Unmanned Aerial Vehicles(UAVs)cooperative multi-task system has become the research focus in recent years.However,the existing network frameworks of UAVs are not flexible and efficient enough to deal with the complex ...Unmanned Aerial Vehicles(UAVs)cooperative multi-task system has become the research focus in recent years.However,the existing network frameworks of UAVs are not flexible and efficient enough to deal with the complex multi-task scheduling,because they are not able to perceive the different features.In this paper,a novel cooperated UAVs network framework for multi-task scheduling is proposed.It is a three-layer network including a core layer,an aggregation layer and an execution layer,which enhances the efficiency of multi-task distribution,aggregation and transmission.Furthermore,an Aggre Gate Flow(AGFlow)based scheduler is dedicatedly designed to maximize the task completion rate,whose key point is to aggregate flows belonging to one task during the multi-task transmission of UAVs network and to allocate priority by calculating the urgency-level of each AGFlow.Simulation results demonstrate that,compared with that of state-of-the-art scheduler,the average task completion rate of AGFlow based scheduler is raised by 0.278.展开更多
This paper explains the goal conflict between schedule and quality in construction projects,including how schedule compression can lead to quality risks,and how quality control may cause delays.It analyzes the interna...This paper explains the goal conflict between schedule and quality in construction projects,including how schedule compression can lead to quality risks,and how quality control may cause delays.It analyzes the internal logic of collaborative management and influencing factors such as construction plans.The paper also introduces collaborative management methods,such as establishing a responsibility traceability system based on Work Breakdown Structure(WBS),and emphasizes the role of intelligent construction technologies and their future development directions.展开更多
Variable Cycle Engine(VCE)serves as the core system in achieving future advanced fighters with cross-generational performance and mission versatility.However,the resultant complex configuration and strong coupling of ...Variable Cycle Engine(VCE)serves as the core system in achieving future advanced fighters with cross-generational performance and mission versatility.However,the resultant complex configuration and strong coupling of control parameters present significant challenges in designing acceleration and deceleration control schedules.To thoroughly explore the performance potential of engine,a global integration design method for acceleration and deceleration control schedule based on inner and outer loop optimization is proposed.The outer loop optimization module employs Integrated Surrogate-Assisted Co-Differential Evolutionary(ISACDE)algorithm to optimize the variable geometry adjustment laws based on B-spline curve,and the inner loop optimization module adopts the fixed-state method to design the open-loop fuel–air ratio control schedules,which are aimed at minimizing the acceleration and deceleration time under multiple constraints.Simulation results demonstrate that the proposed global integration design method not only furthest shortens the acceleration and deceleration time,but also effectively safeguards the engine from overlimit.展开更多
A collaborative optimization method for the sintering schedule of ternary cathode materials was proposed under microscopic coupling constraints.An oxygen vacancy concentration prediction model based on microscopic the...A collaborative optimization method for the sintering schedule of ternary cathode materials was proposed under microscopic coupling constraints.An oxygen vacancy concentration prediction model based on microscopic thermodynamics and a growth kinetics model based on neural networks were established.Then,optimization formulations were constructed in three stages to obtain an optimal sintering schedule that minimized energy consumption for different requirements.Simulations demonstrate that the models accurately predict the oxygen vacancy concentrations and grain size,with root mean square errors of approximately 5%and 3%,respectively.Furthermore,the optimized sintering schedule not only meets the required quality standards but also reduces sintering time by 12.31%and keeping temperature by 11.96%.This research provides new insights and methods for the preparation of ternary cathode materials.展开更多
Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in oper...Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in operator workloads and significantly increase the complexity of scheduling.To address this challenge,this study investigates the Aircraft Pulsating Assembly Line Scheduling Problem(APALSP)under skilled operator allocation,with the objective of minimizing assembly completion time.A mathematical model considering skilled operator allocation is developed,and a Q-Learning improved Particle Swarm Optimization algorithm(QLPSO)is proposed.In the algorithm design,a reverse scheduling strategy is adopted to effectively manage large-scale precedence constraints.Moreover,a reverse sequence encoding method is introduced to generate operation sequences,while a time decoding mechanism is employed to determine completion times.The problem is further reformulated as a Markov Decision Process(MDP)with explicitly defined state and action spaces.Within QLPSO,the Q-learning mechanism adaptively adjusts inertia weights and learning factors,thereby achieving a balance between exploration capability and convergence performance.To validate the effectiveness of the proposed approach,extensive computational experiments are conducted on benchmark instances of different scales,including small,medium,large,and ultra-large cases.The results demonstrate that QLPSO consistently delivers stable and high-quality solutions across all scenarios.In ultra-large-scale instances,it improves the best solution by 25.2%compared with the Genetic Algorithm(GA)and enhances the average solution by 16.9%over the Q-learning algorithm,showing clear advantages over the comparative methods.These findings not only confirm the effectiveness of the proposed algorithm but also provide valuable theoretical references and practical guidance for the intelligent scheduling optimization of aircraft pulsating assembly lines.展开更多
The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system perf...The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.展开更多
To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was establis...To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.展开更多
Servicing is applied periodically in practice with the aim of restoring the system state and prolonging the lifetime. It is generally seen as an imperfect maintenance action which has a chief influence on the maintena...Servicing is applied periodically in practice with the aim of restoring the system state and prolonging the lifetime. It is generally seen as an imperfect maintenance action which has a chief influence on the maintenance strategy. In order to model the maintenance effect of servicing, this study analyzes the deterioration characteristics of system under scheduled servicing. And then the deterioration model is established from the failure mechanism by compound Poisson process. On the basis of the system damage value and failure mechanism, the failure rate refresh factor is proposed to describe the maintenance effect of servicing. A maintenance strategy is developed which combines the benefits of scheduled servicing and preventive maintenance. Then the optimization model is given to determine the optimal servicing period and preventive maintenance time, with an objective to minimize the system expected life-cycle cost per unit time and a constraint on system survival probability for the duration of mission time. Subject to mission time, it can control the ability of accomplishing the mission at any time so as to ensure the high dependability. An example of water pump rotor relating to scheduled servicing is introduced to illustrate the failure rate refresh factor and the proposed maintenance strategy. Compared with traditional methods, the numerical results show that the failure rate refresh factor can describe the maintenance effect of servicing more intuitively and objectively. It also demonstrates that this maintenance strategy can prolong the lifetime, reduce the total lifetime maintenance cost and guarantee the dependability of system.展开更多
A genetic algorithm-based optimization was used for 1 370 mm tandem cold rolling schedule,in which the press rates were coded and operated.The superiority individual is reserved in every generation.Analysis and compar...A genetic algorithm-based optimization was used for 1 370 mm tandem cold rolling schedule,in which the press rates were coded and operated.The superiority individual is reserved in every generation.Analysis and comparison of optimized schedule with the existing schedule were offered.It is seen that the performance of the optimal rolling schedule is satisfactory and promising.展开更多
Rolling schedule not only determines the rolling process to be going smoothly, but also affects the shape accuracy and structure properties of finished strip. In order to gain good strip crown and flatness, the calcul...Rolling schedule not only determines the rolling process to be going smoothly, but also affects the shape accuracy and structure properties of finished strip. In order to gain good strip crown and flatness, the calculation formulas of the most suitable rolling force and bending force are deduced. By taking relatively equal load of rolling power and good shape as objective functions, the optimization mathematical models of finish rolling schedule are established. By contrast, the rolling schedules after optimization can improve the rolling mill working status and ensure the strip crown and flatness to be good. At the same time, the setting value of bending force is improved and this leaves more space for on-line shape control.展开更多
Considering the multivariable, strong-coupled, and nonlinear features of tandem cold rolling mill, a mathematical model of multi-objective optimization was built to facilitate the design of new systems aiming at equat...Considering the multivariable, strong-coupled, and nonlinear features of tandem cold rolling mill, a mathematical model of multi-objective optimization was built to facilitate the design of new systems aiming at equating the relative load, preventing slip, and obtaining best shape. BP （back propagation） neural network based on Levenberg- Marquardt algorithm was used for predicting the rolling force. The multi-objective fuzzy theory and method were introduced during the optimization. With an example of 1 370 mm tandem cold rolling mill, the rolling schedule of the common rolling, the single-objective optimization design, and the multi-objective fuzzy optimization design were compared with each other. The results generated from the case study showed that the proposed approach could significantly decreased the values of three objective functions simultaneously and the performance of the optimal rolling schedule was satisfactory and promising. Moreover, the capability and usefulness of fuzzy application in tandem cold rolling schedule were clearly demonstrated.展开更多
Active schedule is one of the most basic and popular concepts in production scheduling research. For identical parallel machine scheduling with jobs＇ dynamic arrivals, the tight performance bounds of active schedules...Active schedule is one of the most basic and popular concepts in production scheduling research. For identical parallel machine scheduling with jobs＇ dynamic arrivals, the tight performance bounds of active schedules under the measurement of four popular objectives are respectively given in this paper. Similar analysis method and conclusions can be generalized to static identical parallel machine and single machine scheduling problem.展开更多
From the point of view of saving energy, a new shift schedule and auto-controlling strategy for automatic transmission are proposed. In order to verify this shift schedule, a simulation program using a software packag...From the point of view of saving energy, a new shift schedule and auto-controlling strategy for automatic transmission are proposed. In order to verify this shift schedule, a simulation program using a software package of Matlab/ Simulink is developed. The simulation results show the shift schedule is correct. This shift schedule has enriched the theory of vehicle automatic maneuvering and will improve the efficiency of hydrodynanic drive system of the vehicle.展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.61762030 and 61971148)the Guangxi Natural Science Foundation,China(Nos.2019GXNSFFA245007,2018GXNSFDA281013 and 2016GXNSFGA380002)Key Science and Technology Project of Guangxi,China(Nos.AA18242021,ZY19183005,2017AB13014,2018JJA70209,AA19110044 and AA19110046)。
文摘Unmanned Aerial Vehicles(UAVs)cooperative multi-task system has become the research focus in recent years.However,the existing network frameworks of UAVs are not flexible and efficient enough to deal with the complex multi-task scheduling,because they are not able to perceive the different features.In this paper,a novel cooperated UAVs network framework for multi-task scheduling is proposed.It is a three-layer network including a core layer,an aggregation layer and an execution layer,which enhances the efficiency of multi-task distribution,aggregation and transmission.Furthermore,an Aggre Gate Flow(AGFlow)based scheduler is dedicatedly designed to maximize the task completion rate,whose key point is to aggregate flows belonging to one task during the multi-task transmission of UAVs network and to allocate priority by calculating the urgency-level of each AGFlow.Simulation results demonstrate that,compared with that of state-of-the-art scheduler,the average task completion rate of AGFlow based scheduler is raised by 0.278.
文摘This paper explains the goal conflict between schedule and quality in construction projects,including how schedule compression can lead to quality risks,and how quality control may cause delays.It analyzes the internal logic of collaborative management and influencing factors such as construction plans.The paper also introduces collaborative management methods,such as establishing a responsibility traceability system based on Work Breakdown Structure(WBS),and emphasizes the role of intelligent construction technologies and their future development directions.
基金supported by the Basic Research on Dynamic Real-time Modeling and Onboard Adaptive Modeling of Aero Engine,China(No.QZPY202308)。
文摘Variable Cycle Engine(VCE)serves as the core system in achieving future advanced fighters with cross-generational performance and mission versatility.However,the resultant complex configuration and strong coupling of control parameters present significant challenges in designing acceleration and deceleration control schedules.To thoroughly explore the performance potential of engine,a global integration design method for acceleration and deceleration control schedule based on inner and outer loop optimization is proposed.The outer loop optimization module employs Integrated Surrogate-Assisted Co-Differential Evolutionary(ISACDE)algorithm to optimize the variable geometry adjustment laws based on B-spline curve,and the inner loop optimization module adopts the fixed-state method to design the open-loop fuel–air ratio control schedules,which are aimed at minimizing the acceleration and deceleration time under multiple constraints.Simulation results demonstrate that the proposed global integration design method not only furthest shortens the acceleration and deceleration time,but also effectively safeguards the engine from overlimit.
基金supported by the National Natural Science Foundation of China(No.62033014)the Application Projects of Integrated Standardization and New Paradigm for Intelligent Manufacturing from the Ministry of Industry and Information Technology of China in 2016,and the Fundamental Research Funds for the Central Universities of Central South University,China(No.2021zzts0700).
文摘A collaborative optimization method for the sintering schedule of ternary cathode materials was proposed under microscopic coupling constraints.An oxygen vacancy concentration prediction model based on microscopic thermodynamics and a growth kinetics model based on neural networks were established.Then,optimization formulations were constructed in three stages to obtain an optimal sintering schedule that minimized energy consumption for different requirements.Simulations demonstrate that the models accurately predict the oxygen vacancy concentrations and grain size,with root mean square errors of approximately 5%and 3%,respectively.Furthermore,the optimized sintering schedule not only meets the required quality standards but also reduces sintering time by 12.31%and keeping temperature by 11.96%.This research provides new insights and methods for the preparation of ternary cathode materials.
基金supported by the National Natural Science Foundation of China(Grant No.52475543)Natural Science Foundation of Henan(Grant No.252300421101)+1 种基金Henan Province University Science and Technology Innovation Talent Support Plan(Grant No.24HASTIT048)Science and Technology Innovation Team Project of Zhengzhou University of Light Industry(Grant No.23XNKJTD0101).
文摘Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in operator workloads and significantly increase the complexity of scheduling.To address this challenge,this study investigates the Aircraft Pulsating Assembly Line Scheduling Problem(APALSP)under skilled operator allocation,with the objective of minimizing assembly completion time.A mathematical model considering skilled operator allocation is developed,and a Q-Learning improved Particle Swarm Optimization algorithm(QLPSO)is proposed.In the algorithm design,a reverse scheduling strategy is adopted to effectively manage large-scale precedence constraints.Moreover,a reverse sequence encoding method is introduced to generate operation sequences,while a time decoding mechanism is employed to determine completion times.The problem is further reformulated as a Markov Decision Process(MDP)with explicitly defined state and action spaces.Within QLPSO,the Q-learning mechanism adaptively adjusts inertia weights and learning factors,thereby achieving a balance between exploration capability and convergence performance.To validate the effectiveness of the proposed approach,extensive computational experiments are conducted on benchmark instances of different scales,including small,medium,large,and ultra-large cases.The results demonstrate that QLPSO consistently delivers stable and high-quality solutions across all scenarios.In ultra-large-scale instances,it improves the best solution by 25.2%compared with the Genetic Algorithm(GA)and enhances the average solution by 16.9%over the Q-learning algorithm,showing clear advantages over the comparative methods.These findings not only confirm the effectiveness of the proposed algorithm but also provide valuable theoretical references and practical guidance for the intelligent scheduling optimization of aircraft pulsating assembly lines.
文摘The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.
基金Project(2012B091100444)supported by the Production,Education and Research Cooperative Program of Guangdong Province and Ministry of Education,ChinaProject(2013ZM0091)supported by Fundamental Research Funds for the Central Universities of China
文摘To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.
基金supported by the National Defence Preresearch Foundation of China(Nos.51327020105,51304010206)
文摘Servicing is applied periodically in practice with the aim of restoring the system state and prolonging the lifetime. It is generally seen as an imperfect maintenance action which has a chief influence on the maintenance strategy. In order to model the maintenance effect of servicing, this study analyzes the deterioration characteristics of system under scheduled servicing. And then the deterioration model is established from the failure mechanism by compound Poisson process. On the basis of the system damage value and failure mechanism, the failure rate refresh factor is proposed to describe the maintenance effect of servicing. A maintenance strategy is developed which combines the benefits of scheduled servicing and preventive maintenance. Then the optimization model is given to determine the optimal servicing period and preventive maintenance time, with an objective to minimize the system expected life-cycle cost per unit time and a constraint on system survival probability for the duration of mission time. Subject to mission time, it can control the ability of accomplishing the mission at any time so as to ensure the high dependability. An example of water pump rotor relating to scheduled servicing is introduced to illustrate the failure rate refresh factor and the proposed maintenance strategy. Compared with traditional methods, the numerical results show that the failure rate refresh factor can describe the maintenance effect of servicing more intuitively and objectively. It also demonstrates that this maintenance strategy can prolong the lifetime, reduce the total lifetime maintenance cost and guarantee the dependability of system.
文摘A genetic algorithm-based optimization was used for 1 370 mm tandem cold rolling schedule,in which the press rates were coded and operated.The superiority individual is reserved in every generation.Analysis and comparison of optimized schedule with the existing schedule were offered.It is seen that the performance of the optimal rolling schedule is satisfactory and promising.
基金Item Sponsored by National Natural Science Foundation of China(51075353)Hebei Natural Science Foundation of China(E2010001208)
文摘Rolling schedule not only determines the rolling process to be going smoothly, but also affects the shape accuracy and structure properties of finished strip. In order to gain good strip crown and flatness, the calculation formulas of the most suitable rolling force and bending force are deduced. By taking relatively equal load of rolling power and good shape as objective functions, the optimization mathematical models of finish rolling schedule are established. By contrast, the rolling schedules after optimization can improve the rolling mill working status and ensure the strip crown and flatness to be good. At the same time, the setting value of bending force is improved and this leaves more space for on-line shape control.
基金Item Sponsored by National Key Technology Research and Development Programin 11th Five-Year Plan of China(2007BAF02B12)
文摘Considering the multivariable, strong-coupled, and nonlinear features of tandem cold rolling mill, a mathematical model of multi-objective optimization was built to facilitate the design of new systems aiming at equating the relative load, preventing slip, and obtaining best shape. BP （back propagation） neural network based on Levenberg- Marquardt algorithm was used for predicting the rolling force. The multi-objective fuzzy theory and method were introduced during the optimization. With an example of 1 370 mm tandem cold rolling mill, the rolling schedule of the common rolling, the single-objective optimization design, and the multi-objective fuzzy optimization design were compared with each other. The results generated from the case study showed that the proposed approach could significantly decreased the values of three objective functions simultaneously and the performance of the optimal rolling schedule was satisfactory and promising. Moreover, the capability and usefulness of fuzzy application in tandem cold rolling schedule were clearly demonstrated.
基金This work was supported by the National Natural Science Foundation of China (No. 60474002, 60504026)Shanghai Development Foundation forScience and Technology (No. 04DZ11008)
文摘Active schedule is one of the most basic and popular concepts in production scheduling research. For identical parallel machine scheduling with jobs＇ dynamic arrivals, the tight performance bounds of active schedules under the measurement of four popular objectives are respectively given in this paper. Similar analysis method and conclusions can be generalized to static identical parallel machine and single machine scheduling problem.
基金This project is supported by National Natural Science Foundation of China( No.59705005) and Backbone Teacher Foundation of Minis
文摘From the point of view of saving energy, a new shift schedule and auto-controlling strategy for automatic transmission are proposed. In order to verify this shift schedule, a simulation program using a software package of Matlab/ Simulink is developed. The simulation results show the shift schedule is correct. This shift schedule has enriched the theory of vehicle automatic maneuvering and will improve the efficiency of hydrodynanic drive system of the vehicle.
