Manufacturing service composition of the supply side and scheduling of the demand side are two important components of Cloud Manufacturing,which directly affect the quality of Cloud Manufacturing services.However,the ...Manufacturing service composition of the supply side and scheduling of the demand side are two important components of Cloud Manufacturing,which directly affect the quality of Cloud Manufacturing services.However,the previous studies on the two components are carried out independently and thus ignoring the internal relations and mutual constraints.Considering the two components on both sides of the supply and the demand of Cloud Manufacturing services at the same time,a Bilateral Collaborative Optimization Model of Cloud Manufacturing(BCOM-CMfg)is constructed in this paper.In BCOM-CMfg,to solve the manufacturing service scheduling problem on the supply side,a new efficient manufacturing service scheduling strategy is proposed.Then,as the input of the service composition problem on the demand side,the scheduling strategy is used to build the BCOM-CMfg.Furthermore,the Cooperation Level(CPL)between services is added as an evaluation index in BCOM-CMfg,which reveals the importance of the relationship between services.To improve the quality of manufacturing services more comprehensively.Finally,a Self-adaptive Multi-objective Pigeon-inspired Optimization algorithm(S-MOPIO)is proposed to solve the BCOM-CMfg.Simulation results show that the BCOM-CMfg model has advantages in reliability and cost and S-MOPIO can solve BCOM-CMfg effectively.展开更多
Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities,resulting in improved rationality of process routes and high-quality and efficient production. Hence,the...Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities,resulting in improved rationality of process routes and high-quality and efficient production. Hence,the study of Integrated Process Planning and Scheduling (IPPS) has become a hot topic in the current production field. However,when performing this integrated optimization,the uncertainty of processing time is a realistic key point that cannot be neglected. Thus,this paper investigates a Fuzzy IPPS (FIPPS) problem to minimize the maximum fuzzy completion time. Compared with the conventional IPPS problem,FIPPS considers the fuzzy process time in the uncertain production environment,which is more practical and realistic. However,it is difficult to solve the FIPPS problem due to the complicated fuzzy calculating rules. To solve this problem,this paper formulates a novel fuzzy mathematical model based on the process network graph and proposes a MultiSwarm Collaborative Optimization Algorithm (MSCOA) with an integrated encoding method to improve the optimization. Different swarms evolve in various directions and collaborate in a certain number of iterations. Moreover,the critical path searching method is introduced according to the triangular fuzzy number,allowing for the calculation of rules to enhance the local searching ability of MSCOA. The numerical experiments extended from the well-known Kim benchmark are conducted to test the performance of the proposed MSCOA. Compared with other competitive algorithms,the results obtained by MSCOA show significant advantages,thus proving its effectiveness in solving the FIPPS problem.展开更多
Many human-machine collaborative support scheduling systems are used to aid human decision making by providing several optimal scheduling algorithms that do not take operator's attention into consideration.However...Many human-machine collaborative support scheduling systems are used to aid human decision making by providing several optimal scheduling algorithms that do not take operator's attention into consideration.However, the current systems should take advantage of the operator's attention to obtain the optimal solution.In this paper, we innovatively propose a human-machine collaborative support scheduling system of intelligence information from multi-UAVs based on eye-tracker. Firstly, the target recognition algorithm is applied to the images from the multiple unmanned aerial vehicles(multi-UAVs) to recognize the targets in the images. Then,the support system utilizes the eye tracker to gain the eye-gaze points which are intended to obtain the focused targets in the images. Finally, the heuristic scheduling algorithms take both the attributes of targets and the operator's attention into consideration to obtain the sequence of the images. As the processing time of the images collected by the multi-UAVs is uncertain, however the upper bounds and lower bounds of the processing time are known before. So the processing time of the images is modeled by the interval processing time. The objective of the scheduling problem is to minimize mean weighted completion time. This paper proposes some new polynomial time heuristic scheduling algorithms which firstly schedule the images including the focused targets. We conduct the scheduling experiments under six different distributions. The results indicate that the proposed algorithm is not sensitive to the different distributions of the processing time and has a negligible computational time. The absolute error of the best performing heuristic solution is only about 1%. Then, we incorporate the best performing heuristic algorithm into the human-machine collaborative support systems to verify the performance of the system.展开更多
The industrial Internet of Things(IIoT)is a new indus-trial idea that combines the latest information and communica-tion technologies with the industrial economy.In this paper,a cloud control structure is designed for...The industrial Internet of Things(IIoT)is a new indus-trial idea that combines the latest information and communica-tion technologies with the industrial economy.In this paper,a cloud control structure is designed for IIoT in cloud-edge envi-ronment with three modes of 5G.For 5G based IIoT,the time sensitive network(TSN)service is introduced in transmission network.A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration.For a transmission control protocol(TCP)model with nonlinear disturbance,time delay and uncertainties,a robust adaptive fuzzy sliding mode controller(AFSMC)is given with control rule parameters.IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows.IIoT workflow scheduling is a non-deterministic polynomial(NP)-hard problem in cloud-edge environment.An adaptive and non-local-convergent particle swarm optimization(ANCPSO)is designed with nonlinear inertia weight to avoid falling into local optimum,which can reduce the makespan and cost dramatically.Simulation and experiments demonstrate that ANCPSO has better performances than other classical algo-rithms.展开更多
基金This paper was supported in part by Natural Science Foundation of Jiangsu Province of China under Grant BK20191381in part by Jiangsu Planned Projects for Postdoctoral Research Funds under Grant 2019K223+2 种基金in part by the National Natural Science Foundation of China under Grant 61802208,Grant 61772286,Grant 61771258,and Grant 61701252in part by Project funded by China Postdoctoral Science Foundation Grant 2019M651923in part by Primary Research&Development Plan of Jiangsu Province under Grant BE2019742,and in part by NUPTSF under Grant NY220060,NY218035.
文摘Manufacturing service composition of the supply side and scheduling of the demand side are two important components of Cloud Manufacturing,which directly affect the quality of Cloud Manufacturing services.However,the previous studies on the two components are carried out independently and thus ignoring the internal relations and mutual constraints.Considering the two components on both sides of the supply and the demand of Cloud Manufacturing services at the same time,a Bilateral Collaborative Optimization Model of Cloud Manufacturing(BCOM-CMfg)is constructed in this paper.In BCOM-CMfg,to solve the manufacturing service scheduling problem on the supply side,a new efficient manufacturing service scheduling strategy is proposed.Then,as the input of the service composition problem on the demand side,the scheduling strategy is used to build the BCOM-CMfg.Furthermore,the Cooperation Level(CPL)between services is added as an evaluation index in BCOM-CMfg,which reveals the importance of the relationship between services.To improve the quality of manufacturing services more comprehensively.Finally,a Self-adaptive Multi-objective Pigeon-inspired Optimization algorithm(S-MOPIO)is proposed to solve the BCOM-CMfg.Simulation results show that the BCOM-CMfg model has advantages in reliability and cost and S-MOPIO can solve BCOM-CMfg effectively.
文摘Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities,resulting in improved rationality of process routes and high-quality and efficient production. Hence,the study of Integrated Process Planning and Scheduling (IPPS) has become a hot topic in the current production field. However,when performing this integrated optimization,the uncertainty of processing time is a realistic key point that cannot be neglected. Thus,this paper investigates a Fuzzy IPPS (FIPPS) problem to minimize the maximum fuzzy completion time. Compared with the conventional IPPS problem,FIPPS considers the fuzzy process time in the uncertain production environment,which is more practical and realistic. However,it is difficult to solve the FIPPS problem due to the complicated fuzzy calculating rules. To solve this problem,this paper formulates a novel fuzzy mathematical model based on the process network graph and proposes a MultiSwarm Collaborative Optimization Algorithm (MSCOA) with an integrated encoding method to improve the optimization. Different swarms evolve in various directions and collaborate in a certain number of iterations. Moreover,the critical path searching method is introduced according to the triangular fuzzy number,allowing for the calculation of rules to enhance the local searching ability of MSCOA. The numerical experiments extended from the well-known Kim benchmark are conducted to test the performance of the proposed MSCOA. Compared with other competitive algorithms,the results obtained by MSCOA show significant advantages,thus proving its effectiveness in solving the FIPPS problem.
基金the National Natural Science Foundation of China(No.61403410)
文摘Many human-machine collaborative support scheduling systems are used to aid human decision making by providing several optimal scheduling algorithms that do not take operator's attention into consideration.However, the current systems should take advantage of the operator's attention to obtain the optimal solution.In this paper, we innovatively propose a human-machine collaborative support scheduling system of intelligence information from multi-UAVs based on eye-tracker. Firstly, the target recognition algorithm is applied to the images from the multiple unmanned aerial vehicles(multi-UAVs) to recognize the targets in the images. Then,the support system utilizes the eye tracker to gain the eye-gaze points which are intended to obtain the focused targets in the images. Finally, the heuristic scheduling algorithms take both the attributes of targets and the operator's attention into consideration to obtain the sequence of the images. As the processing time of the images collected by the multi-UAVs is uncertain, however the upper bounds and lower bounds of the processing time are known before. So the processing time of the images is modeled by the interval processing time. The objective of the scheduling problem is to minimize mean weighted completion time. This paper proposes some new polynomial time heuristic scheduling algorithms which firstly schedule the images including the focused targets. We conduct the scheduling experiments under six different distributions. The results indicate that the proposed algorithm is not sensitive to the different distributions of the processing time and has a negligible computational time. The absolute error of the best performing heuristic solution is only about 1%. Then, we incorporate the best performing heuristic algorithm into the human-machine collaborative support systems to verify the performance of the system.
文摘The industrial Internet of Things(IIoT)is a new indus-trial idea that combines the latest information and communica-tion technologies with the industrial economy.In this paper,a cloud control structure is designed for IIoT in cloud-edge envi-ronment with three modes of 5G.For 5G based IIoT,the time sensitive network(TSN)service is introduced in transmission network.A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration.For a transmission control protocol(TCP)model with nonlinear disturbance,time delay and uncertainties,a robust adaptive fuzzy sliding mode controller(AFSMC)is given with control rule parameters.IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows.IIoT workflow scheduling is a non-deterministic polynomial(NP)-hard problem in cloud-edge environment.An adaptive and non-local-convergent particle swarm optimization(ANCPSO)is designed with nonlinear inertia weight to avoid falling into local optimum,which can reduce the makespan and cost dramatically.Simulation and experiments demonstrate that ANCPSO has better performances than other classical algo-rithms.
