Integrated Prescriptive Maintenance and Production Planning: a Machine Learning Approach for the Development of an Autonomous Decision Support Agent. Issue 10 (2022)

• Record Type: Journal Article
• Authors: Elbasheer, Mohaiad; Longo, Francesco; Mirabelli, Giovanni; Padovano, Antonio; Solina, Vittorio; Talarico, Simone
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 10(2022)
• Page Start: 2605
• Page End: 2610
• Publication Date: 2022
• Subjects: Prescriptive Maintenance -- Production Planning & Control -- Intelligent Agents -- Machine Learning -- Reinforcement Learning -- Conceptual Framework; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.10.102
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Machine Learning (ML) practice represents a vital construct for developing intelligent Cyber-Physical Production Systems (CPPS) capable of making timely optimization for Maintenance and Planning actions. Integrating Adaptive Production Planning and Prescriptive Maintenance (PsM) in future factories provides a novel perspective for flexibility, customization, and resilience of production plans. To this end, we propose a framework for developing an intelligent Decision Support Agent (DSA) for integrated PsM and production planning and control (PPC) based on Reinforcement Learning. The paper highlights the practical implications of developing an autonomous DSA from an ML perspective using a demonstrative use-case of integrated Maintenance and PPC.