Multi-objective metaheuristics for solving a type II robotic mixed-model assembly line balancing problem

Abstract

Nowadays, robots are used extensively in robotic assembly line balancing system because of the capabilities of the robots. Robotic assembly lines are used to manufacture high volume product in customization and specialization production. In this paper, type II robotic mixed-model assembly line balancing is considered. The goals are to minimize robot purchasing costs, robot setup costs, sequence dependent setup costs, and cycle time. The proposed model tries to determine an optimal or near-optimal configuration of tasks, workstations in U-shaped assembly line balancing. In this model, two types of tasks including the special task for one product model and the common task for several products models exist. The problem with the aforementioned conditions is NP-hard problem. So, we used two different multi- objective evolutionary algorithms (MOEAs) to solve the problem. First algorithm is non-dominated sorting genetic algorithm (NSGA-II) and the second one is multi-objective particle swarm optimization (MOPSO). Also, we used GAMS software to solve the problem in small size problem to validate our proposed model. Then, some numerical examples are presented and the experimental results and the performance of the algorithms are compared with each other.