{"id":1941,"date":"2021-07-14T09:30:00","date_gmt":"2021-07-14T07:30:00","guid":{"rendered":"http:\/\/cirpicme.org\/?page_id=1941"},"modified":"2021-07-13T18:57:01","modified_gmt":"2021-07-13T16:57:01","slug":"a-framework-for-automated-multiobjective-factory-layout-planning-using-reinforcement-learning","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/biological-transformation-sustainability\/a-framework-for-automated-multiobjective-factory-layout-planning-using-reinforcement-learning\/","title":{"rendered":"A framework for automated multiobjective factory layout planning using reinforcement learning"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>by <em>Matthias Klar, Pascal Langlotz, Jan C. Aurich<\/em><\/em> <em>(Germany)<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Abstract<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Layout planning is a central element of the factory planning process. Given its complexity, layout planning is often time consuming and involves creative processes. One possible way to deal with this complexity is the training of a machine learning algorithm, which enables to generate and optimize factory layouts. Consequently, this paper outlines a reinforcement learning based concept for automated layout planning. In particular, boundary conditions and objective functions are derived from the existing planning parameters of factory layouts. The presented approach will allow a multiobjective optimization of the layout and uses material flow and energy consumption as optimization criteria.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Keywords<\/strong>: Factory planning, Layout planning, Multiobjective optimization, Machine learning, Reinforcement learning<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Video presentation<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-video\"><video height=\"1080\" style=\"aspect-ratio: 1920 \/ 1080;\" width=\"1920\" controls src=\"http:\/\/cirpicme.org\/wp-content\/uploads\/2021\/06\/Matthias_Klar.mp4\"><\/video><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Presenting author<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-subtle-light-gray-background-color has-background\"><tbody><tr><td><\/td><td><\/td><td><\/td><\/tr><tr><td><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"150\" height=\"198\" class=\"wp-image-1765\" style=\"width: 150px;\" src=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2021\/06\/Matthias_Klar_Photo.jpg?resize=150%2C198\" alt=\"\" srcset=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2021\/06\/Matthias_Klar_Photo.jpg?w=205&amp;ssl=1 205w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2021\/06\/Matthias_Klar_Photo.jpg?resize=113%2C150&amp;ssl=1 113w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/><\/td><td><strong>Name:<\/strong><br><br><strong>Affiliation:<\/strong><br><br><strong>Email:<\/strong><\/td><td>Matthias Klar<br><br>Technische Universitaet Kaiserslautern, Germany<br><br>matthias.klar@mv.uni-kl.de<\/td><\/tr><tr><td><\/td><td><\/td><td><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>by Matthias Klar, Pascal Langlotz, Jan C. Aurich (Germany) Abstract Layout planning is a central element of the factory planning process. Given its complexity, layout planning is often time consuming and involves creative processes. One possible way to deal with this complexity is the training of a machine learning algorithm,&#8230;<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/cirpicme.org\/index.php\/biological-transformation-sustainability\/a-framework-for-automated-multiobjective-factory-layout-planning-using-reinforcement-learning\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":9,"featured_media":0,"parent":2360,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"nf_dc_page":"","om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-1941","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/1941","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/comments?post=1941"}],"version-history":[{"count":2,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/1941\/revisions"}],"predecessor-version":[{"id":2189,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/1941\/revisions\/2189"}],"up":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/2360"}],"wp:attachment":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/media?parent=1941"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}