Robust design and reconfiguration planning of mixed-model assembly lines under uncertain evolutions of product family

Abstract

Assembly lines commonly run for dozens of years before being decommissioned. As product families may evolve several times per year by following the needs of sales and marketing, process engineers reconfigure the lines several dozens of times throughout their life cycle. If the line is not flexible enough, these reconfigurations may be costly, and they can lead to poor efficiency. The present work investigates the possibility of designing a line while accounting for product evolution throughout the life cycle of the line. The evolution of the product family is unknown and we consider a robust optimisation approach. We study a mixed-model assembly line, where each station contains a worker/robot and its equipment. The line produces different product models from the same family, and a reconfiguration occurs when a new product model replaces one of the current variants in the product family. Reconfiguration re-arranges resources and equipment pieces, and it can re-assign some tasks. In this study, we formulate a novel Mixed-Integer Linear Programming (MILP) that minimizes the total cost of the initial design and future reconfigurations of the line over some future product family evolution for the worst case. We consider the worst-case among different scenarios that represent possible production requirements of the new product model. An adversarial approach is also developed to solve large-size instances. We perform computational experiments on the benchmark data from the literature. The results show the proposed adversarial approach performs well, and the proposed robust model significantly reduces the design and reconfiguration costs when compared to the classical approach that designs and reconfigures by accounting only for the current product family.

Keywords:

• Robust optimisation
• Mixed model assembly line
• Reconfigurability
• Product family evolution
• Adversarial approach

Acknowledgements

The authors also would like to thank the CCIPL computing centre for the generous computing resource allocation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

Data supporting the findings of this study are available at a reasonable request from the authors.

Additional information

Funding

The authors would like to thank the project ASSISTANT (https://assistant-project.eu/) that is funded by the European Commission, under grant agreement number 101000165, H2020–ICT-38-2020, artificial intelligence for manufacturing.

Notes on contributors

Yosra Mezghani

Yosra Mezghani, an industrial engineer, graduated from the National Engineering School of Tunis (ENIT) in 2022 with both an engineering diploma in Industrial Engineering and a master's diploma in Next Production Revolution. She did her master's internship at IMT Atlantique, Nantes, France, under supervision of Prof. Alexandre Dolgui, Dr. Simon Thevenin, and Dr. S. Ehsan Hashemi-Petroodi. During the internship she worked on Robust Design and Reconfiguration Planning of Mixed-Model Assembly Lines under Uncertain Product Family Evolutions. Her research interests focus on Manufacturing Line Design, Robust Optimization, Mathematical Programming, Exact Optimization Methods, (Meta-)Heuristics, and currently she is passionate about finding solutions to preserve the planet and reduce the ecological impact through innovative approaches based on her field.

S. Ehsan Hashemi-Petroodi

S. Ehsan Hashemi-Petroodi is Assistant Professor of production systems and Industry 4.0 in the Department of Operations Management and Information Systems. He received a Ph.D. in Industrial Engineering and Operations Research from the IMT Atlantique (Institute Mines Telecom), campus in Nantes, France in 2021. During his Ph.D., he worked on combinatorial optimisation for the configuration of workforce and equipment in reconfigurable assembly lines. From 2021 to 2023 he was a post-doctoral researcher and the leader of process planning optimisation task in the European project ASSISTANT – LeArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments – consisting of 12 industrial and academic partners. He has been also involving in some other national and European projects on reconfigurable manufacturing systems and logistics. His research focuses on combinatorial optimisation, robust optimisation, assembly line design and balancing, workforce and process planning, and decision aid systems. His main results are based on the exact mathematical programming methods and their intelligent connection with heuristic algorithms. His contributions have been published in leading peer-reviewed scientific journals as International Journal of Production Economics (IJPE), International Journal of Production Research (IJPR), Omega-The International Journal of Management Science, etc. and presented at major international conferences.

Simon Thevenin

Simon Thevenin is an assistant professor in the Automation, Production, and Computer Sciences Department at the IMT Atlantique, France. He received a Ph.D. from the University of Geneva in 2015 for his work on metaheuristics to solve scheduling problems in production systems. His current research interests focus on optimisation methods for production management, including production scheduling, production planning, and manufacturing line design.

Alexandre Dolgui

Alexandre Dolgui is a Fellow of IISE, a Distinguished Professor of Industrial Engineering, and the Head of the Automation, Production, and Computer Sciences Department at the IMT Atlantique, campus in Nantes, France. His research focuses on manufacturing line design, production planning and scheduling, and supply chain engineering. His main results are based on exact mathematical programming methods and their intelligent coupling with heuristics and metaheuristics algorithms. He has contributed to the theory of assembly line balancing, combinatorial design of machining lines, process planning, supply chain scheduling, lot sizing and replenishment under uncertainties. He is the author of over 700 publications and communications, numerous books and articles, the editor-in-chief of the International Journal of Production Research, an area editor of Computers & Industrial Engineering, a member of the editorial boards for several other journals, including the International Journal of Production Economics, Fellow of the European Academy for Industrial Management, Member of the Board of the International Foundation for Production Research, former Chair of IFAC TC 5.2 Manufacturing Modelling for Management and Control, and Member of IFIP WG 5.7 Advances in Production Management Systems.