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
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
Aircraft final assembly line(AFAL)involves thousands of processes that must be completed before delivery.However,the heavy reliance on manual labor in most assembly processes affects the quality and prolongs the deliv...Aircraft final assembly line(AFAL)involves thousands of processes that must be completed before delivery.However,the heavy reliance on manual labor in most assembly processes affects the quality and prolongs the delivery time.While the advent of artificial intelligence of things(AIoT)technologies has introduced advancements in certain AFAL scenarios,systematically enhancing the intelligence level of the AFAL and promoting the widespread deployment of artificial intelligence(AI)technologies remain significant challenges.To address these challenges,we propose the intelligent and collaborative aircraft assembly(ICAA)framework,which integrates AI technologies within a cloud-edge-terminal architecture.The ICAA framework is designed to support AI-enabled applications in the AFAL,with the goal of improving assembly efficiency at both individual and multiple process levels.We analyze specific demands across various assembly scenarios and introduce corresponding AI technologies to meet these demands.The three-tier ICAA framework consists of the assembly field,edge data platform,and assembly cloud platform,facilitating the collection of heterogeneous terminal data and the deployment of AI technologies.The framework enhances assembly efficiency by reducing reliance on manual labor for individual processes and fostering collaboration across multiple processes.We provide detailed descriptions of how AI functions at each level of the framework.Furthermore,we apply the ICAA framework to a real AFAL,focusing explicitly on the flight control system testing process.This practical implementation demonstrates the effectiveness of the framework in improving assembly efficiency and promoting the adoption of AIoT technologies.展开更多
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.
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 in part by the National Natural Science Foundation of China under Grants 92167205,61933009,62025305,and 62103268.
文摘Aircraft final assembly line(AFAL)involves thousands of processes that must be completed before delivery.However,the heavy reliance on manual labor in most assembly processes affects the quality and prolongs the delivery time.While the advent of artificial intelligence of things(AIoT)technologies has introduced advancements in certain AFAL scenarios,systematically enhancing the intelligence level of the AFAL and promoting the widespread deployment of artificial intelligence(AI)technologies remain significant challenges.To address these challenges,we propose the intelligent and collaborative aircraft assembly(ICAA)framework,which integrates AI technologies within a cloud-edge-terminal architecture.The ICAA framework is designed to support AI-enabled applications in the AFAL,with the goal of improving assembly efficiency at both individual and multiple process levels.We analyze specific demands across various assembly scenarios and introduce corresponding AI technologies to meet these demands.The three-tier ICAA framework consists of the assembly field,edge data platform,and assembly cloud platform,facilitating the collection of heterogeneous terminal data and the deployment of AI technologies.The framework enhances assembly efficiency by reducing reliance on manual labor for individual processes and fostering collaboration across multiple processes.We provide detailed descriptions of how AI functions at each level of the framework.Furthermore,we apply the ICAA framework to a real AFAL,focusing explicitly on the flight control system testing process.This practical implementation demonstrates the effectiveness of the framework in improving assembly efficiency and promoting the adoption of AIoT technologies.
