Investigating two variants of the sequence-dependent robotic assembly line balancing problem by means of a split-based approach

Abstract

The Robotic Assembly Line Balancing Problem (RALBP) is a joint optimisation problem that is concerned with assigning both assembly operations and robots to workstations that are placed within a straight line. RALBP-2 is the particular problem where the cycle time, which is the maximum time spent on a workstation by the product being assembled, is minimised while the number of workstations is fixed. Sequence-dependent setup times are considered which raises the problem of sequencing the operations assigned to each workstation. Both the durations of the operations and the setup times depend on the robot. Two different variants are identified from literature. The first variant assumes that, given a set of types of robots, each type of robot can be assigned to multiple workstations without any limitation. Given a set of robots, the second variant forces each robot to be assigned to at most one workstation. Both assumptions are studied in this paper. The particular case of a given giant sequence of operations is solved thanks to a polynomial optimal algorithm. The latter algorithm, called split, is then embedded in a metaheuristic framework that explores the space of giant sequences. Benchmark data sets from literature are considered in the experimental section. A comparative study with other methods from literature shows the competitiveness of the suggested approach.

Keywords:

• Assembly line balancing
• robotic assembly line
• sequence-dependent setup times
• polynomial case
• metaheuristic

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author, Y.L., upon reasonable request.

Notes

1 In a multi-set, an element can be present more than once, the multiplicity defines the number of copies of an element in the set

Additional information

Funding

The authors acknowledge the support received from the Agence Nationale de la Recherche of the French government through the program “Investissements d'Avenir” [grant number 16-IDEX-0001 CAP 20-25].

Notes on contributors

Youssef Lahrichi

Youssef Lahrichi is currently post-doctoral researcher at EM Strasbourg Buisness School in Strasbourg (France). He holds a PhD degree from Université Clermont Auvergne (France). His research focuses on applications of operational research models for problems of logistics, transport or production.

David Damand

David Damand is currently associate professor at EM Strasbourg Buisness School in Strasbourg (France). He is member of Humanis laboratory. David Damand heads the supply chain management master's degree at EM Strasbourg. His research focuses on inventory management and facility layout.

Laurent Deroussi

Laurent Deroussi is currently associate professor at Université Clermont Auvergne (France). He is member of UMR CNRS 6158 LIMOS laboratory. Laurent Deroussi heads the logistics and information systems bachelor's degree. His research interests include metaheuristics, assembly line balancing and vehicle routeing problems.

Nathalie Grangeon

Nathalie Grangeon is currently associate professor at Université Clermont Auvergne (France). She is member of UMR CNRS 6158 LIMOS laboratory. Her research interests include metaheuristics, scheduling, vehicle routeing and assembly line balancing problems.

Sylvie Norre

Sylvie Norre holds a full professor position at Université Clermont Auvergne (France). She is member of UMR CNRS 6158 LIMOS laboratory. Sylvie Norre is co-founder of the logistics and transport management department at IUT Clermont Auvergne (Montluçon) and co-head of the industrial engineering master's degree. Her research focuses on optimisation of goods and services production systems (exact and approximate methods).