Time Series Forecasting-Based Job Assignment for Autonomous Mobile Robots to Minimize Rundown Losses in Dynamic Job Shop Facilities

Jong-Un Won, Jaewon Lee, Minjung Kang, Subin Oh, Haejoong Kim, Dae-Eun Lim

Abstract


Rail-based material handling systems such as overhead hoist transfer (OHT) and automated guided vehicle (AGV) have been used extensively in semiconductor manufacturing for the past three decades. However, to adapt to changes in the manufacturing environment, manufacturers are attempting to adopt autonomous mobile robot (AMR) systems. An AMR can respond immediately to layout changes such as facility expansion and movement because it does not require the installation of a rail; however, it is less responsive to transportation requests compared with OHT systems. In particular, the task allocation for the robot imposes greater effect on the performance of manufacturing logistics transport because the robot travels relatively slowly in different paths. Herein, we propose a prediction-based mobile robot task assignment method for a dynamic job shop manufacturing environment. In this method, a mobile robot is advanced to a position such that its traversal distance to a transport request facility is minimized via transport request prediction. Experiment results show that the results yielded by the proposed method are similar or superior to existing results.


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References


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