Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
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Session Overview
Session
Intelligent Systems for Manufacturing Planning and Control in the Industry 4.0.
Time:
Tuesday, 07/Sept/2021:
3:00pm - 5:00pm

Session Chair: Silvestro Vespoli
Session Chair: Julia Christine Arlinghaus
Virtual location: Tuesday Room 1

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Presentations
ID: 176 / ISM-02: 1
Special session: Industry 4.0 technologies for manufacturing sustainability
Keywords: Industry 4.0; fourth industrial revolution; India; policies; challenges; value drivers.

Industry 4.0: An Indian perspective

Anbesh Jamwal1, Rajeev Agrawal1, Monica Sharma1,2, Saurabh Pratap3

1Department of Mechanical Engineering, Malaviya National Institute of Technology Jaipur (Rajasthan) INDIA - 302017, India; 2Department of Management Studies, Malaviya National Institute of Technology Jaipur (Rajasthan) INDIA - 302017, India; 3Department of Mechanical Engineering, Indian Institute of Technology (IIT-BHU) Varanasi, India



ID: 442 / ISM-02: 2
Special track: Intelligent Systems for Manufacturing Planning and Control in the Industry 4.0.
Keywords: Reinforcement Learning, DQN, Scheduling, Flow Shop.

Dynamic scheduling in a flow shop using Deep Reinforcement Learning

Maria Grazia Marchesano, Guido Guizzi, Liberatina Carmela Santillo, Silvestro Vespoli

Università degli Studi di Napoli Federico II, Italy



ID: 555 / ISM-02: 3
Special track: Intelligent Systems for Manufacturing Planning and Control in the Industry 4.0.
Keywords: Digital manufacturing, risk management, cybersecurity, digitalization

Risk Assessment and Mitigation for Industry 4.0: Implementation of a Digital Risk Quick Check

Julia C. Arlinghaus, Falko Bendik

Otto-von-Guericke-Universität Magdeburg, Germany



ID: 381 / ISM-02: 4
Special track: Intelligent Systems for Manufacturing Planning and Control in the Industry 4.0.
Keywords: Industry 4.0, Industrial Production System, CONWIP Flow-Shop, Throughput Estimation, Deep Learning

A deep learning algorithm for the throughput estimation of a CONWIP line

Silvestro Vespoli, Andrea Grassi, Guido Guizzi, Valentina Popolo

Università degli Studi di Napoli Federico II, Italy



ID: 509 / ISM-02: 5
Special track: Intelligent Systems for Manufacturing Planning and Control in the Industry 4.0.
Keywords: Interdependency effects, prediction, logistics performance

Exploring Interdependency Effects of Production Orders as Central Impact Factors of Logistics Performance in Manufacturing Systems

Victor Vican1, Julia C. Arlinghaus2

1Jacobs University gGmbH; 2Fraunhofer-Institut für Fabrikbetrieb und -automatisierung IFF



 
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