Co-evolutionary particle swarm optimization algorithm for two-sided robotic assembly line balancing problem

Zixiang Li,Peter Nielsen,Mukund Nilakantan Janardhanan,Qiuhua Tang

doi:10.1177/1687814016667907

Abstract

Industries utilize two-sided assembly lines for producing large-sized volume products such as cars and trucks. By employing robots, industries achieve a high level of automation in the assembly process. Robots help to replace human labor and execute tasks efficiently at each workstation in the assembly line. From the literature, it is concluded that not much work has been conducted on two two-sided robotic assembly line balancing problems. This article addresses the two-sided robotic assembly line balancing problem with the objective of minimizing the cycle time. A mixed-integer programming model of the proposed problem is developed which is solved by the CPLEX solver for small-sized problems. Due to the problems in non-polynomial--hard nature, a co-evolutionary particle swarm optimization algorithm is developed to solve it. The co-evolutionary particle swarm optimization utilizes local search on the global best individual to enhance intensification, modification of global best to emphasize exploration, and restart mechanism to escape from local optima. The performances of the proposed co-evolutionary particle swarm optimization are evaluated on the modified seven well-known two-sided assembly line balancing problems available in the literature. The proposed algorithm is compared with five other well-known metaheuristics, and computational and statistical results demonstrate that the proposed co-evolutionary particle swarm optimization outperforms most of the other metaheuristics for majority of the problems considered in the study.

Highlights

Manufacturing companies extensively use assembly lines and the assembly process is considered to be one of the critical processes in manufacturing systems.1,2 Different layout types have been widely utilized in industries based on the size and type of products.3 For assembly of large-sized volume products, for example, cars, trucks, and buses utilize twosided assembly lines
The C-genetic algorithm (GA) can find better best solution, whereas the GA and artificial bee colony (ABC) can improve a little or not improve after reaching 600 s. These results prove that the C-particle swarm optimization (PSO) has stronger capacity of finding a new best solution
Optimizing cycle time is an important task in assembly lines and to the author’s knowledge, there has been no work reported on optimizing cycle time for two-sided robotic assembly lines

Summary

Introduction

Manufacturing companies extensively use assembly lines and the assembly process is considered to be one of the critical processes in manufacturing systems.1,2 Different layout types (traditional straight line, Ushaped, two-sided, and parallel) have been widely utilized in industries based on the size and type of products.3 For assembly of large-sized volume products, for example, cars, trucks, and buses utilize twosided assembly lines. In this article, a new way of representing the solution is presented and it is described as follows: taking the first mated-station as example, 2nm 3 (2nm 2 1) combinations should be checked before selecting the best combination with smallest operation time. //Station selection mechanism Step 3: Obtain assignable task set for both sides and execute Step 4.

Results

Conclusion