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dc.contributorInternational FAIM Conference 24th : 2014 : San Antonio, Texas
dc.contributorUniversity of Texas at San Antonio. Center for Advanced Manufacturing and Lean Systems
dc.contributor.authorGlorieux, Emile
dc.contributor.authorSvensson, Bo
dc.contributor.authorDanielsson, Fredrik
dc.contributor.authorLennartson, Bengt
dc.date.accessioned2022-07-11T17:35:45Z
dc.date.available2022-07-11T17:35:45Z
dc.date.issued2014
dc.identifier.otherhttp://dx.doi.org/10.14809/faim.2014.0909
dc.identifier.urihttps://hdl.handle.net/20.500.12588/1032
dc.descriptionPaper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio
dc.descriptionIncludes bibliographical references
dc.description.abstractSimulation-based optimisation often considers computationally expensive problems. Successfully optimising such large scale and complex problems within a practical timeframe is a challenging task. Optimisation techniques to fulfil this need to be developed. A technique to address this involves decomposing the considered problem into smaller subproblems. These subproblems are then optimised separately. In this paper, an efficient algorithm for simulation-based optimisation is proposed. The proposed algorithm extends the cooperative coevolutionary algorithm, which optimises subproblems separately. To optimise the subproblems, the proposed algorithm enables using a deterministic algorithm, next to stochastic genetic algorithms, getting the flexibility of using either type. It also includes a constructive heuristic that creates good initial feasible solutions to reduce the number of fitness calculations. The extension enables solving complex, computationally expensive problems efficiently. The proposed algorithm has been applied on automated sheet metal press lines from the automotive industry. This is a highly complex optimisation problem due to its non-linearity and high dimensionality. The optimisation problem is to find control parameters that maximises the line's production rate. These control parameters determine velocities, time constants, and cam values for critical interactions between components. A simulation model is used for the fitness calculation during the optimisation. The results show that the proposed algorithm manages to solve the press line optimisation problem efficiently. This is a step forward in press line optimisation since this is to the authors' knowledge the first time a press line has been optimised efficiently in this way.
dc.language.isoen_US
dc.publisherDEStech Publications, Inc.
dc.relation.ispartofseriesProceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing
dc.subjectMathematical optimization
dc.subjectAlgorithms
dc.subjectSheet-metal work--Automatic control
dc.subjectAutomobiles--Design and construction--Simulation methods
dc.subjectAssembly-line methods
dc.titleA constructive cooperative coevolutionary algorithm applied to press line optimisation
dc.typeArticle


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