Document Zbl 1542.90073

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A.</a>; <a href="/authors/berkhout.joost" title="Author Profile">Berkhout, J.</a>; <a href="/authors/heidergott.bernd-f" title="Author Profile">Heidergott, B.</a></div> <h2 class="title"> <strong>A neural network approach to performance analysis of tandem lines: the value of analytical knowledge.</strong> <i>(English)</i> <a class="label nowrap" href="/1542.90073">Zbl 1542.90073</a> </h2> <div class="source"> <a href="/serials/1149" title="Journal Profile">Comput. Oper. Res.</a> <a href="/?q=in%3A498872" title="Articles in this Issue">152, Article ID 106124, 15 p. (2023)</a>. </div> <div class="abstract">Summary: We develop a neural network (NN) metamodeller for efficiently approximating the throughput of different finite-buffer multi-server tandem lines (with varying service rates, number of stations, buffers, and servers). The resulting NN serves as a quick performance evaluation tool and is subsequently used for optimising the tandem-line layout. Specifically, we discuss the optimal allocation of buffer places and optimising service rates where service rates at machines are associated with costs. Our NN metamodelling approach is new as we integrate (biased) analytical queuing knowledge into the training data. The setup and training of the NN metamodeller are discussed in the paper. In particular, we discuss the integration of analytical results from queuing theory. Our numerical studies corroborate the common belief that adding analytical knowledge (in this case from queueing theory) significantly improves the ensuing NN’s prediction power. The framework developed in this paper demonstrates how analytical system knowledge can be integrated with data science in performance evaluation and optimisation. Our message is that even basic NNs, combined with formulae available from OR theory, offer invaluable improvements for building metamodellers in simulation optimisation.</div> <div class="clear"></div> <br> <div class="citations"><div class="clear"><a href="/?q=rf%3A7706547">Cited in <strong>2</strong> Documents</a></div></div> <div class="classification"> <h3>MSC:</h3> <table><tr> <td> <a class="mono" href="/classification/?q=cc%3A90B22" title="MSC2020">90B22</a> </td> <td class="space"> Queues and service in operations research </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A68T07" title="MSC2020">68T07</a> </td> <td class="space"> Artificial neural networks and deep learning </td> </tr></table> </div><div class="keywords"> <h3>Keywords:</h3><a href="/?q=ut%3Aneural+networks">neural networks</a>; <a href="/?q=ut%3Aanalytical+knowledge">analytical knowledge</a>; <a href="/?q=ut%3Aproduction+system">production system</a>; <a href="/?q=ut%3Atandem+lines">tandem lines</a>; <a href="/?q=ut%3Aqueues">queues</a>; <a href="/?q=ut%3Ametamodel">metamodel</a></div> <div class="software"> <h3>Software:</h3><a href="/software/8058">Scikit</a>; <a href="/software/22205">Adam</a></div>  <div class="modal fade" id="metadataModal" tabindex="-1" role="dialog" aria-labelledby="myModalLabel"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">Cite</h4> </div> <div class="modal-body"> <div class="form-group"> <label for="select-output" class="control-label">Format</label> <select id="select-output" class="form-control" aria-label="Select Metadata format"></select> </div> <div class="form-group"> <label for="metadataText" class="control-label">Result</label> <textarea class="form-control" id="metadataText" rows="10" style="min-width: 100%;max-width: 100%"></textarea> </div> <div id="metadata-alert" class="alert alert-danger" role="alert" style="display: none;">  </div> </div> <div class="modal-footer"> <button type="button" class="btn btn-primary" onclick="copyMetadata()">Copy to clipboard</button> <button type="button" class="btn btn-default" data-dismiss="modal">Close</button> </div> </div> </div> </div> <div class="functions clearfix"> <div class="function">  <a type="button" class="btn btn-default btn-xs pdf" data-toggle="modal" data-target="#metadataModal" data-itemtype="Zbl" data-itemname="Zbl 1542.90073" data-ciurl="/ci/07706547" data-biburl="/bibtex/07706547.bib" data-amsurl="/amsrefs/07706547.bib" data-xmlurl="/xml/07706547.xml" > Cite </a> <a class="btn btn-default btn-xs pdf" data-container="body" type="button" href="/pdf/07706547.pdf" title="Zbl 1542.90073 as PDF">Review PDF</a> </div> <div class="fulltexts"> <span class="fulltext">Full Text:</span> <a class="btn btn-default btn-xs" type="button" href="https://doi.org/10.1016/j.cor.2022.106124" aria-label="DOI for “A neural network approach to performance analysis of tandem lines: the value of analytical knowledge”" title="10.1016/j.cor.2022.106124">DOI</a> </div> <div class="sfx" style="float: right;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" title="Open Access License" class="cc-license-link no-new-tab-icon"> <img src="https://static.zbmath.org/contrib/img/cc/svg/icons/cc.svg" alt="Creative Commons CC license icon" class="cc-license-icon"> <img src="https://static.zbmath.org/contrib/img/cc/svg/icons/by.svg" alt="Creative Commons BY license icon" class="cc-license-icon"> </a> </div> </div> <div class="references"> <h3>References:</h3> <table><tr> <td>[1]</td> <td class="space">Alfa, A. 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