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Research On Resource Allocation And Processing Scheduling Of Ship Body Assembly Welding Workshop

Posted on:2019-04-06Degree:MasterType:Thesis
Country:ChinaCandidate:Y Y GuoFull Text:PDF
GTID:2392330548993107Subject:Mechanical engineering
Abstract/Summary:PDF Full Text Request
In recent years,practical production and processing problems tend to be solved by means of intellectualization and information with the rapid development of China's manufacturing industry.This paper selects hull construction as the research background,and analyzes.the rational allocation of constrained resources and the uncertain time processing schedul.Several key areas,such as resource scheduling,hull plane and hull surface section welding workshop,are combined.Intelligent optimization method is employed to solve optimization of multiobjective production scheduling.Firstly,the performance of the flexible mixed flow assembly line in the scheduling of surface section shop is analyzed.Aiming at flexible shop scheduling,a multi-objective MOGV algorithm based on Pareto is proposed And verifiedSecondly,the uncertainty factors in the fabrication process of the hull surface section welding workshop are analyzed.An event-driven rescheduling strategy is proposed.Numerical experiments and examples are carried out for three typical uncertain factors in the production process of curved section workshop.Thirdly,In order to study the allocation of resources for ships,a resource constrained project scheduling model is proposed from the viewpoint of robustness.The resource constrained project scheduling problem(RCPSP)is optimized.The effect of each parameter on resource scheduling is analyzed with examples.A robust stochastic resource constrained project scheduling(SRCPSP)model is formed and tabu search algorithm is designed to verify the effectiveness of the proposed resource scheduling algorithm.Finally,the entire simulation experiment platform is completed on Visual Basic 6.0 Visual interface.
Keywords/Search Tags:Mixed flow welding manufacturing system, Production scheduling, Resource allocation, Pareto optimal solution, Improved MOGV algorithm, Heavy scheduling
PDF Full Text Request
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