The proliferation of carrier aircraft and the integration of unmanned aerial vehicles(UAVs)on aircraft carriers present new challenges to the automation of launch and recovery operations.This paper investigates a coll...The proliferation of carrier aircraft and the integration of unmanned aerial vehicles(UAVs)on aircraft carriers present new challenges to the automation of launch and recovery operations.This paper investigates a collaborative scheduling problem inherent to the operational processes of carrier aircraft,where launch and recovery tasks are conducted concurrently on the flight deck.The objective is to minimize the cumulative weighted waiting time in the air for recovering aircraft and the cumulative weighted delay time for launching aircraft.To tackle this challenge,a multiple population self-adaptive differential evolution(MPSADE)algorithm is proposed.This method features a self-adaptive parameter updating mechanism that is contingent upon population diversity,an asynchronous updating scheme,an individual migration operator,and a global crossover mechanism.Additionally,comprehensive experiments are conducted to validate the effectiveness of the proposed model and algorithm.Ultimately,a comparative analysis with existing operation modes confirms the enhanced efficiency of the collaborative operation mode.展开更多
In response to the challenges faced by unmanned swarms in mountain obstacle-breaching missions within complex terrains,such as poor task-resource coupling,lengthy solution generation times,and poor inter-platform coll...In response to the challenges faced by unmanned swarms in mountain obstacle-breaching missions within complex terrains,such as poor task-resource coupling,lengthy solution generation times,and poor inter-platform collaboration,an unmanned swarm scheduling strategy tailored is proposed for mountain obstacle-breaching missions.Initially,by formalizing the descriptions of obstacle breaching operations,the swarm,and obstacle targets,an optimization model is constructed with the objectives of expected global benefit,timeliness,and task completion degree.A meta-task decomposition and reassembly strategy is then introduced to more precisely match the capabilities of unmanned platforms with task requirements.Additionally,a meta-task decomposition optimization model and a meta-task allocation operator are incorporated to achieve efficient allocation of swarm resources and collaborative scheduling.Simulation results demonstrate that the model can accurately generate reasonable and feasible obstacle breaching execution plans for unmanned swarms based on specific task requirements and environmental conditions.Moreover,compared to conventional strategies,the proposed strategy enhances task completion degree and expected returns while reducing the execution time of the plans.展开更多
Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e....Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.展开更多
Aimed to address the multi-objective scheduling problem in printing workshops,a hybrid optimization algorithm combining Particle Swarm Optimization(PSO),Genetic Algorithm(GA),and Simulated Annealing(SA)was by proposed...Aimed to address the multi-objective scheduling problem in printing workshops,a hybrid optimization algorithm combining Particle Swarm Optimization(PSO),Genetic Algorithm(GA),and Simulated Annealing(SA)was by proposed which called PGA-PSO-SA(Parallel Genetic Algorithm-Particle Swarm Optimization-Simulated Annealing).Firstly,PSO algorithm was used for global search to quickly find the initial solution.Then,GA optimization selection and crossover operations were used to enhance population diversity.Then,SA algorithm was employed for local search to further improve the solution quality.Experimental results showed that this method achieves better results in terms of job completion time,energy consumption,and machine load distribution.Compared to single algorithms,PGA-PSO-SA hybrid algorithm can more effectively find the global optimal solution,enhancing the overall performance of the scheduling scheme.The research results provides new ideas and methods for scheduling optimization in printing workshops.展开更多
With the maturation of emerging information technologies(Internet of Things,cloud computing,and big data),distributed manufacturing has emerged as an important model for future manufacturing.3D printing,with its integ...With the maturation of emerging information technologies(Internet of Things,cloud computing,and big data),distributed manufacturing has emerged as an important model for future manufacturing.3D printing,with its integrated molding and design freedom,is a powerful catalyst for distributed manufacturing.This paper investigates the integrated production and transportation scheduling problem in distributed 3D printing.To solve this problem,we decompose the original problem into three sub-problems and design a multilevel optimization algorithm.We employ a genetic algorithm in the outer-level optimization to determine the optimal allocation of parts to machines.In the inner-level optimization,we utilize a simulated annealing algorithm to tackle the vehicle routing problem during the transportation stage followed by a local search algorithm to address the scheduling problem encountered during the production stage.Our algorithm is validated using real data from a 3D printing company,and the results show that our algorithm can obtain solutions that are the same as or better than those of Gurobi in a reasonable time for small-sized instances.Additionally,three types of initial methods are tested on large-sized instances to verify the efficiency of the proposed algorithm,and some interesting insights are also revealed and discussed.展开更多
Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investig...Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investigate the integrated scheduling of communication,sensing,and control for UAV-aided FSO communication systems.Initially,a sensing-control model is established via the control theory.Moreover,an FSO communication channel model is established by considering the effects of atmospheric loss,atmospheric turbulence,geometrical loss,and angle-of-arrival fluctuation.Then,the relationship between the motion control of the UAV and radial displacement is obtained to link the control aspect and communication aspect.Assuming that the base station has instantaneous channel state information(CSI)or statistical CSI,the thresholds of the sensing-control pattern activation are designed,respectively.Finally,an integrated scheduling scheme for performing communication,sensing,and control is proposed.Numerical results indicate that,compared with conventional time-triggered scheme,the proposed integrated scheduling scheme obtains comparable communication and control performance,but reduces the sensing consumed power by 52.46%.展开更多
Recovery is a crucial supporting process for carrier aircraft,where a reasonable landing scheduling is expected to guide the fleet landing safely and quickly.Currently,there is little research on this topic,and most o...Recovery is a crucial supporting process for carrier aircraft,where a reasonable landing scheduling is expected to guide the fleet landing safely and quickly.Currently,there is little research on this topic,and most of it neglects potential influence factors,leaving the corresponding supporting efficiency questionable.In this paper,we study the landing scheduling problem for carrier aircraft considering the effects of bolting and aerial refueling.Based on the analysis of recovery mode involving the above factors,two types of primary constraints(i.e.,fuel constraint and wake interval constraint)are first described.Then,taking the landing sequencing as decision variables,a combinatorial optimization model with a compound objective function is formulated.Aiming at an efficient solution,an improved firefly algorithm is designed by integrating multiple evolutionary operators.In addition,a dynamic replanning mechanism is introduced to deal with special situations(i.e.,the occurrence of bolting and fuel shortage),where the high efficiency of the designed algorithm facilitates the online scheduling adjustment within seconds.Finally,numerical simulations with sufficient and insufficient fuel cases are both carried out,highlighting the necessity to consider bolting and aerial refueling during the planning procedure.Simulation results reveal that a higher bolting probability,as well as extra aerial refueling operations caused by fuel shortage,will lead to longer recovery complete time.Meanwhile,due to the strong optimum-seeking capability and solution efficiency of the improved algorithm,adaptive scheduling can be generated within milliseconds to deal with special situations,significantly improving the safety and efficiency of the recovery process.An animation is accessible at bilibili.com/video/BV1QprKY2EwD.展开更多
The dwell scheduling problem for a multifunctional radar system is led to the formation of corresponding optimiza-tion problem.In order to solve the resulting optimization prob-lem,the dwell scheduling process in a sc...The dwell scheduling problem for a multifunctional radar system is led to the formation of corresponding optimiza-tion problem.In order to solve the resulting optimization prob-lem,the dwell scheduling process in a scheduling interval(SI)is formulated as a Markov decision process(MDP),where the state,action,and reward are specified for this dwell scheduling problem.Specially,the action is defined as scheduling the task on the left side,right side or in the middle of the radar idle time-line,which reduces the action space effectively and accelerates the convergence of the training.Through the above process,a model-free reinforcement learning framework is established.Then,an adaptive dwell scheduling method based on Q-learn-ing is proposed,where the converged Q value table after train-ing is utilized to instruct the scheduling process.Simulation results demonstrate that compared with existing dwell schedul-ing algorithms,the proposed one can achieve better scheduling performance considering the urgency criterion,the importance criterion and the desired execution time criterion comprehen-sively.The average running time shows the proposed algorithm has real-time performance.展开更多
In an aircraft final assembly line(AFAL),the rational scheduling of assembly workers to complete tasks in an orderly manner is crucial for enhancing production efficiency.This paper addresses the multi-skilled worker ...In an aircraft final assembly line(AFAL),the rational scheduling of assembly workers to complete tasks in an orderly manner is crucial for enhancing production efficiency.This paper addresses the multi-skilled worker scheduling problem in the AFAL,where the processing time of each task varies due to the assigned workers’skill levels,referred to as variable duration.The objective is to minimize the makespan,i.e.,the total time required for all workers to complete all tasks.A mixed integer linear programming model is formulated under complex constraints including assembly precedence relations,skill requirements,worker skill capabilities,and workspace capacities.To solve the model effectively,a multi-pass priority rule-based heuristic(MPRH)algorithm is proposed.This algorithm integrates 14 activity priority rules and nine worker priority rules with worker weights.Extensive experiments iteratively the best-performing priority rules,and the most effective rule subsets are integrated through a lightweight multi-pass mechanism to enhance its efficiency.The computational results demonstrate that the MPRH can find high-quality solutions effectively within very short central processing unit central processing unit(CPU)time compared to GUROBI.A case study based on real data obtained from an AFAL confirms the necessity and the feasibility of the approach in practical applications.Sensitivity analyses provide valuable insights to real production scenarios.展开更多
The efficiency of carrier-based aircraft support operation scheduling critically impacts aircraft carrier operational effectiveness by determining sortie generation rates,yet faces significant challenges in complex de...The efficiency of carrier-based aircraft support operation scheduling critically impacts aircraft carrier operational effectiveness by determining sortie generation rates,yet faces significant challenges in complex deck environments characterized by resource coupling,dynamic constraints,and highdimensional state-action spaces.Traditional optimization algorithms and vanilla reinforcement learning(RL)struggle with computational inefficiency,sparse rewards,and adaptability to dynamic scenarios,while human expert systems are constrained by the quality of expert knowledge,and poor expert guidance may even have a negative impact.To address these limitations,this paper proposes a human experience-guided actor-critic reinforcement learning framework that synergizes domain expertise with adaptive learning.First,a dynamic Markov decision process(MDP)model is developed to rigorously simulate carrier deck operations,explicitly encoding constraints on positions,resources,and collision avoidance.Building upon this foundation,a human experience database is constructed to enable real-time pattern-matching-based intervention during agent-environment interactions,dynamically correcting wrong actions to avoid catastrophic states while refining exploration efficiency.Finally,the policy and value network objectives are reshaped to incorporate human intent through hybrid reward functions and adaptive guidance weighting,ensuring balanced integration of expert knowledge with RL's exploration capabilities.Extensive simulations across three scenarios demonstrate superior performance compared to state-of-the-art methods and maintain robustness under suboptimal human guidance.These results validate the framework's ability to harmonize human expertise with adaptive learning,offering a practical solution for real-world carriers.展开更多
The multifunctional integration system(MFIS)is based on a common hardware platform that controls and regulates the system’s configurable parameters through software to meet dif-ferent operational requirements.Dwell s...The multifunctional integration system(MFIS)is based on a common hardware platform that controls and regulates the system’s configurable parameters through software to meet dif-ferent operational requirements.Dwell scheduling is a key for the system to realize multifunction and maximize the resource uti-lization.In this paper,an adaptive dwell scheduling optimization model for MFIS which considers the aperture partition and joint radar communication(JRC)waveform is established.To solve the formulated optimization problem,JRC scheduling condi-tions are proposed,including time overlapping condition,beam direction condition and aperture condition.Meanwhile,an effec-tive mechanism to dynamically occupy and release the aperture resource is introduced,where the time-pointer will slide to the earliest ending time of all currently scheduled tasks so that the occupied aperture resource can be released timely.Based on them,an adaptive dwell scheduling algorithm for MFIS with aperture partition and JRC waveform is put forward.Simulation results demonstrate that the proposed algorithm has better com-prehensive scheduling performance than up-to-date algorithms in all considered metrics.展开更多
Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems...Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.展开更多
在现代制造业迅速发展的背景下,车间调度问题在生产制造中起着重要作用,是确保生产流程顺畅、提高生产效率、降低生产成本的关键环节。为全面系统地分析我国车间调度领域的发展状况和研究动态,通过检索中国知网(CNKI)和Web of Science(W...在现代制造业迅速发展的背景下,车间调度问题在生产制造中起着重要作用,是确保生产流程顺畅、提高生产效率、降低生产成本的关键环节。为全面系统地分析我国车间调度领域的发展状况和研究动态,通过检索中国知网(CNKI)和Web of Science(WOS)数据库,获得2000年~2023年以车间调度为主题的中英文文献共14298篇,再运用CiteSpace 6.3 R1软件对文献进行可视化分析,分别从发文量、研究作者、国家地区、机构、关键词共现和被引文献等不同角度审视研究主题结构,结合可视化知识图谱对车间调度领域的现状、趋势和热点进行分析。分析结果表明,由于车间调度在实际应用中的复杂性和多样性,车间调度领域的研究热点也呈现多元化的趋势,目前该领域的研究主要围绕动态与实时调度的优化、多目标优化的平衡追求和智能化算法的应用等方面展开,智能化、绿色化、可持续化和跨学科融合等是未来的发展方向。展开更多
文摘The proliferation of carrier aircraft and the integration of unmanned aerial vehicles(UAVs)on aircraft carriers present new challenges to the automation of launch and recovery operations.This paper investigates a collaborative scheduling problem inherent to the operational processes of carrier aircraft,where launch and recovery tasks are conducted concurrently on the flight deck.The objective is to minimize the cumulative weighted waiting time in the air for recovering aircraft and the cumulative weighted delay time for launching aircraft.To tackle this challenge,a multiple population self-adaptive differential evolution(MPSADE)algorithm is proposed.This method features a self-adaptive parameter updating mechanism that is contingent upon population diversity,an asynchronous updating scheme,an individual migration operator,and a global crossover mechanism.Additionally,comprehensive experiments are conducted to validate the effectiveness of the proposed model and algorithm.Ultimately,a comparative analysis with existing operation modes confirms the enhanced efficiency of the collaborative operation mode.
基金supported by the National Natural Science Foundation of China(61374186)。
文摘In response to the challenges faced by unmanned swarms in mountain obstacle-breaching missions within complex terrains,such as poor task-resource coupling,lengthy solution generation times,and poor inter-platform collaboration,an unmanned swarm scheduling strategy tailored is proposed for mountain obstacle-breaching missions.Initially,by formalizing the descriptions of obstacle breaching operations,the swarm,and obstacle targets,an optimization model is constructed with the objectives of expected global benefit,timeliness,and task completion degree.A meta-task decomposition and reassembly strategy is then introduced to more precisely match the capabilities of unmanned platforms with task requirements.Additionally,a meta-task decomposition optimization model and a meta-task allocation operator are incorporated to achieve efficient allocation of swarm resources and collaborative scheduling.Simulation results demonstrate that the model can accurately generate reasonable and feasible obstacle breaching execution plans for unmanned swarms based on specific task requirements and environmental conditions.Moreover,compared to conventional strategies,the proposed strategy enhances task completion degree and expected returns while reducing the execution time of the plans.
基金the financial support of the National Key Research and Development Plan(2021YFB3302501)the financial support of the National Natural Science Foundation of China(12102077)。
文摘Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.
文摘Aimed to address the multi-objective scheduling problem in printing workshops,a hybrid optimization algorithm combining Particle Swarm Optimization(PSO),Genetic Algorithm(GA),and Simulated Annealing(SA)was by proposed which called PGA-PSO-SA(Parallel Genetic Algorithm-Particle Swarm Optimization-Simulated Annealing).Firstly,PSO algorithm was used for global search to quickly find the initial solution.Then,GA optimization selection and crossover operations were used to enhance population diversity.Then,SA algorithm was employed for local search to further improve the solution quality.Experimental results showed that this method achieves better results in terms of job completion time,energy consumption,and machine load distribution.Compared to single algorithms,PGA-PSO-SA hybrid algorithm can more effectively find the global optimal solution,enhancing the overall performance of the scheduling scheme.The research results provides new ideas and methods for scheduling optimization in printing workshops.
基金supported by the National Natural Science Foundation of China(72022018,72091210)Youth Innovation Promotion Association,Chinese Academy of Sciences(2021454).
文摘With the maturation of emerging information technologies(Internet of Things,cloud computing,and big data),distributed manufacturing has emerged as an important model for future manufacturing.3D printing,with its integrated molding and design freedom,is a powerful catalyst for distributed manufacturing.This paper investigates the integrated production and transportation scheduling problem in distributed 3D printing.To solve this problem,we decompose the original problem into three sub-problems and design a multilevel optimization algorithm.We employ a genetic algorithm in the outer-level optimization to determine the optimal allocation of parts to machines.In the inner-level optimization,we utilize a simulated annealing algorithm to tackle the vehicle routing problem during the transportation stage followed by a local search algorithm to address the scheduling problem encountered during the production stage.Our algorithm is validated using real data from a 3D printing company,and the results show that our algorithm can obtain solutions that are the same as or better than those of Gurobi in a reasonable time for small-sized instances.Additionally,three types of initial methods are tested on large-sized instances to verify the efficiency of the proposed algorithm,and some interesting insights are also revealed and discussed.
文摘Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investigate the integrated scheduling of communication,sensing,and control for UAV-aided FSO communication systems.Initially,a sensing-control model is established via the control theory.Moreover,an FSO communication channel model is established by considering the effects of atmospheric loss,atmospheric turbulence,geometrical loss,and angle-of-arrival fluctuation.Then,the relationship between the motion control of the UAV and radial displacement is obtained to link the control aspect and communication aspect.Assuming that the base station has instantaneous channel state information(CSI)or statistical CSI,the thresholds of the sensing-control pattern activation are designed,respectively.Finally,an integrated scheduling scheme for performing communication,sensing,and control is proposed.Numerical results indicate that,compared with conventional time-triggered scheme,the proposed integrated scheduling scheme obtains comparable communication and control performance,but reduces the sensing consumed power by 52.46%.
基金the financial support of the National Natural Science Foundation of China(12102077,12161076)the Natural Science and Technology Program of Liaoning Province(2023-BS-061).
文摘Recovery is a crucial supporting process for carrier aircraft,where a reasonable landing scheduling is expected to guide the fleet landing safely and quickly.Currently,there is little research on this topic,and most of it neglects potential influence factors,leaving the corresponding supporting efficiency questionable.In this paper,we study the landing scheduling problem for carrier aircraft considering the effects of bolting and aerial refueling.Based on the analysis of recovery mode involving the above factors,two types of primary constraints(i.e.,fuel constraint and wake interval constraint)are first described.Then,taking the landing sequencing as decision variables,a combinatorial optimization model with a compound objective function is formulated.Aiming at an efficient solution,an improved firefly algorithm is designed by integrating multiple evolutionary operators.In addition,a dynamic replanning mechanism is introduced to deal with special situations(i.e.,the occurrence of bolting and fuel shortage),where the high efficiency of the designed algorithm facilitates the online scheduling adjustment within seconds.Finally,numerical simulations with sufficient and insufficient fuel cases are both carried out,highlighting the necessity to consider bolting and aerial refueling during the planning procedure.Simulation results reveal that a higher bolting probability,as well as extra aerial refueling operations caused by fuel shortage,will lead to longer recovery complete time.Meanwhile,due to the strong optimum-seeking capability and solution efficiency of the improved algorithm,adaptive scheduling can be generated within milliseconds to deal with special situations,significantly improving the safety and efficiency of the recovery process.An animation is accessible at bilibili.com/video/BV1QprKY2EwD.
基金supported by the National Natural Science Foundation of China(6177109562031007).
文摘The dwell scheduling problem for a multifunctional radar system is led to the formation of corresponding optimiza-tion problem.In order to solve the resulting optimization prob-lem,the dwell scheduling process in a scheduling interval(SI)is formulated as a Markov decision process(MDP),where the state,action,and reward are specified for this dwell scheduling problem.Specially,the action is defined as scheduling the task on the left side,right side or in the middle of the radar idle time-line,which reduces the action space effectively and accelerates the convergence of the training.Through the above process,a model-free reinforcement learning framework is established.Then,an adaptive dwell scheduling method based on Q-learn-ing is proposed,where the converged Q value table after train-ing is utilized to instruct the scheduling process.Simulation results demonstrate that compared with existing dwell schedul-ing algorithms,the proposed one can achieve better scheduling performance considering the urgency criterion,the importance criterion and the desired execution time criterion comprehen-sively.The average running time shows the proposed algorithm has real-time performance.
基金supported by the National Natural Science Foundation of China(52175475).
文摘In an aircraft final assembly line(AFAL),the rational scheduling of assembly workers to complete tasks in an orderly manner is crucial for enhancing production efficiency.This paper addresses the multi-skilled worker scheduling problem in the AFAL,where the processing time of each task varies due to the assigned workers’skill levels,referred to as variable duration.The objective is to minimize the makespan,i.e.,the total time required for all workers to complete all tasks.A mixed integer linear programming model is formulated under complex constraints including assembly precedence relations,skill requirements,worker skill capabilities,and workspace capacities.To solve the model effectively,a multi-pass priority rule-based heuristic(MPRH)algorithm is proposed.This algorithm integrates 14 activity priority rules and nine worker priority rules with worker weights.Extensive experiments iteratively the best-performing priority rules,and the most effective rule subsets are integrated through a lightweight multi-pass mechanism to enhance its efficiency.The computational results demonstrate that the MPRH can find high-quality solutions effectively within very short central processing unit central processing unit(CPU)time compared to GUROBI.A case study based on real data obtained from an AFAL confirms the necessity and the feasibility of the approach in practical applications.Sensitivity analyses provide valuable insights to real production scenarios.
基金supported by funding from the National Natural Science Foundation of China(Grant Nos.62325602,62406292,62302459,62406293,and 62036010)。
文摘The efficiency of carrier-based aircraft support operation scheduling critically impacts aircraft carrier operational effectiveness by determining sortie generation rates,yet faces significant challenges in complex deck environments characterized by resource coupling,dynamic constraints,and highdimensional state-action spaces.Traditional optimization algorithms and vanilla reinforcement learning(RL)struggle with computational inefficiency,sparse rewards,and adaptability to dynamic scenarios,while human expert systems are constrained by the quality of expert knowledge,and poor expert guidance may even have a negative impact.To address these limitations,this paper proposes a human experience-guided actor-critic reinforcement learning framework that synergizes domain expertise with adaptive learning.First,a dynamic Markov decision process(MDP)model is developed to rigorously simulate carrier deck operations,explicitly encoding constraints on positions,resources,and collision avoidance.Building upon this foundation,a human experience database is constructed to enable real-time pattern-matching-based intervention during agent-environment interactions,dynamically correcting wrong actions to avoid catastrophic states while refining exploration efficiency.Finally,the policy and value network objectives are reshaped to incorporate human intent through hybrid reward functions and adaptive guidance weighting,ensuring balanced integration of expert knowledge with RL's exploration capabilities.Extensive simulations across three scenarios demonstrate superior performance compared to state-of-the-art methods and maintain robustness under suboptimal human guidance.These results validate the framework's ability to harmonize human expertise with adaptive learning,offering a practical solution for real-world carriers.
基金supported by the National Natural Science Foundation of China(6203100762371093).
文摘The multifunctional integration system(MFIS)is based on a common hardware platform that controls and regulates the system’s configurable parameters through software to meet dif-ferent operational requirements.Dwell scheduling is a key for the system to realize multifunction and maximize the resource uti-lization.In this paper,an adaptive dwell scheduling optimization model for MFIS which considers the aperture partition and joint radar communication(JRC)waveform is established.To solve the formulated optimization problem,JRC scheduling condi-tions are proposed,including time overlapping condition,beam direction condition and aperture condition.Meanwhile,an effec-tive mechanism to dynamically occupy and release the aperture resource is introduced,where the time-pointer will slide to the earliest ending time of all currently scheduled tasks so that the occupied aperture resource can be released timely.Based on them,an adaptive dwell scheduling algorithm for MFIS with aperture partition and JRC waveform is put forward.Simulation results demonstrate that the proposed algorithm has better com-prehensive scheduling performance than up-to-date algorithms in all considered metrics.
基金supported by the National Natural Science Foundation of China(61571149,62001139)the Initiation Fund for Postdoctoral Research in Heilongjiang Province(LBH-Q19098)the Natural Science Foundation of Heilongjiang Province(LH2020F0178).
文摘Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.
文摘在现代制造业迅速发展的背景下,车间调度问题在生产制造中起着重要作用,是确保生产流程顺畅、提高生产效率、降低生产成本的关键环节。为全面系统地分析我国车间调度领域的发展状况和研究动态,通过检索中国知网(CNKI)和Web of Science(WOS)数据库,获得2000年~2023年以车间调度为主题的中英文文献共14298篇,再运用CiteSpace 6.3 R1软件对文献进行可视化分析,分别从发文量、研究作者、国家地区、机构、关键词共现和被引文献等不同角度审视研究主题结构,结合可视化知识图谱对车间调度领域的现状、趋势和热点进行分析。分析结果表明,由于车间调度在实际应用中的复杂性和多样性,车间调度领域的研究热点也呈现多元化的趋势,目前该领域的研究主要围绕动态与实时调度的优化、多目标优化的平衡追求和智能化算法的应用等方面展开,智能化、绿色化、可持续化和跨学科融合等是未来的发展方向。
