Scheduling approach for aircraft assembly pulsation production lines with deep reinforcement learning and knowledge transfer
Aircraft assembly is a critical process in aircraft manufacturing.Scheduling the assembly pulsation production lines of aircraft assembly in a rational manner for cost reduction and efficiency improvement is an important scientific problem in the intelligent manufacturing field.However,the scenario of aircraft assembly lines is complex,with each assembly involving tens of thousands of operations,which poses new challenges for formally modeling and efficiently solving the aircraft assembly scheduling problem.Thereby,current industry practices heavily rely on manual scheduling through the expertise of human professionals.This paper aims to minimize human resource load and proposes two domain-specific techniques to address the scheduling problem of aircraft assembly pulsation lines.Firstly,the scheduling problem of aircraft assembly pulsation production lines is modeled as two Markov decision processes,and a bi-level reinforcement learning agent is used to make decisions on feasible scheduling solutions for aircraft assembly.Secondly,to tackle the problem of robustness deficiency in reinforcement learning decisions,a domain-knowledge transfer paradigm is proposed,whereas the problem-solving knowledge obtained via reinforcement learning is transferred to the constraint pruning process of the integer linear programming model,and the final scheduling solutions with excellent overall performance are attained through an integer programming solver.Experiments are conducted on real scheduling data from aircraft assembly pulsation production lines.Results demonstrate that the proposed scheduling method based on reinforcement learning and knowledge transfer can successfully scale up to scheduling the assembly pulsation production lines with a yield of nearly one hundred aircraft per year,a problem intractable for combinatorial optimization methods.The solving time of the proposed method is reduced to minutes,and the performance exhibits significant advantages compared to baseline methods.
aircraft assemblyintelligent schedulingcombinatorial optimizationreinforcement learningknowledge transfer
NETL
NSTL
万方数据
