End-of-Life product quality management for efficient design of disassembly lines under uncertainty

Abstract

The design of a disassembly line is a long-term investment. Therefore, it is important to optimise its performance and demonstrate its financial viability. The design process must consider the unique End-of-Life product characteristics and disassembly operation particularities. These peculiarities are mainly expressed by uncertainty in post-consumer products quality, variability of task processing times and presence of hazardous material. To achieve efficient disassembly line design, efficient decision-making tools are needed. The purpose of this work is to propose such a tool. The objective is to efficiently design a disassembly line, as a disassembly system, that provides optimal income and takes into account Enf-of-Life (EoL) product quality uncertainty and task processing times variability. The proposed tool allows the decision-maker to select the optimal disassembly process and depth while assigning retained tasks to the line workstations. Hazardous material of EoL products is handled and the designed line guarantees a certain level of service set by the decision-maker. The product part revenue depends formally on its EoL state or quality. Several product examples from remanufacturing sectors in industry are utilised to demonstrate interest and relevance of the proposed decision-aiding tool in an industrial context.

Keywords:

• Disassembly
• sustainable manufacturing
• line design
• EoL product quality
• uncertainty
• decision aiding

Data availability statement

The data that support the findings of this study are available from the corresponding author, M.-Lounes BENTAHA, upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Mohand-Lounes Bentaha

Mohand-Lounes Bentaha is currently Associate Professor at Université Lumière Lyon 2, DISP laboratory, France. He received the Engineering degree in Industrial Engineering from École Nationale Polytechnique, Alger, 2010; the M.S. degree in Operational Research-Decision Aiding from Université Paris Dauphine, LAMSADE, UMR 7243, 2011; the Ph.D. degree in Industrial Engineering from École des Mines de Saint-Étienne, LIMOS, UMR 6158, 2014. He obtained, in 2013, an International Cooperation and Mobility fellowship and led research on disassembly line design at the University of Michigan, Department of Mechanical Engineering, under supervision of Pr. S. Jack Hu. After his Ph.D., he spent 2 years, 2014–2016, as Research Fellow at the Université de Lorraine, CRAN, UMR 7039. His research activities focus on manufacturing/remanufacturing systems modelling and optimisation, assembly and disassembly line balancing under uncertainty. His actual research interests include real-time scheduling, lifetime assessment, predictive maintenance, zero default manufacturing and decision support systems.

Pascale Marangé

Dr. Pascale Marangé obtained her Ph.D. in Automation, Signal Processing and Computer Engineering at the University of Reims Champagne Ardennes in 2008. Since 2010, she is an associate professor at the Centre de Recherche en Automatique de Nancy (CRAN-UMR 7039) of the University of Lorraine (France) where she participates in several industrial projects and is developing since 2012 works around the circular economy. Her current research interests focus on the definition and evaluation of the implementation of industrial regeneration (recovery of products at the end of their use) according to an ecosystem.

Alexandre Voisin

Dr. Alexandre Voisin was graduated from EFREI, a French public graduate school of engineering in computer science and electronics, in 1992 and received his Ph. D. degree in Electrical Engineering from the Université de Lorraine in 1999. In 2016, he was certified ‘Lean 6 sigma’ Black Belt with project leaded in Baccarat crystal factory. He is currently Associate Professor at the Research Center for Automatic Control of Nancy (CRAN-UMR 7039) in the Université de Lorraine (France) where he leads the dependability and Prognostics, Health Management and Maintenance research project (around 15 researchers). His current research interests deal with Predictive Maintenance & PHM including health monitoring of hierarchical systems, system prognostic and decision making in a predictive maintenance framework.

Néjib Moalla

Néjib Moalla Full Professor at the University Lumiere Lyon 2 and a member of the DISP laboratory in France. He finished his Ph.D. thesis in 2007 and his habilitation in 2015. He held the responsibility of the project management department between 2008 and 2012. His research activities deal with software and data engineering with finalised innovative applications in industry. He develops research concepts in software quality, service-oriented architectures, performance-based service reuse, microservice architecture, ontology engineering, knowledge Management, etc. and proposes interoperable ICT architectures and solutions for manufacturing systems in industry. As a project manager, he continues to be involved in several international projects: E+ ENHANCE (2021–2024, Coor), H2020 DIH4CPS (2020–2022), H2020 vf-OS (2017–2019), etc. He is a member of several international program committees (IPC) of international journals and conferences.