Cloud Material Handling Systems: Conceptual Model and Cloud-Based Scheduling of Handling Activities

Fabio Sgarbossa (), Mirco Peron () and Giuseppe Fragapane ()
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Fabio Sgarbossa: NTNU—Norwegian University of Science and Technology
Mirco Peron: NTNU—Norwegian University of Science and Technology
Giuseppe Fragapane: NTNU—Norwegian University of Science and Technology

Chapter Chapter 5 in Scheduling in Industry 4.0 and Cloud Manufacturing, 2020, pp 87-101 from Springer

Abstract: Abstract Nowadays, the implementation of cloud manufacturing technologies epitomizes the avant-garde in production systems. This affects several aspects of the management of these production systems, in particular scheduling activities, due to the possibility provided by cloud manufacturing of having real-time information about the stages of a product life cycle and about the status of all services. However, so far, cloud manufacturing has mainly focused on machines, with limited interest in material handling systems. This shortfall has been addressed in this study, where a new material-handling paradigm, called Cloud Material Handling System (CMHS) and developed in the Logistics 4.0 Lab at NTNU (Norway), has been introduced. With CMHS, the scheduling of the Material Handling Modules (MHMs) can be optimized, increasing the flexibility and productivity of the overall manufacturing system. To achieve this, the integration of advanced industry 4.0 technologies such as Internet of Things (IoT), and in particular Indoor Positioning Technologies (IPT), Cloud Computing, Machine Learning (ML), and Artificial Intelligence (AI), is required. In fact, based on the relevant information provided on the cloud platform by IPT and IoT for each product, called Smart Object (SO) (position, physical characteristics and so on), an Intelligent Cognitive Engine (ICE) can use ML and AI to decide, in real time, which MHM is most suitable for carrying out the tasks required by these products based on a compatibility matrix, on their attributes, and on the defined scheduling procedure.

Date: 2020
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DOI: 10.1007/978-3-030-43177-8_5

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