In this paper, we consider the incorporation of user preferences based on Nissan automotive company’s domain knowledge into a multi-objective search process for assembly line balancing. We focus on the Time and Space Assembly Line Balancing problem, a more realistic variant of this family of problems considering the joint minimisation of the number of stations and their area in the assembly line configuration. The multi-objective optimisation algorithm considered is based on Ant Colony Optimisation, a research area where the consideration of multi-criteria decision making issues is still not extended. The proposed approach borrows a successful preference scheme from the evolutionary multi-objective optimisation community, which provides experts with solutions of their contextual interest in the objective space. The expressions of the considered preferences are based on the Nissan plant designer’s expert knowledge and on real-world economical variables. Using the real data of the Nissan Pathfinder engine, an experimental study is carried out to obtain the most preferred solutions for the decision makers in six different Nissan scenarios.

Peer Reviewed