Multi-objective parallel adjacent U-shaped assembly line balancing collaborated by robots and normal and disabled workers

Abstract

This paper emphasises the parallel adjacent U-shaped assembly line balancing problem in which the collaborative work forces in the system comprise human workers and robots. Apart from normal workers, disabled workers are also hired to enable the company to enjoy corporate tax benefits from the government. Five objectives related to the system as a whole and another three interacting entities are optimised simultaneously. A mathematical model is formulated for the first time for this type of layout. Due to the NP-hard nature of the problem, a reference point based evolutionary algorithm, namely non-dominated sorting teaching-learning-based optimisation (NSTLBO III), is proposed to solve medium- and large-sized problems. The performances of NSTLBO III are benchmarked in the sense of Pareto efficiency with two well-known multi-objective evolutionary algorithms, i.e. non-dominated sorting genetic algorithm (NSGA III) and multi-objective evolutionary algorithm based on decomposition (MOEA/D). The experimental results show that NSTLBO III surpasses its contestants significantly concerning the convergence to the true Pareto front.