchanged.
基金supported in part by the National Research Foundation(NRF)of Singapore,Intra-CREATE(No.NRF2022-ITS010-0005)Ministry of Education Singapore under its Award Ac RF TIER 1 RG60/22the NRF of Singapore,Energy Market Authority under its Energy Programme(EP Award EMAEP004-EKJGC-0003)。
文摘Aiming at multi-agent coordinated scheduling problems in power systems under uncertainty,a generic projection and decomposition(P&D)approach is proposed in this letter.The canonical min-max-min two-stage robust optimization(TSRO)model with coupling constraints is equivalent to a concise robust optimization(RO)model in the version of mixed-integer linear programming(MILP)via feasible region projection.The decentralized decoupling of the non-convex MILP problem is realized through a dual decomposition algorithm,which ensures the fast convergence to a high-quality solution in the distributed optimization.Numerical tests verify the superior performance of the proposed P&D approach over the existing distributed TSRO method.
