Balancing of assembly lines with collaborative robots: comparing approaches of the Benders’ decomposition algorithm

AbstractIn recent years, human workers in manual assembly lines are increasingly being supported by the deployment of complementary technology. Collaborative robots (or cobots) represent a low-threshold opportunity for partial automation and are increasingly being utilised by manufacturing corporations. As collaborative robots can be used to either conduct tasks in parallel to the human worker or collaborate with the worker on an identic task, industrial planners experience an increasingly complex environment of assembly line balancing. This contribution proposes three different decomposition approaches for Benders’ decomposition algorithms exploring the multiple possible partitions of the formulation variables. We evaluate the performance of the algorithms by conducting extensive computational experiments using test instances from literature and compare the findings with results generated by a commercial solver and a metaheuristic solution procedure. The results demonstrate the Benders’ decomposition algorithms’ efficiency of finding exact solutions even for large instances, outperforming the benchmark procedures in computational effort and solution quality.KEYWORDS: Assembly line balancingcollaborative robotscobotsBenders’ decompositioncollaboration Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.Additional informationNotes on contributorsCelso Gustavo Stall SikoraCelso Gustavo Stall Sikora is a Post-Doc at the Institute for Operations Research at the Faculty of Business Administration – University of Hamburg. He holds a degree in Industrial Mechanical Engineering and a master’s degree from the Graduate Program in Electrical and Computer Engineering (CPGEI) from Federal University of Technology – Paraná (UTFPR). His PhD studies were developed at University of Hamburg and focus on assembly line balancing under uncertain demand. For his work during the master and PhD studies, he was awarded the 7th Prize for application-oriented scientific achievements in the subject area ‘Planning and optimization in the automotive and related innovative industries’ from the Initiative Wissenschaft und Automobilindustrie e.V. in Jena, Germany. His research interests involve modelling and decomposition algorithms in production and logistic.Christian WeckenborgChristian Weckenborg is an assistant professor (Akademischer Rat) at the Chair of Production and Logistics at the Institute of Automotive Management and Industrial Production at Technische Universität Braunschweig, Germany. He holds a MSc degree in industrial engineering and a PhD in business administration. His research is mainly centred on the conceptual development and implementation of techno-economic models for decision support in production, logistics, and sustainability management.