Modelling and condition-based control of a flexible and hybrid disassembly system with manual and autonomous workstations using reinforcement learning

Remanufacturing includes disassembly and reassembly of used products to save natural resources and reduce emissions. While assembly is widely understood in the field of operations management, disassembly is a rather new problem in production planning and control. The latter faces the challenge of high uncertainty of type, quantity and quality conditions of returned products, leading to high volatility in remanufacturing production systems. Traditionally, disassembly is a manual labor-intensive production step that, thanks to advances in robotics and artificial intelligence, starts to be automated with autonomous workstations. Due to the diverging material flow, the application of production systems with loosely linked stations is particularly suitable and, owing to the risk of condition induced operational failures, the rise of hybrid disassembly systems that combine manual and autonomous workstations can be expected. In contrast to traditional workstations, autonomous workstations can expand their capabilities but suffer from unknown failure rates. For such adverse conditions a condition-based control for hybrid disassembly systems, based on reinforcement learning, alongside a comprehensive modeling approach is presented in this work. The method is applied to a real-world production system. By comparison with a heuristic control approach, the potential of the RL approach can be proven simulatively using two different test cases

Computation of production control policies by a dynamic programming technique

"September, 1982."Bibliography: leaves 16-17.National Science Foundation Grant DAR78-17826 National Science Foundation Grant ECS 79-20834by Joseph Kimemia, Stanley B. Gershwin, Dimitri Bertsekas

A methodology to assess and manage material and machine tool risks for a manufacturer

Globalization and competition have forced manufacturers to analyze their processes to minute levels in order to improve the quality and on-time delivery of the product. Due to the increased complexity of manufacturing and the associated supply chain, a wide range of additional risk factors have been introduced that impact the manufacturing processes. A process that is constantly exposed to such risks may not be able to meet customer expectations such as the on-time delivery of products. Extensive research has been done on enhancing the capabilities of the manufacturing processes. However the focus of this effort is to develop a methodology to manage risks that have a high impact on the process lead time and will enhance the ability to sustain process performance. The purpose of this study is to identify key risks associated with manufacturing and develop a framework to assist manufacturers mitigate the risks resulting in increasing the manufacturing lead time. The framework takes on the format of an assessment that investigates the multiple risk dimensions associated with material and tooling. Inputs to the assessments are confidence interval of 95%. Finally a mathematical analysis using AHP is done for prioritization of risk mitigation activities. A case study is presented to the methodology