Using genetic programming and simulation to learn how to dynamically adapt the number of cards in reactive pull systems. Issue 6 (15th April 2015)

• Record Type: Journal Article
• Authors: Belisário, Lorena Silva; Pierreval, Henri
• Is Part Of: Expert systems with applications. Volume 42:Issue 6(2015)
• Page Start: 3129
• Page End: 3141
• Publication Date: 2015-04-15
• Subjects: Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2014.11.052
• Languages: English
• ISSNs: 0957-4174

Abstract

<abstract xml:lang="en" abstract-type="author-highlights" id="ab005"> <title id="st080">Highlights</title> <sec> <p id="sp0005"> <list id="l0005"> <list-item id="o0005"> <label></label> <p id="p0350">We show how to learn dynamically to adapt the number of cards in real time in token-based pull systems.</p> </list-item> <list-item id="o0010"> <label></label> <p id="p0355">We propose a Simulation-based Genetic Programming approach which does not need training sets.</p> </list-item> <list-item id="o0015"> <label></label> <p id="p0360">We illustrate how the approach can be implemented using Arena and μGP.</p> </list-item> <list-item id="o0020"> <label></label> <p id="p0365">A reactive ConWIP example show the efficiency of the approach and of the knowledge extracted.</p> </list-item> <list-item id="o0025"> <label></label> <p id="p0370">The resulting decision tree can be used online by production managers or for self-adaptation issues.</p> </list-item> </list> </p> </sec> </abstract>