{"id":3096,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3096"},"modified":"2022-07-26T13:06:54","modified_gmt":"2022-07-26T11:06:54","slug":"delivery-scheduling-in-meat-industry-using-reinforcement-learning","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/production-systems-networks-2\/delivery-scheduling-in-meat-industry-using-reinforcement-learning\/","title":{"rendered":"Delivery scheduling in meat industry using reinforcement learning"},"content":{"rendered":"\n

by Alica Hoepken, Hergen Pargmann, Harald Schallner, Andr\u00e9 Galczynski, Lennard Gerdes (Germany)<\/em><\/p>\n\n\n\n

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Abstract<\/strong><\/p>\n\n\n\n

The reduction of food waste in the meat industry is an ongoing challenge. Therefore, as part of the German research project \u00d3REIF – Resource Efficient, Economic and Intelligent Food Chain\u00d3, optimized planning algorithms were developed using artificial intelligence algorithms based on reinforcement learning. This approach proposes a solution for two daily planning tasks: (i) rescheduling the animal deliveries and (ii) balancing the animal qualities to sales orders qualities. A German food manufacturer has validated the prototype in terms of minimizing the amount of frozen meat not assigned to suitable customer requirements. Planning results show a significant reduction in food waste.<\/p>\n\n\n\n

Keywords<\/strong>: Manufacturing; Artificial neural networks; Reinforcement learning; Meat industry; Delivery scheduling; Waste reduction<\/p>\n\n\n\n

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Video presentation<\/strong><\/p>\n\n\n\n