Robotic U-shaped assembly line balancing using particle swarm optimization

Abstract

Automation in an assembly line can be achieved using robots. In robotic U-shaped assembly line balancing (RUALB), robots are assigned to workstations to perform the assembly tasks on a U-shaped assembly line. The robots are expected to perform multiple tasks, because of their capabilities. U-shaped assembly line problems are derived from traditional assembly line problems and are relatively new. Tasks are assigned to the workstations when either all of their predecessors or all of their successors have already been assigned to workstations. The objective function considered in this article is to maximize the cycle time of the assembly line, which in turn helps to maximize the production rate of the assembly line. RUALB aims at the optimal assignment of tasks to the workstations and selection of the best fit robot to the workstations in a manner such that the cycle time is minimized. To solve this problem, a particle swarm optimization algorithm embedded with a heuristic allocation (consecutive) procedure is proposed. The consecutive heuristic is used to allocate the tasks to the workstation and to assign a best fit robot to that workstation. The proposed algorithm is evaluated using a wide variety of data sets. The results indicate that robotic U-shaped assembly lines perform better than robotic straight assembly lines in terms of cycle time.

Mathematical models and migrating birds optimization for robotic U-shaped assembly line balancing problem

“…Hence, this work presents two major contributions to the literature as follows: 1) Two new mixedinteger linear programming models are developed to minimize the cycle time in a U-shaped robotic assembly line. These two new linear models outperform the non-linear model proposed in Nilakantan and Ponnambalam (2016) in the comparative study conducted. 2) A newly developed metaheuristic algorithm, migrating birds optimization (MBO) algorithm is employed and improved to tackle the considered RUALBP in an acceptable computational time.…”

“…However, none of the models report the considered RUALBP or is able to tackle robot assignment. The only available model on RUALBP is a nonlinear model proposed by Nilakantan and Ponnambalam (2016) and this model might not be able to achieve the optimal solution even for small-sized instances. Hence, this section develops two new mixed-integer linear programming models to solve the considered problem.…”

“…Robotic U-shaped assembly line inherits the main features of U-shaped assembly line and robotic assembly line. On the basis of assumptions considered in Levitin et al (2006) and Nilakantan and Ponnambalam (2016), the main assumptions in this paper are presented as follows:…”

“…A task in U-shaped assembly line is assignable when all its predecessors or successors have been allocated to the same or earlier workstation (Miltenburg and Wijngaard, 1994;Scholl and Klein, 1999). When compared with the straight assembly line, U-shaped line has higher flexibility to meet the changes in demand with reduced cycle time and reduced cost (Nilakantan and Ponnambalam, 2016).…”

“…From the aforementioned literature, it is established that there is limited research on RUALBP. The only available nonlinear model proposed by Nilakantan and Ponnambalam (2016) is hard to be solved optimally using exact techniques for large-sized or even small-sized problems and it is also observed that only few evolutionary algorithms have been utilized in solving RUALBP, whereas literature reports extensive usage of evolutionary algorithms for solving RALBP problems.…”