Next generation integrated smart manufacturing based on big data analytics, reinforced learning, and optimal routes planning methods

Abstract

In this study, Big Data Analytics has been applied to implement smart manufacturing services performed by local commercial laundry Small and Medium Sized Enterprises (SMEs), which, to be specific, is called Smart Laundry Services (SLSs). This laundry service first uses traffic big data inputs from the City Traffic monitoring Server and abstract patterns to develop dynamic optimal scheduling routes for logistic terminals after taking pick-up orders from hotel supply managers. On the other hand, the laundry floor, which isfor fully automatic operation through integrated smart manufacturing with reinforced learning algorithms, will automatically classify the arriving orders according to the Radio Frequency Identification (RFID) tags on the orders, which contain data for each order such as material type, forbidden policies, detergent suggestion, and other relevant specifications. The orders are immediately processed and assigned to a laundry terminal with least waiting time and with optimal laundry parameters learned by the algorithm. The processed orders are then shipped back to the hotels by static scheduling routes with the optimal patterns estimated from real-time traffic big data. The entire operation, from initial orders to dropping back orders, are monitored by hotel supply managers and laundry SMEs’ personnel on their computers or mobile devices such as cell phones via an APP. As a result, this proposed integrated Smart Manufacturing architecture characterized with cutting-edge technologies such as big data analytics and intelligent production will make the commercial laundry SMEs more competitive in local markets.