A bi-level optimisation approach for assembly line design using a nested genetic algorithm

Abstract

This article presents a novel approach for the automated design of assembly lines that combines the assembly line balancing problem with resource selection and the positioning of the chosen resources into one single optimisation problem. Existing approaches for the automated planning of assembly plants either focus on one planning step or work through different planning steps sequentially. So far, no method exists that sufficiently takes into account the interdependency between the selection and positioning of resources. This article addresses this problem by presenting a bi-level optimisation approach for the automated design of assembly lines. A nested genetic algorithm is used to solve an assembly line balancing problem that includes the selection of production resources while simultaneously considering the layouting options for the chosen resources. Three examples for the evaluation and validation of the algorithm are presented. The presented approach is economically promising as the design of assembly lines requires a lot of expert knowledge and is still mostly done manually.

KEYWORDS:

• assembly line balancing
• assembly line design
• assembly line planning
• layout planning
• nested genetic algorithm
• bi-level optimisation

Acknowledgement

The authors of this article would like to thank the German Federal Ministry of Education and Research (BMBF) and the Project Management Agency Research Centre Karlsruhe (PTKA) for funding this work, which is part of the research and development project ProMoA (funding number 02P15A100).

Disclosure statement

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

Data availability statement

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

Additional information

Funding

This work was supported by the German Federal Ministry of Education and Research [Bundesministerium für Bildung und Forschung] (BMBF) and the Project Management Agency Research Centre Karlsruhe (PTKA) under Grant 02P15A100.

Notes on contributors

Daria Leiber

Daria Leiber is a research associate at the Institute for Machine Tools and Industrial Management at the Technical University Munich. She is head of the department Assembly Technology and Robotics and holds a M. Sc. in mechanical engineering. Her research focuses on algorithms for the design and optimisation of assembly lines.

Gunther Reinhart

Gunther Reinhart Professor Gunther Reinhart is holder of the Chair for Industrial Management and Assembly Technology of the iwb (Institute for Machine Tools and Industrial Management) at the Technical University Munich. He is also the chairman of the Bavarian Cluster for Mechatronics and Automation e.V. Since 1 July 2016 he is executive director of Fraunhofer Research Institution for Casting, Composite and Processing Technology (IGCV). Furthermore, Professor Gunther Reinhart is board member and council member as well as consultant for several companies.